Use of Satellite and In-Situ Data to Improve Sustainability
Use of Satellite and In-Situ Data to Improve Sustainability
Edited by
Felix Kogan National Oceanic & Atmospheric Administration (NOAA/NESDIS) Center for Satellite Application and Research (STAR) Washington DC, USA
Alfred M. Powell, Jr. National Oceanic & Atmospheric Administration (NOAA/NESDIS) Center for Satellite Application and Research (STAR) Washington DC, USA
Oleg Fedorov Space Research Institute of the National National Space Agency of Ukraine Kiev, Ukraine
Published in Cooperation with NATO Public Diplomacy Division
Proceedings of the NATO Advanced Research Workshop on Using Satellite Data and In-Situ Data to Improve Sustainability Kiev, Ukraine, 9-12 June 2009
ISBN 978-98-481-9620-3 (PB) ISBN 978-90-481-9617-3 (HB) ISBN 978-90-481-9618-0 (e-book)
Published by Springer, P.O. Box 17, 3300 AA Dordrecht, The Netherlands. www.springer.com
Printed on acid-free paper
All Rights Reserved © 2011 Springer Science+Business Media B.V. No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form-or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.
PREFACE
The Advanced Research Workshop (ARW) “Using Satellite and In situ Data to Improve Sustainability” was held in Kyiv, UKRAINE during June 9–12, 2009. The ARW was sponsored by the North Atlantic Treaty Organization (NATO) and organized by the National Space Agency of Ukraine (NSAU) in cooperation with the Center for Satellite Application and Research (STAR) of the National Environmental Satellite Data and Information Services (NESDIS), National Oceanic and Atmospheric Administration (NOAA) http://www.star.nesdis.noaa.gov/smcd/emb/ vci/VH. Drs. Powell (NOAA) and Fedorov (NSAU) served as ARW Directors. The ARW was focused on the current issues of changing climate and providing services for sustainable economy, healthy environment and better human life and had the following sessions • • • • • •
Early warning of natural disasters Weather and food security Climate services to enhance national security Land cover/land change and anthropogenic activities Human health and the environment Satellite and in situ data records for trend analysis
Eighty five scientists from North America, Europe and Asia, attended the Workshop, participated in the discussions and gave 53 presentations. The brain-storming discussions at the end of each day session resulted in the ARW Summary. The following important issues were emphasized at the Workshop 1. More than 30-year satellite data time series have already provided sufficient information to be used for monitoring land, ocean and atmosphere, improving sustainable economy, environment and human life; these activities must be expanded, advertised and widely distributed. 2. The gap between research and applications for improving sustainability should be overcome by combining satellite and in situ data for enhancing spatial and temporal coverage of the Earth and expanding the products and services quantity and quality. 3. In order to expand the applications, satellite data sets and products must be presented in a ready-to-use form, easily available and be user friendly. 4. Existing and new satellite data and products must be validated and calibrated to enhance their credibility. 5. Following the ARW goals (climate services and sustainability), the current NOAA satellite data and products can be scaled into three categories: (a) mature science/ready to use (flash-flood, drought, snow, vegetation health); (b) intermedium
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maturity science (agriculture, land-water interactions, sensitivity of world ecosystem to ENSO) and (c) prospective science/need development (climate forcing, land cover/land change, anthropogenic activities, loss of bio-productivity, bio-productivity potential, wave analysis and external climate forcing). In situ data must be actively collected for validation and calibration of satellite datasets and products. Create regional polygon (Kherson administrative region, which combines Black Sea open water and costal ecosystems) and collect comprehensive data sub-sets (satellite, in situ, economic) with easy access for scientists in order to develop and test new methods, data, models and products. Create working groups covering (a) climate science (change impacts, prediction from forcing); (b) economic effectiveness of climate services; (c) satellite/in situ data assimilation; (d) bio-productivity potential. Implement NOAA satellite-based technique and products for monitoring droughts, flash-flood rainfall, vegetative health, and snow cover. Approve the first year of Cooperation (MOU) between the National Oceanic and Atmospheric Administration and the National Space Agency of Ukraine. Considering the Cooperation success continue the MOA activities for the next 4 years.
Cooperation between National Oceanic and Atmospheric Administration (NOAA) and the National Space Agency of Ukraine (NSAU) started in June 2008 when NOAA Administrator and Director General of NSAU signed the Memorandum of Agreement (MOA) to promote the application of NOAA operational environmental satellites for climate services in order to achieve sustainability. The MOA was under the umbrella of the Global Earth Observing System of Systems (GEOSS) and Group on Earth Observations (GEO). The most advanced thirty two papers discussing the basic science, new methods, datasets, products and applications were selected to be including in this book. Scientific and application results presented in these papers can be used today for an early detection of large-scale natural disasters, assessments of agricultural production losses, monitoring fires, climate and land surface trend analysis, application of climate forcing for lead-time predictions and others. This book consists of the following five parts and appendix Part I: Large-Scale Weather Disasters: Early Detection and Monitoring from Space and In Situ Data Part II: Environment and Food Security: Diagnosis and Prediction Part III: Climate Change, Environment and Socioeconomics Part IV: Marine Ecosystem, Land Ccover, Atmosphere and Anthropogenic Activities Part V: Satellite and In Situ Long Records for Trend Analysis, Modeling and Monitoring Part I consists of six chapters discussing such large-scale disasters as drought, flood, severe weather and fires. Several methods are presented showing both satellite and in situ data used for monitoring and assessments. Part II consissts of six chapters discussing mostly food security issues in terms of monitoring large-scale
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agricultural production from satellite and in situ data. The authors share their experience in Ukraine, Russia and USA. Part III consists of seven chapters discussing climate change issues and implications for a reduction of glassiers, changes in bio-climatic potential, crop yield variations under different IPCC scenarios etc. In addition, some aspects of climate change origination and climate forcing impact on land surface are also analysed and presented. Part IV consists of seven chapters discussing marine ecosystem issues such as interaction between land and coastal water. Also, some papers are devoted to desertification issue, geomagnetic activities and others. Part V consists of six chapters discussing long-term satellite-based time series records for monitoring changes in land surface, comparing the records produced from the same source but with different processing algorithms. The appendix contains color images for several papers. Felix Kogan Alfred Powell Oleg Fedorov
CONTENTS
PART I
LARGE-SCALE WEATHER DISASTERS: EARLY DETECTION & MONITORING FROM SPACE & IN SITU DATA
Monitoring Droughts and Impacts on Crop Yield in Ukraine from Weather and Satellite Data .................................................................. Tatyana Adamenko and Anatoly Prokopenko
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Early Detection and Monitoring Droughts From NOAA Environmental Satellites .................................................................... Felix Kogan and Wei Guo
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Flood Monitoring from SAR Data................................................................. Nataliia Kussul, Andrii Shelestov, and Sergii Skakun Satellite Rainfall Information for Flood Preparedness and Response ................................................................................................... Robert J. Kuligowski
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Monitoring Severe Weather in UKRAINE with Satellite Data .................. Oleksiy Kryvobok, Mykola Kulbida, and Ludmila Savchenko
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Daily Fire Occurrence in Ukraine from 2002 to 2008 ................................. Wei Min Hao, Shawn P. Urbanski, Bryce Nordgren, and Alex Petkov
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Satellite-Based Systems for Agro-meteorological Monitoring .................... Alexander Kleschenko, Oleg Virchenko, and Olga Martinenko
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PART II
ENVIRONMENT AND FOOD SECURITY: DIAGNOSIS AND PREDICTION
Monitoring Droughts and Pastures Productivity in Mongolia Using NOAA-AVHRR Data ...................................................... Leah Orlovsky, Felix Kogan, Eldad Eshed, and Chultem Dugarjav
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Satellite-Derived Information on Snow Cover for Agriculture Applications in Ukraine ....................................................... Peter Romanov
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Grain Yield Prediction in the Russian Federation ....................................... Anna Strashnaya, Tamara Maksimenkova, and Olga Chub Satellite-Based Crop Production Monitoring in Ukraine and Regional Food Security ........................................................................... Felix Kogan, Tatiana Adamenko, and Mikola Kulbida
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New Regression Models for Prediction of Grain Yield Anomalies from Satellite-Based Vegetation Health Indices ....................... 105 Gennady Menzhulin, Natalya Shamshurina, Artyom Pavlovsky, and Felix Kogan Phytosanitary Situation of Agrocenosis in Ukraine and New Technologies for Monitoring Harmful Organisms .............................. 113 Vladimir Chayka, Tatiana Neverovska, Nelia Prokopiuk, and Olga Baklanova PART III
CLIMATE CHANGE, ENVIRONMENT AND SOCIOECONOMICS
30-Year Land Surface Trend from AVHRR-Based Global Vegetation Health Data ................................................................................... 119 Felix Kogan Global Warming, Atlantic Multi-decadal Oscillation, Thermohaline Catastrophe and Their Impact on Climate of the North Atlantic Region .......................................................................... 125 Alexander Polonsky Global Warming and Possible Changes in the Recurrences of Grain Crops Anomalies.............................................................................. 145 Gennady Menzhulin and Artyom Pavlovsky Regime Shifts in the Atmosphere and Their Relationship to Abrupt Ocean Changes .............................................................................. 151 Alfred M. Powell, Jr, Jianjun Xu, and Ming Chen Glacier Degradation from GIS and Remote Sensing Data ......................... 159 Azamat Tynybekov ENSO Impact on Vegetation .......................................................................... 165 Felix Kogan Bio-climatic Potential of Russia and Climate Change ................................. 175 Alexander Kleschenko
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PART IV
MARINE ECOSYSTEM, LAND COVER, ATMOSPHERE & ANTHROPOGENIC ACTIVITIES
Consequences of Land and Marine Ecosystems Interaction for the Black Sea Coastal Zone ...................................................................... 181 Vladimir Kushnir, Gennady Korotaev, Felix Kogan, and Alfred M. Powel, Jr Utilizing Satellite Data to Highlight High Ozone Concentration Events During Fire Episodes.......................................................................... 191 Rasa Girgždienė and Steigvilė Byčenkienė Geomagnetic Disturbances and Seismic Events in the Vrancea Zone from in Situ Data.................................................................... 199 Frina Sedova, Vladimir Bakhmutov, and Tamara Mozgovaya First Step Towards Monitoring Surface Ozone Dynamics at Ukrainian Stations ...................................................................................... 209 Oleg Blum, Vira Godunova, Volodymyr Lapchenko, Oleksiy Perekhod, Yaroslav Romanyuk, and Mikhail Sosonkin Satellite Monitoring of Nitrogen Oxide Emissions....................................... 219 Igor Konovalov, Matthias Beekmann, Andreas Richter, and John Burrows Detection of Desertification Zones Using Multi-year Remote Sensing Data ...................................................................................... 235 Lev Spivak, Irina Vitkovskaya, Madina Batyrbayeva, and Alex Terekhov Satellite Desertification Monitoring in Sahara ............................................. 241 Mikhail A. Popov, Sergey A. Stankevich, Alexei I. Sakhatsky, Menny O. El Bah, Daoud Mezzane, and Igor A. Luk’yanchuk PART V SATELLITE & IN SITU LONG RECORDS FOR TREND ANALYSIS, MODELING & MONITORING Global Vegetation Health: Long-Term Data Records ................................. 247 Felix Kogan, Wei Guo, and Aleksandar Jelenak Aero-Space Radar Online Monitoring of Disasters in Ukraine.................. 257 Mariya Belobrova, Dmitry Bychkov, Anatoly Boev, Alexandre Gavrilenko, Valentin Efimov, Alexandre Kabanov, Ivan Kalmykov, Alexandre Matveev, and Valery Tsymbal Comparison of AVHRR-Based Global Data Records .................................. 267 Felix Kogan, Marco Vargas, and Wei Guo
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Merging Remote Sensing and In Situ Data for Estimation of Energy Balance Components Under Climate Change Conditions: Ukrainian Steppe Zone .............................................................. 273 Tatiana Ilienko and Elena Vlasova Atmosphere Aerosol Properties Measured with AERONET/PHOTONS Sun-Photometer over Kyiv During 2008–2009 .......................................................................................... 285 Vassyl Danylevsky, Vassyl Ivchenko, Gennadi Milinevsky, Michail Sosonkin, Philippe Goloub, Zhengqiang Li, and Oleg Dubovik Global Distribution of Magnetic Storm Fields and Relativistic Particles Fluxes ................................................................... 295 Olga Maksimenko and Galyna Melnyk Appendix 1 ....................................................................................................... 305 Appendix 2 ....................................................................................................... 306 Appendix 3 ....................................................................................................... 307 Appendix 4 ....................................................................................................... 308 Appendix 5 ....................................................................................................... 309
Part I
Large-Scale Weather Disasters: Early Detection & Monitoring From Space & In Situ Data
Monitoring Droughts and Impacts on Crop Yield in Ukraine from Weather and Satellite Data Tatyana Adamenko and Anatoly Prokopenko
Abstract The agrarian sector is an important component of the Ukrainian economy. Within this sector about 20% of able-bodied citizens of the country are employed and 12–15% of the gross domestic product is formed. During previous years simultaneous with the tendency of improved agricultural technology, the significant fluctuations of crops productivity owing to weather conditions, first of all owing to droughts, increase. Their frequency and intensity rise. Every 2–3 years the drought covers up to 20–40% of the country territory. Crop losses owing to droughts can run up to 50% and more. There is a dangerous tendency of drought distribution over territory which earlier belonged to a zone of sufficient moisture. In the Ukraine, climate fluctuations affecting crop production account for a 20–50% loss of winter crops and 35–75% loss of summer crops. Keywords Droughts • Agrometeorological monitoring • Yield • Crop forecasting • Modeling • Observing system
Introduction The southern areas of the Ukraine suffer from permanent moisture deficiency under sufficient heat provision. More often, annual precipitation in these areas is limited for sustained agricultural production. The situation becomes very acute in drought years. Optimum soil moisture deficit during the growing season for receiving top yield in the central and southern regions is 50–100 and 150–200 mm, respectively. As the adverse agroclimatic phenomena, droughts are inherent to the climate of the Ukraine. During past 30–50 years they became more frequent and intense, covering up to half of its territory every 10–12 years, and up to 20% every 2– 3 years. Drought-related crop losses in such years can reach 50%. In combination T. Adamenko (*) and A. Prokopenko Ukrainian Hydrometeorological Centre, Kyiv, Ukraine e-mail:
[email protected] F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_1, © Springer Science+Business Media B.V. 2011
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yield t / ha
with anthropogenic factors, increases in drought occurrence and intensity leads to land degradation and desertification. The following are the economic reasons the irrigated areas in Ukraine is reducing. In 2008, droughts started to appear even in the zone of Polesye, an obvious manifestation of climatic change and economic activities. Droughts even occurred in years with near normal precipitation leading to crop shortages. Figure 1 shows 0.5–1.5 t/ha crop reduction in the drought of 2003 compared to the non-drought year of 2008. Figure 2 shows that in the drought years of 1990–2008, winter wheat yield was reduced two to three times compared to non drought years.
5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Winter wheat
Barley 2008
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Fig. 1 Crop yields in drought (2003) and non drought (2008) years in Ukraine
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The Ukrainian Hydrometcenter monitors agrometeorological conditions in Ukraine, regularly observing the state of microclimate, soil moisture, crops, and pastures in a timely and objective manor. The information includes weather observations received from meteorological stations and the satellite data. The data are processed automatically with the aim of simulation of the production process and assessment of crop growth, development, and yield formation.
Methods Ukraine has sufficient experience in the development of various components of drought monitoring, and in carrying out field (ground point and en route) surveys of agricultural crops. The systematic study of droughts and hot winds in Ukraine has a long history. During this time many different criteria of drought, with definition of its types, including atmospheric, soil, and mixed ones, were suggested. The automated program of “drought” was developed on the basis of analysis on the current state of assessment of drought and hot winds, their influence on grain yield by the UkrHMC, in cooperation with the Odessa State Environmental University (OSENU) (A. Polevoy). This program is compatible with automated working place of an agrometeorologist (ARM-Agro), i.e. with the operative agrometeorological information and allows each decade to assess the impact of drought on crop cereals (winter wheat, spring barley) as shown in Fig. 3.
Fig. 3 Estimated reduction in yield of spring barley (%) due to drought in regions of Ukraine. Calculation June 30, 2009
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Study Area and Data Accounting of dry conditions is realized by decades of vegetation by means of analysing of several known methods of evaluation of dry events, which are adapted for different soil-climatic zones of Ukraine. The climatic peculiarities of the territory – moistening of arable (0–20 cm) and 1 m soil layers, the amount of precipitation and of air moisture deficit were assessed. In addition, the UkrHMC uses a well-known complex index of moistening and water-supply of plants – the hydrothermal coefficient – HTC (the ratio of total precipitation to the sum of temperatures above 10°C), which is also calculated in the operational mode. Taking into account the climate change, in particular, the air temperature increase with still persistent amount of precipitation, it is likely that droughts will grow stronger in Ukraine. Ukraine signed the UN Convention “On Combating Desertification”, but today very little attention to assessing and taking measures for mitigation of droughts is paid on the state level. Incorporation of all available know-how in a single monitoring system using the regular data of satellite observations will significantly increase the effectiveness of drought management, primarily by improving the opportunities of providing services and communicating agrometeorological information to end-users online. On ground agrometeorological monitoring is regularly carried out by 188 meteorological stations in Ukraine. In parallel with the meteorological observations, about 145 stations simultaneously monitor growth, development, condition, humidity, and security of crops according to a single method. Observations are carried out for the most common crops in the area of a meteorological station observation. They are winter wheat and spring barley, legumes, oilseeds, and industrial crops. Standard observation of weather elements is carried out every 3 h. The regularity of crop observations is every 2 days, the actual water-supply is defined once in 10 days. Meteorological and agrometeorological information is collected on an operational basis by the regional hydrometeorological centers and the Ukrainian Hydrometeorological Center. Data on the phenological development of crops, height, density, weed infestation, actual water-supply and condition of crops (a points-system) allows to create the general picture on the country. This information is the basis for crop estimating and forecasting. The information on the actual soil water-supply is especially important. Ukraine has accumulated an extensive and reliable data on agrometeorological observations. Its adequacy is proved by estimates obtained in different in degrees of favourability years.
Crop Yield Forecasting The harvest assessment is carried out with a temporal resolution of a decade. Issue (calculation, analysis, adoption of a final figure) of yield prediction and croppage takes into account critical periods of development of a specific culture as for its
Monitoring Droughts and Impacts on Crop Yield in Ukraine from Weather and Satellite Data
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future harvest. Forecasting is carried out on several levels (weather station, district, region, country) using all the methods developed, tested, and adapted to the territory of Ukraine. A basic software package based on a dynamic model of crops productivity using the actual operational information was created at the UkrHMC. Standard indicators of decadal agrometeorological information – temperature and air humidity deficit, amount of precipitation, sun-shine duration; reserves of productive water-supply in the soil are used. Agro-physical and agrochemical characteristics of soil are taken into account. This information is transformed into a system of input parameters of the model. Calculations of primary characteristics of vegetation cover are realized (the size of the biomass of individual organs of plants – leaves, stems, roots, seeds), initial values of estimates of extreme conditions are given. The model estimates the influence of weather conditions on yield for any period of vegetation (a decade, an interphase period, an entire growing season), a possible reduction of yield due to unfavorable conditions for specific territory (frost, drought, dry winds). as shown in Fig. 4. The level and volume of harvest are calculated directly. Results of surface monitoring and forecasting are used for: • • • • • • •
Operational reporting to leaders of the country, ministries Decision-making on export, import Farmers Insurance companies Recommendations for optimizing of the cultivation of crops Determining of the need for fertilizers and chemicals Justifying the specialization of farms and agricultural cultures zoning
Fig. 4 Assessment of the impact of weather on winter wheat yield (in % of optimum conditions) in administrative regions on May 31, 2009
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In years with particularly unfavourable weather conditions, the meteorological stations carry out ground-route survey of agricultural crops. All the agrometeorological information comes to the regional centres and from there it goes to the UkrHMT.
Receipt and Processing of Data Processing of the agrometeorological observations is performed on an agrometeorologist AWP. The AWP was developed and implemented in the Ukrainian Hydrometeorological Center and regional centers for hydrometeorology over 9 years ago. An agrometeorologist automated working place (an agrometeorologist AWP) is a specialized program for an operative display of operational meteoro logical and agrometeorological information, processing, and systematization it for making the various operational and long term materials by agrometeorologists. An AWP ensures receiving of primary daily and decadal data from weather stations in the special code from the server connection. Then the processing of this information, checking, and editing it on the AWP screen, is carried out. AWP performs the following tasks: • Forming long-term observations databases on all types of information for all types of crops and research plots which were observed • Drawing any agrometeorological information for any date or period on a cartographic basis • Forming new types of maps for selected crops, areas, dates and other information by an agrometeorologist • Getting maps with drawing information from each meteorological station, averaging over areas and soil-climatic zones, etc. • Comparing the factual information with the normals • Generating and printing of various tables, the type and composition of which is formed by an expert agrometeorologist • Automated logging of observational data into introduction tables with the possi bility of sorting and filtering • Comparing of data with the standards and conducting any calculations and averaging • Carrying out the calculations of agrometeorological forecasts of: productivity, the timing of various phenological phases of agricultural cultures onset and drought manifestations or “molting”of grain and others AWP-Agro enables us to assess online the drought phenomena using various methods and evaluate possible losses of grain crops. Evaluation of a drought is done both using the actual data on air temperature, relative humidity and, more importantly, of water-supply. Current agrometeorological information enables us to assess online the impact of drought phenomena on the development and yield of field crops.
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Today the UkrHMC, having an adequate technical support and staffing, is actively pursuing work on adaptation of satellite data for assessing the status and productivity of crops in Ukraine. Development of monitoring system with regular data from satellite observations significantly will increase the effectiveness of drought management in Ukraine. First of all, it is the use of vegetation index in conjunction with other indicators, adaptation of the European system “Mars” together with the UHMI (Ukrainian Hydrometeorological Institute), as well as the developments of ARRIAM (All-Russia Research Institute of Agricultural Meteorology) (Russia).
Early Detection and Monitoring Droughts From NOAA Environmental Satellites Felix Kogan and Wei Guo
Abstract With nearly 30 years of the accumulated AVHRR data which were collected from NOAA operational polar-orbiting environmental satellites, the area of their applications expanded in the direction of monitoring vegetation condition, modeling agricultural production, analysis of climate and global change, resource management, and early and more efficient monitoring of droughts and their impacts on economy and society. This becomes possible due to the development of Vegetation Health (VH) indices. This paper discusses utility of the AVHRR-based VH focusing on monitoring vegetation with the emphasis on early drought warning and drought features. Keywords Droughts • Environmental satellites • Vegetation Health indices
Introduction Drought is a typical phenomenon of the earth’s climate. The losses from droughts are normally staggering. The average annual cost of drought in the United States of America, a country of the advanced technology, is around $6 billion. However, in extreme drought years such as 1988, the cost of the drought is five to eight times larger. In the recent years, large-scale intensive droughts were reported all over the globe. Developing countries of Africa and Asia were the most affected. For example, the Horn of Africa experienced droughts 6 years in a row which led to serious food shortages. Rangeland in Mongolia have also suffered from a very intensive droughts resulting in a lack of feed for the livestock. Unusual summer
F. Kogan (*) NOAA/NESDIS Center for Satellite Application and Research (STAR), Washington DC, USA e-mail:
[email protected] W. Guo IMSG Inc., Washington D.C., USA F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_2, © Springer Science+Business Media B.V. 2011
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dryness also affected the new countries (from the former USSR) in the Black Sea and Caspian Sea regions. Western USA experienced drought-induced forest fires for several years. In 2003 and 2007, nearly 20% of the global lands were drought-stricken. Weather data are traditionally used for drought monitoring. However, weatherderived drought-watch system has shortcomings. Weather data represent point locations rather than an area and meteorological stations are not uniformly distributed. Even in the United States, with a well developed weather network, the density of stations is not sufficient to characterize regional drought. For example, in South Dakota one weather station is normally used for monitoring drought on 1–2 million acres of crop land. In Ukraine, each of the 180 weather stations covers 0.8 million acres of land. The problem of insufficient density of weather stations becomes especially acute in the areas with marginal climatic resources such as Africa, Asia, Central and South America. This paper discusses a new satellite-based land-surface observing system used for drought monitoring. Principally, satellite data in the form of vegetation indices have been applied for land monitoring since the mid 1980s (Tarpley et al. 1984). In the last 20 years, NOAA has designed a new AVHRR-based Vegetation Health (VH) theory, system and data set (Global Vegetation Index, GVI-x) that showed success when applied to drought detection and tracking (Kogan 1990, 1997, 2001). Unlike other remote sensing techniques, the new method and system uses multi-spectral radiances and the main ecosystem laws for analysis of vegetation health in response to weather changes. During the last 8 years, this method was tested and validated thoroughly against ground data in all major agricultural countries of the world and proved to be of excellent utility for early drought detection, accurate monitoring of its development, affected area and impacts on agriculture, rangeland and forestry (Salazar et al. 2007). This paper presents the results of using the VH for monitoring droughts.
Satellite Data The GVI-x system was developed based on the NOAA AVHRR Global Area Coverage (GAC) data set. The GAC is produced by sampling and mapping the AVHRR 1-km daily reflectance in the visible (VIS, 0.58–0.68 mm, near infrared (NIR, 0.72–1.1 (mm), and two infrared bands (IR4, 10.3–11.3 and IR5, 11.5– 12.5 mm) to a 4-km map. The VIS and NIR reflectance were pre- and post-launch calibrated and the normalized difference vegetation index (NDVI) was calculated as (NIR-VIS)/(NIR+VIS). The IR4 emission was converted to brightness temperature (BT), which was corrected for non-linear behavior of the AVHRR sensor. Daily NDVI and BT were composited over a 7-day period by saving those values that have the largest NDVI for each map cell. The 1981–2010 NDVI and BT weekly time series were processed to remove high frequency noise, identify seasonal cycle and to calculate climatology.
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Vegetation Health Method A new method is based on the estimation of green canopy stress/no stress from AVHRR-derived indices, characterizing moisture, thermal conditions and total vegetation health (Kogan 1990, 1997, 2001). Unlike the two spectral channels approach (NDVI-based) routinely applied for vegetation monitoring, the new numerical method in addition to NDVI, also uses BT from 10.3–11.3 mm IR4 channel, which estimates the hotness of the vegetation canopy. In dry years, high temperatures, coupled with an insufficient water supply, lead to overheating of the canopy, which intensifies negative effects of moisture deficit impact on vegetation. The three-channel algorithm consists of comprehensive processing of NDVI and BT annual time series, which includes complete removal of high-frequency noise, enhancing seasonal cycle, calculation of climatology and single out medium-to-low frequency fluctuations associated with weather impacts on vegetation (Kogan 1995, 1997,). This procedure was formalized by Eqs. (1)–(3), where climatology was represented by the difference between 22-year absolute maximum and minimum both NDVI and BT values for each pixel and week.
VCI = 100 ∗ (NDVI − NDVI min ) (NDVI max − NDVI min )
(1)
VCI = 100 ∗ (BTmax − BT ) (BTmax − BTmin )
(2)
VHI = a ∗ VCI + b ∗ TCI
(3)
where NDVI, NDVImax, and NDVImin (BT, BTmax, and BTmin ) are the smoothed weekly NDVI (BT), their multi-year absolute maximum and minimum, respectively; a and b = 1 − a are coefficients quantifying a share of VCI and TCI contribution in the total vegetation health. The VCI (Vegetation Condition Index), TCI (Temperature Condition Index) and VHI (Vegetation Health Index) are indices estimating cumulative moisture, temperature and total vegetation health conditions, respectively on a scale from zero (extreme stress) to 100 (favorable condition) with 50 corresponding to the average condition.
Global Droughts The VH system has been used successfully for monitoring vegetation health, including drought-related vegetation stress around the world since the 1990s. These data were presented at http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_currentImage.php. Examples of vegetation health (condition) in mid 2007 and 2008 are shown in Fig. 1. As seen, intensive summer drought-related vegetation stress were observed in the western USA, southern Ukraine and Russia, Mongolia (Northern Hemisphere, summer) and in Argentina (2008), western Australia, Brazil and Southern Africa (Southern Hemisphere, winter). These estimates are supported by in situ observations (Le Comte 2008, 2009).
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Vegetation Health in mid-July Fig. 1 Vegetation health index in mid July 2007 and 2008 (Color image is provided in Appendix 1)
It should be emphasized that satellite data in addition to drought start/end, d uration and area, also estimate drought intensity. In the USA, four categories characterize drought intensity (severity): moderate (D1), severe (D2), extreme (D3) and exceptional (D4). In terms of precipitation deficit they are characterized by 120
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Fig. 3 Total number of events in 2007 and 2008 when hourly ground-level ozone concentrations exceeded the critical levels of 60–180 mg/m3, NBG station
Fig. 4 Daily variability of ground-level ozone concentration in Kiev (NBG)
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Fig. 5 Comparison of ozone concentrations measured at two sites in Kiev: at NBG (half-tone line) and at Golosiiv (black line)
Comparison of July–August 2006 ozone measurements at two sites in Kiev indicates that: (1) the two curves have similar trend, and (2) ozone concentrations at Golosiiv are significantly higher (due to its location downwind from industrialized region) than those at NBG, which is located in the urban core (see Fig. 5). Continuous high-frequency automatic measurements of surface ozone at the high-altitude station at Terskol in Caucasus indicate that there was no increase in surface ozone since the summer of 2003. Ozone concentrations show a clear seasonal cycle with maximum values from May to August (up to 85 ppbv) and minimal values in November–December (down to 20 PPbv) (Godunova et al. 2006). Daily variability of ozone concentrations is mainly related to the following factors (in addition to usual photochemical processes): variations in meteorological conditions, horizontal advection and vertical exchange processes over mountain terrain. However, other natural events, such as intrusion of stratospheric ozone, could induce somewhat larger fluctuations of the ozone mixing ratio and cause the average daily ratio to vary by about 10 ppbv around its monthly average. Figure 6 shows a daily ozone concentration profile, which is typical during a sunny windless day for the period from late spring to mid-autumn. There is a slight increase during the first half of the day and a decrease in the afternoon. Mean daily values typically do not exceed 50 ppbv between September and February and are, on average, 40 ppbv in this period and 60 ppbv and higher between April and August.
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Figure 7 illustrates little difference in daily averages of ozone concentrations in summer at Terskol.
Conclusions Ground-level ozone variability at the four sites in southeastern Europe has been studied based on ozone records covering the period from 2004 to 2008. Three of these sites are situated at different locations in Ukraine; the fourth station is set up at an altitude of 3125 m in the Northern Caucasus, Russia. Since air quality in this part of Europe still needs more investigation, it is imperative to develop here a monitoring network, which would include these stations, as well as additional sites to be set up in Ukraine and in Russia. Data sets from this region could significantly contribute to an effective control of atmospheric composition over continent. Other tasks to be performed in the near future might include: the development of technical
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cooperation in air quality monitoring; distribution of ozone information to the public; and enhancement of educational attainments in atmospheric sciences.
References Air quality in Europe: state and trends 1990–1999 (2003) (Topics report No.4/2002). EEA, Copenhagen, 2003 Blum O, Butnerowicz A, Maning W, Popovicheva L (1998) Ambient tropospheric ozone in the Ukrainian Carpathian mountains and Kiev Region: detection with passive samplers and bioindicator plants. Environ Pollut 98(3):299–304 Directive 2002/3/EC of the European Parliament and of the Council of 12 February 2002 relating to ozone in ambient air. OJ L 67/14-30 (2002) Godunova V, Sosonkin M (2006) Terskol observatory as a regional station for monitoring air quality in eastern europe. In: Granier C et al (ed) Air quality in eastern europe. A review of measurement and modelling practices and needs. Report 8.2006 of the ACCENT/JRC Expert Workshop Gregg J, Jones C, Dawson TE (2003) Urbanization effects on tree growth in the vicinity of New York City. Nature 424:183–187
Satellite Monitoring of Nitrogen Oxide Emissions Igor Konovalov, Matthias Beekmann, Andreas Richter, and John Burrows
Abstract The efficient control of air pollutant emissions into the atmosphere is important for sustainable development. Remarkable recent progress in satellite measurements of the composition of the troposphere has opened new prospects for monitoring of air pollution and emissions of pollutants. This paper presents examples of using satellite measurements of atmospheric composition for estimation of long-term changes of emissions in nitrogen oxides which are important air pollutants playing a major role in photochemical smog formation. The estimations are based on the use of inverse modeling methods enabling combination of the data for tropospheric NO2 column amounts derived from the long-term (1996–2008) GOME and SCIAMACHY satellite measurements with simulations performed by the CHIMERE chemistry transport model. Keywords Air quality • Pollutants • Nitrogen oxides • Chemistry transport model • Satellite measurements
Introduction The emissions of pollutants into the atmosphere have strong impact on both air quality and climate. Accordingly, the efficient control of these emissions into the atmosphere is one of the important conditions for a sustainable development. Usually, emissions are estimated by compiling the available information about emission I. Konovalov () Institute of Applied Physics, Russian Academy of Sciences, Nizhniy Novgorod, Russia e-mail:
[email protected] M. Beekmann Université Paris-Est and Université Paris 7, Créteil, France A. Richter and J. Burrows University of Bremen, Bremen, Germany
F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_25, © Springer Science+Business Media B.V. 2011
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sources (e.g., industry, power plants, transportation etc.) and by applying pre- estimated emission factors to individual sources. However, this procedure frequently involves incomplete or uncertain information, as well as many assumptions that are difficult to verify. An alternative way for emission estimation is provided by inverse modeling approach (see, e.g., Enting 2002). The idea of this approach is to derive the information about emissions directly from atmospheric measurements by using a chemistry transport model. Remarkable recent progress in satellite observations of the troposphere composition has opened new prospects for monitoring air pollution and emissions of pollutants. In particular, it has been demonstrated that satellite measurements of nitrogen dioxide (NO2) allow estimating anthropogenic emissions of nitrogen oxides and their multi-annual changes (e.g., Martin et al. 2003; Richter et al. 2005; Konovalov et al. 2006, 2008; Konovalov 2007). Oxides of nitrogen (NOx) are important air pollutants affecting the oxidising properties of the atmosphere and playing the major role in the photochemical smog formation. In this paper, we provide an overview of our recent inverse studies aimed at estimation of multi-annual changes of NOx emissions from satellite measurements of tropospheric column amounts of nitrogen dioxide. Accurate independent estimates of past changes in NOx emissions provide new ways for the evaluation of the efficiency of air pollution control measures, for the assessment of uncertainties in emission cadastres, and for testing the ability of chemistry transport models to reproduce past and predict future changes in atmospheric composition.
Measurement and Model Data Satellite Data Tropospheric NO2 columns derived from satellite measurements by IUP, University of Bremen, have been used. Seven years (1996–2002) of GOME measurements (Burrows et al. 1999) were complemented with 6 years (2003–2008) of SCIAMACHY measurements (Bovensmann et al. 1999). The GOME and SCIAMACHY instruments provide measurements of NO2 columns at the horizontal resolution of 320 × 40 km2 and 60 × 30 km2, of global coverage at the equator achieved in 3 and 6 days, respectively. We use the same data-products for tropospheric NO2 columns derived from satellite measurements and analysed earlier in Richter et al. (2005), where a general description of the retrieval method can be found. We considered only summertime measurements because of prevailing cloudy conditions in eastern Europe during the cold season. A pre-processing stage (specific for this study) includes, the projection of daily data for tropospheric NO2 columns onto a 1° × 1° grid and averaging of all the data for 3 summer months (June–August) of each year. In order to avoid systematic “jumps” in the time series of the gridded NO2 columns between 2002 and 2003 when linking the lower resolution GOME data and the higher spatial resolution SCIAMACHY data, an additional transformation of data was performed using two
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different methods. The first method involves deconvolution of the GOME NO2 data. This method is very similar to the one suggested in our earlier study (Konovalov et al. 2006). The basis for this approach is to superimpose the spatial structure of the NO2 columns derived from the SCIAMACHY measurements over the spatial structure of the NO2 columns derived from the GOME measurements. The second method is used to obtain consistent time series for NO2 columns over megacities and is used only in the framework of the nonlinear version of our inversion algorithm (see Section 3.2). The idea is to simulate the smoothing of the spatial structure of NO2 columns, introduced by the GOME measurements. The method involves the convolution of the NO2 columns from SCIAMACHY:
2m [ j − m]2 cos(f )2 cs(conv c exp ≈ ∑ s (i − m + j ) i) − 2 wc 2 j=0
(1)
where cs(i) are the original NO2 column amounts in the grid cell i, m is the number of grid cells on the longitudinal plane within 320 km (the typical resolution of the GOME measurements), f is the latitude, and wc is a scaling factor. Here, we approximate the smoothing introduced by the GOME window on the longitudinal plane into the seasonally averaged NO2 columns by the Gauss function with an efficient width (wc/cos(f))2. The parameter wc was estimated to be equal 0.88 by minimizing the mean squared differences between the convoluted NO2 columns for 2003 and the original NO2 columns (from GOME) for 2002 over 14 megacities considered in Section 4.2. These convoluted NO2 columns were used in our analysis instead of the original NO2 columns from SCIAMACHY.
Simulated Data To generate the simulated NO2 columns, the CHIMERE CTM was used (http://www. lmd.polytechnique.fr/chimere/). The model takes into account all important processes that determine the evolution of nitrogen oxides released into the atmosphere, such as gas-phase reactions which define the chemical balance between NOx species and their transformation to nitric acid, dinitrogen pentoxide and organic nitrogen compounds. Dry deposition and wet scavenging which are responsible for the removal of the reactive nitrogen compounds from the atmosphere; advective transport, eddy diffusion and deep convection. The simulated NO2 columns were sampled consistently in space and time with the measurement-based daily NO2 columns. In this study we use a spatial domain that covers all of Europe, the Mediterranean area and the Middle East with a horizontal resolution of 1° × 1°. The model runs were performed with 12 layers defined as hybrid coordinates. The top of the upper level was fixed at 200 hPa pressure level. Meteorological input data were calculated off-line with horizontal resolution of 100 × 100 km2 using the MM5 non-hydrostatic meso-scale model (http://www.mmm.ucar.edu/mm5/). MM5 was initialized with NCEP Reanalysis-2 data (http://www.cpc.ncep.noaa.gov/products/wesley/ncep_data/).
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The anthropogenic emission data used in this study are based on the so-called “expert” annual data of the EMEP emission inventory (Vestreng et al. 2005) for the years 1996–2004. We have used the two versions of the expert data that were available at the EMEP web site (http://webdab.emep.int/) on a 0.5° × 0.5° grid in autumn 2006 (before 30 November) and in spring 2007. The comparison of the older and the newer versions of the emission data gives some idea about uncertainties in the bottom-up inventories which is helpful for validation of emission estimates obtained in this study, particularly because more recent emission data are expected to be more accurate than the older ones. As it is shown below, the differences in trends between the two versions of the EMEP data are, in some cases, rather large. CHIMERE was run independently for each summer season starting on 24 May with the same initial and boundary conditions. A “base” decadal run of CHIMERE was performed with the EMEP emission data for the year 2001. Only the results of the base run are used in the inversion algorithm. Additional control runs were performed with emission estimates obtained in this study.
Method Linear Approach Following the standard Bayesian approach (see e.g., Enting 2002), assuming that uncertainties in inter-annual variations of NO2 columns satisfy the normal probability distribution and linearizing the modeled relationship between perturbations of the NO2 columns and NOx emissions, we get the following probability distribution for the interannual changes of NOx emissions constrained by observations:
p( ∆E | ∆Co ) ∝ 2 n +1 n ∂(Cmi + Cmi ) 1 N N exp − ∑ ∑ ∆ E j |E = E0 − ∆ (Cio − Cim |E = E 0 ) s ci−2 pa ( ∆E) (2) 2 ∂E j 2 i =1 j =1
where E is a vector of emission estimates (a subscript “0” denotes a base case defined above), Co and Cm are the observed and modeled NO2 columns, sc is the standard deviation for the uncertainties in the NO2 columns, i and j are the indexes of a grid cell, n is the number of a year, D is an operator of an interannual variation (e.g., DE(n) = En+1 − En) and pa is the a priori probability distribution (specified below) for interannual changes in emissions. The idea behind the distribution (2) is very simple: if we have some differences between inter-annual changes in NO2 columns from observations and the model with constant emissions, we assume that this difference is probably due to corresponding changes in NOx emissions. As common in inversion methods, we are looking for the maximum likelihood a posteriori estimates of DE that yield a maximum of p. Note that the vector E here represents
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only anthropogenic NOx emissions, because available data on the consumption of fertilizers suggest that changes in biogenic emissions in the concerned regions were rather small during the considered decade. In order to ensure that the a posteriori estimates of emissions are always positive, we seek the solution in terms of natural logarithms of emissions, denoted below as e (that is, we replace E with exp(e)). As it is common in geophysical studies, we characterize the long-term changes in the considered atmospheric characteristics by the linear trend and the year-to-year variability superimposed over the trend:
e n = e + (n − n )∆ t e + ∆ d e n
(3)
where the subscripts “t” and “d” denote the trend (independent on the year) with the deviation from the trend, respectively. Accordingly, our inversion procedure consists of two major steps. First, we estimate Dte by finding values of Den which provide a maximum of the distribution (2) in which D(C0 − Cm) is replaced by the linear trend in the difference (C0 − Cm), that is, Dt(C0 − Cm); the estimated Den for each pair of neighboring years are averaged. At this step we have also to define the probability distribution function for a priori emission estimates, pa. As we do not dispose of any specific information on uncertainty in the EMEP emission data which could provide a priori estimates for emission trends, we put pa as a constant inside realistic limits (lmin < Dt e < lmax) and zero outside. Limits are chosen as the minimum and maximum values of linear trends in the EMEP NOx emissions data considered on a 1° ´ 1° grid of our model. This gives values of lmin = −0.07 and lmax = 0.1. This procedure gives an estimate of the spatial distribution of the NOx emission trends which is practically independent of the corresponding distribution based on the “bottom-up” inventory, while avoiding unrealistically high magnitudes of emission trends. Second, we estimate deviations from the trend, Dde, that provide the maximum of the distribution (2) where D(C0 − Cm) is replaced by Dd(C0 − Cm) and pa is a Gaussian distribution for (De)d with the standard deviation equal 0.14. This value is chosen to adjust the variance of the a posteriori deviations from the trends in Great Britain to the variance calculated for the deviation from the trends in the “new” EMEP data for the same country (about 4.2%). Note that typical magnitudes of interannual variations in our a posteriori emission estimates are determined not only by parameters pa, but also by the values of sc. Although the choice of Great Britain is rather arbitrary, it seems reasonable taking into account that we consider ground based measurement of NOx in this country for validation of our results. Values of the standard deviation for uncertainties in the NO2 columns, sc, are estimated from above as a function of magnitude of the measured NO2 columns by calculating the mean squared differences between the inter-annual changes of the measured and modeled NO2 columns within a “moving window” (note that only the variation of sc from one grid cell to another is of interest in Eq. (2)). This approach is described in Konovalov et al. (2005). The such defined sc is found to increase monotonically from about 0.25 to 2.5 as Co increases from about zero to 15 (always in molecules · 1015 cm−2).
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Finding an accurate mathematically solution of our inversion problem would be computationally too expensive. Instead, we use approximate methods that were developed in our earlier studies. The main idea is to substitute the original model for a set of linear statistical models describing the relationships between perturbations of NOx emissions and NO2 columns approximately (Konovalov et al. 2006). Such an approximation is possible because the lifetime of freshly emitted NOx is rather limited in summer. Our method allows us to take into account the transport of NOx on the scale of three neighboring grid cells (that is, 200–300 km). The statistical models were created by performing 100 model runs with randomly perturbed NOx emissions for each year independently. The optimal estimates of Dte and Dde for each pair of years are obtained by means of the iterative steepest descent method using zero and the obtained estimates of Dte as initial guesses, respectively. Although a sufficiently accurate estimation of the uncertainties in our results is hardly possible because of the lack of knowledge about numerous factors that can contribute to these uncertainties, we tried to get rough estimates of uncertainties as follows. First, we considered how deviations from the trend in the columns, Dd(C0 − Cm), can influence the estimates of Dte. Although such deviations are not necessarily due to uncertainties, this approach allows us to estimate, at least, the upper limit of the respective uncertainties in Dte. Technically, we performed a Monte-Carlo experiment based on the bootstrapping method: each vector Dd(C0n − Cmn) corresponding to some pair of years (n, n + 1) is attributed to another pair (e.g., l, l + 1), where l is a random number. The experiment included 100 inversions with such randomly mixed deviations. In order to estimate uncertainties in the emission trends associated with the numerical method and approximations we (i) replace the observed NO2 columns values with the those calculated by the model with the a posteriori emissions (serving as a substitute for an exact solution), (ii) perform the inversion, and (iii) calculate the differences between the results and the “exact” solution. These differences are summed up with the “random” uncertainties in the trends. To take into account uncertainties related to systematic biases in the modelled and measured NO2 columns, we scaled the measured NO2 columns by the mean ratio of the simulated (for the base case) and measured NO2 columns in a given grid cell and repeated the inversion procedure with such modified inputs. If the biases were caused by errors in NOx emissions specified for the base case, then such scaling would probably yield most accurate estimates of relative changes in emissions. But since we do not know the actual reason for the disagreements between the model and measurements, we attribute the differences between the results obtained with the original and modified input data to the uncertainty in our estimates of emission trends found with the original data for measured NO2 columns. Contributions from all different sources of uncertainties discussed above are summed up quadratically to provide an estimate of the overall uncertainty in the NOx emission trend for a given grid cell or country. Note, finally, that we do not attempt here to estimate a part of uncertainties in our results due to essentially unknown systematic uncertainties in the modeled or measured NO2 columns.
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However, it seems very unlikely that systematic errors in the NO2 columns exceed the random uncertainties estimated as it is mentioned above.
onlinear Approach (the Case of Emissions N from Megacities) Formulation of the problem. In a general case, our task can be formulated as follows. Let’s assume that we want to evaluate temporal changes of the value x, while in our disposal are only noisy inaccurate measurements y: y i = x i + e i , i = 1, …, n, where i is the temporal index (e.g. the number of year) and e are the errors. And let us make three assumptions. First, the noise is uncorrelated (white): áeiejñ = 0 for any i ¹ j, where the angled brackets denote averaging over a statistical ensemble. Second, the changes of the real value x are small in comparison with the level of noise (xi+1 − xi)2 « áe2ñ for any i. And third, e satisfies the normal distribution. The goal of the method is to filter out the noise without constraining the type of the temporal evolution of x. That is, our aim is to obtain a series of values xe, such that (xei − xi)2 « áe2ñ for any i. In principle, the second condition can be further relaxed; however it is not necessary in the framework of the given study. The special requirements are the self consistent estimation of uncertainties of the results and their applicability to short time series. General description of the algorithm. The basic ideas of our algorithm are the following. First, we use a neural network (of the perceptron type) for approximation of the unknown nonlinear trend: N
xei (w) = w0 + wL i + ∑ wk gk , k =1
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1 , 1 + exp (wˆ k i + wˆ 0 k )
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where w are weight coefficients, and N is the number of neurons. Indeed, it is well known that a neural network is a universal approximator; that is, given a sufficient number of neurons, it can approximate any smooth function with any given accuracy. Second, we follow the probabilistic approach applied here for estimation of weight coefficients of the neural network. Specifically, using Bayes’s theorem, we get the following a posteriori probability distribution function (pdf) for the weight coefficients.
n (y − x (w))2 i ei pa ( w), pa = const | w < wmax , p (w | y ) ∝ exp − ∑ 2s ε2 i =1 pa = 0 | w > wmax
(5)
where se2 = áe2ñ. Note that we a priori constrain only the maximum magnitude of w. In principle, we could specify the a priori pdf in different ways. The rectangular structure of the a priori pdf was chosen as the result of preliminary experiments with both artificial and real data. It was found that this simple structure enables both
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an efficient filtering of noise and high sensitivity of the algorithm to actual nonlinearities in input data. The maximum likelihood estimates of w can then be found as follows: n
wˆ (wmax , N ) = arg min ∑ (yi − xei (w )) , w ∈[− wmax ; wmax ] 2
(6)
i =1
Third, we use the leave-one-out method of the cross validation in order to filter out the noise. The idea is to minimize the difference between a given measurement and its approximation which was built without using this measurement. Specifically, following the Bayesian approach, we can define the probability distribution for the parameters involved in (5, 6):
n (y − x (wˆ ))2 i ei p (wmax , N | y ) ∝ exp − ∑ i =1 2s ε2
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Here, the estimate xei is obtained without using corresponding measurement yi. We assume that the difference between yi and xei is due to noise. By finding the maximum of this distribution, we define the optimal constraints for weight coefficients, wmaxх. Technically, this optimization is carried out by means of the onedimensional golden search method. In principle, a similar procedure could also be used to estimate the optimal number of neurons. However, in practice, it is also necessary to take into account that the uncertainties of estimates xei obtained with a larger number of neurons are larger. Besides, the differences between estimates obtained with different number of neurons are frequently too insignificant. Thus we define the optimal number of neurons in a different way. Specifically, we find the estimates of the trends consecutively with N = 0, N = 1, and so on, and each time we check the difference between xe(N) and xe(N + 1). When this difference becomes statistically insignificant (in terms of 68.3 percentile), the procedure is stopped. The corresponding value of N is considered optimal. We can also estimate the level of noise as follows: 2 1 n (8) ∑ (yi − xei (wˆ )) n i =1 This estimate is further used to assess the uncertainties in results by means of the Monte Carlo method. Specifically, we sample the errors, ei, from the normal distribution with se defined by Eq. (8) and add these errors to the xei (obtained using all data points). Then the whole estimation procedure is repeated (with the fixed optimal N) many times (in this study, 300) and the 68.3 percentile of the statistical distribution of the obtained estimates xe is evaluated. Because of the random errors e in the original dataset, there is a probability that our algorithm will detect a nonlinear trend even when the real values x demonstrate only a linear trend or no trend at all. In order to assess this probability, we performed an additional Monte Carlo experiment. Namely, we sampled an entirely random time series from the normal distribution having the same standard deviation as the original input data, and applied our algorithm to these random
σ ε2 ≈
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data. The 68.3 percentile of the distribution of xei obtained this way was then added to the uncertainty range of the optimal estimate of the trend. Before application to real data, the algorithm was tested with artificial time series representing both “ideal” and “noisy” cases. In the latter, the level of noise corresponded to that in the real data sets which we used in this study. In all the cases considered, the algorithm has managed to retrieve the character of the actual trend and to estimate the uncertainties properly. Combining measured and simulated NO2 columns over megacities. We assume that the dependence of NO2 columns on NOx emissions can be approximated as C(ti) = Cb(ti) + a(ti)E(ti), where C(ti) is the seasonally averaged NO2 column amount over a given megacity for a year i, Cb(t) is the “background” NO2 column amount which is not related to emissions from the given megacity, E(ti) are the seasonally average NOx emission rate and a(ti) is the sensitivity of the NO2 columns to changes of the NOx emissions. In special tests this linear approximation was found to be sufficiently accurate. Having in mind this approximation, we estimate the normalized annual emission rates as follows:
Co (ti ) − Cbm (ti ) E( t i ) / E 0 ≅ C m (ti ) |E = E0 −Cbm (ti )
(9)
where the indexes “o” and “m” denote the observed and modeled data, respectively, and E0 are emissions for the reference year (2001). By employing the model in the context of Eq. (9) we attempt to account for those variations in NO2 columns that are due to meteorological variability. The part of the meteorological variability that cannot be explained by our model is treated as random noise. Because values of emission rates are positive, it is reasonable to assume in a general case that their uncertainties satisfy lognormal distribution. Accordingly, we use the natural logarithms of the estimates defined by Eq. (9) as input data (y) for our analysis. Such time series were composed for 13 largest cities and urban agglomerations (with the total population more than four millions) covered by the domain of our model. Additionally, in case of Cairo, for which the EMEP emission data are not provided, the NOx emission changes were estimated directly from the measured NO2 columns: E(ti)/E(t0) @ Co(ti)/Co(t0), where t0 indicates the year 2001.
Results Decadal Trends in NOx Emissions in the Period from 1996 to 2005 Figure 1 presents time series of NOx emissions averaged over several countries. Rather close agreement between the new EMEP data and our estimates is found,
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Fig. 1 Time series and linear trends in anthropogenic NOx emissions averaged over several countries. Values provided in round brackets are estimates of uncertainties. Values reported in square brackets are the statistical uncertainty (the standard deviation) of a linear fit to the EMEP data (not including systematic uncertainty)
particularly for France, Germany and Great Britain. It is important to note that our estimates of the trends for these countries are in better agreement with new EMEP emission data than with old ones. Statistically significant differences between our results and the expert estimates are found for Italy, where our data suggest that the decrease in NOx emissions in the EMEP inventory is strongly overestimated. The differences between our results and the expert emission data are larger outside of Western Europe. Particularly, the directions of the trends in the measurements and the new EMEP data differ in Russia and Turkey. In Russia, there is also a big difference between the old and new EMEP data, and the new EMEP emissions do not agree with our estimates than the old ones. These observations and other results not shown here indicate that the current knowledge about emissions and their inter-annual changes in former USSR countries, in the Balkans and in the Middle East is still very incomplete and probably inadequate.
Estimates of Nonlinear Trends in Megacities in the Period from 1996 to 2008 Our nonlinear estimates of NOx emission trends for several megacities (with the total population of more than four millions) are shown in Figs. 2 and 3. Nonlinear trends are detected in Bagdad, Barcelona, Madrid, Milan, Moscow and Paris.
Fig. 2 Estimates of NOx emission trends in several large cities. The interannual changes (solid line with dots) are evaluated as a local slope of the trends. The EMEP emissions were averaged over 10 grid cells of our model, surrounding the city center (5 grid cells in the west-east direction and 2 grid cells in the south–north direction).
Fig. 3 The same as in Fig. 2 but for other cities
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These results should be considered as preliminary. Specifically, for Madrid and Moscow we got positive changes in 1990s and negative changes in more recent years. An interesting nonlinearity which indicates the growth of the rate of the negative trend in recent years is found also for Paris. Most probably, the latest tendencies in Madrid, Milan, Moscow, and Paris are related to the increase of the fraction of modern cars equipped with catalytic converters. In Bagdad the change of the direction of the trend is chronologically associated with the well known events of 2003. In the cases of Alexandria, Berlin, London, and Ruhr agglomeration nonlinearities are not detected. Our method gives negative linear trends. In Tehran, the nonlinearities are also not revealed but an interesting result here is a strong positive linear trend (about 5%). This trend can be regarded as an indication of stable development of the Iranian economy. Smaller positive linear trends are also revealed in Istanbul and Cairo. The EMEP data are in reasonable agreement with our estimates in most of the considered cities in Western Europe (Barcelona, Berlin, Madrid, London and Ruhr agglomeration) and also in Istanbul. In Paris, the EMEP data show a considerably stronger downward trend than that in our estimates until 2005, which is followed by an upward “jump” between 2005 and 2006. It seems very unlikely that emissions in such a big city as Paris can “suddenly” increase by about 15% during 1 year. Most probably, this jump and similar irregularities in EMEP data for several other cities are due to changes in methods used by EMEP for spatial allocation of emissions. In Milan, satellite data do not confirm the negative trend predicted by EMEP. The same kind of disagreement between satellite measurement based estimates and the EMEP data was found for the whole of Italy in the case of decadal trends discussed above (see Fig. 1). In Alexandria our analysis yields a negative trend, while the EMEP data show a positive trend. In Moscow, our estimates are in reasonable agreement with the EMEP data in the period from 1996 to 2005, but strongly disagree in the latest years. The fact that the EMEP data for Bagdad and Tehran are constant indicates that EMEP did not have sufficient information about emissions in these cities. The emissions for Cairo are not provided by EMEP. Taking into account that our model (based on the EMEP emission data) could not simulate the NO2 column over Cairo properly, the NOx emission trend in this city was retrieved directly from satellite data for NO2 columns. This approach can be associated with some underestimation of the magnitude of emission trend (up to about 30% according to our analysis).
hecking the Agreement Between the Measurement C Data and Simulations Obtained with the Measurement-Based Emission Estimates Optimization of NOx emissions should lead to improvements in the agreement between the modelled and measured data. A rather critical test of our results can be provided by comparison of independent data such as near surface NOx concentrations
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with corresponding simulations performed with the estimated NOx emission trends. Specifically, in order to validate the results of the inverse modelling, the following near-surface measurements have been used: (i) measurements of nitrogen oxides from the United Kingdom Automatic Urban and Rural Network (AURN) between 1996 and 2008, (ii) the measurements of NOx in Madrid (2000–2007) and NO2 in Milan (1999–2007) (the data obtained from the Airbase data base), and (iii) the measurements of NOx concentrations in Paris from Airparif. The raw hourly data have been processed to yield the seasonally averaged (over the summer months) daily mean NOx (or NO2) concentrations. Note that the amount of data available for validation is very limited particularly because the multi-annual measurements have to be performed at the same sites. Because of the low spatial representativeness of surface measurements (compared to satellite measurements), we have to combine data from several sites. The idea of validation of our nonlinear trend estimates is to calculate linear trends of concentrations over the periods where the corresponding nonlinear trends are monotonous. The results are presented in Figs. 4 and 5. Clear improvements (see Fig. 4) both in the trends and RMSE (defined as the RMS difference between respective time series) calculated for NOx concentrations in UK
Fig. 4 Comparison of NOx results of a model run for which changes of NOx emissions were specified using either (1) results of this study or (2) the most recent and (3) older expert data of the EMEP inventory with (4) measurement data for NOx near surface concentrations at AURN in Great Britain. The measurement and modelled data from individual monitors were combined to equalise contributions from sites with different level of air pollution. One sigma uncertainties in trends are indicated in brackets
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Fig. 5 Comparison of measurement data for NOx (or NO2) near surface concentrations in Madrid, Milan, Paris and London with corresponding results of a model run for which changes of NOx emissions were specified using both nonlinear and linear estimate of NOx emission trend (except for London where only a linear trend is estimated). The linear trends in concentrations and emissions are evaluated separately for the periods indicated below the figures
take place when the old EMEP data are replaced either with the new expert data or with our own estimates. The difference between results obtained with our emission estimates and new EMEP data is very small and statistically insignificant: the RMSE calculated with our emission estimates is slightly larger than that obtained with the new EMEP data, the trend in simulated data gets slightly closer to the trend in the measurement data, and improvements in the agreement between the data for
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individual years take place only for 6 years. These results indicate that the new EMEP data for Great Britain are already rather good. In the case of megacities (see Fig. 5), we have an almost perfect agreement between the linear trends in measured and simulated NOx concentrations in London. In the other cities considered, the simulations performed with our nonlinear estimates of emissions are more consistent with measurements than the simulations based on the linear estimates of emission trends. Specifically, the changes of “partial” linear trends in measurements are significantly better reproduced in simulations based on the nonlinear than linear emission trends.
Conclusions Our studies presented in this paper demonstrate the feasibility of monitoring NOx emissions by means of satellite. Specifically, we have shown that satellite measurements can be used for estimating multi-annual changes of NOx emissions either considered on a regular grid or representing the sources of air pollution in megacities. Here we investigated the decadal changes (between 1996 and 2005) in NOx emissions in Europe, the Mediterranean and the Middle East and estimated nonlinear trends of NOx emissions in several megacities over the period of 13 years (from 1996 to 2008). We used the data on tropospheric NO2 column amounts derived from the long-term GOME and SCIAMACHY measurements which were combined with calculations performed with the CHIMERE chemistry transport model. Our results indicate that, in agreement with expert estimates, NOx emissions in Western Europe have been mostly decreasing. Much larger differences between the satellite measurement based estimates and the EMEP data was found outside of Western Europe, indicating that emission inventories for those territories are not adequate. The analysis of NOx emission changes in megacities revealed statistically significant nonlinearities in emission trends in Baghdad, Barcelona, Madrid, Milan, Moscow and Paris. In particular, the NOx emissions in Moscow increased in 1990s but show a negative trend in more recent years. In Paris, the decrease of NOx emissions accelerated since about 2002. Available independent ground based measurements of nitrogen oxides in Madrid, Milan and Paris suggest that our nonlinear estimates of NOx emission trends describe actual emission changes more adequately than linear trends. Acknowledgements This research was funded by the Russian Foundation for Basic Research (grant No. 08-05-00969-a) and the European Commission through the GEOMON FP6 project.
References Bovensmann H, Burrows JP, Buchwitz M et al. (1999) SCIAMACHY – mission objectives and measurement modes. J Atmos Sci 56:127–150 Burrows JP, Weber M, Buchwitz M et al. (1999) The Global Ozone Monitoring Experiment (GOME): Mission concept and first scientific results. J Atmos Sci 56:151–175 Enting IG (2002) Inverse problems in atmospheric constituents transport. Cambridge University Press, New York
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Konovalov IB, Beekmann M, Vautard R et al (2005) Comparison and evaluation of modelled and GOME measurement derived tropospheric NO2 columns over Western and Eastern Europe. Atmos Chem Phy 5:169–190 Konovalov IB, Beekmann M, Richter A, Burrows JP (2006) Inverse modelling of the spatial distribution of NOx emissions on a continental scale using satellite data. Atmos Chem Phys 6:1747–1770 Konovalov IB (2007) Regional differences in decadal changes of the atmospheric emissions of nitrogen oxides in the European part of Russia: results of the inverse modeling based on satellite measurements. Doklady Earth Sci 417:685–688 Konovalov IB, Beekmann M, Burrows JP, Richter A (2008) Satellite measurement based estimates of decadal changes in European nitrogen oxides emissions. Atmos Chem Phys 8:2623–2641 Martin RV, Jacob DJ, Chance K, et al (2003) Global inventory of nitrogen oxide emissions constrained by space-based observations of NO2 columns. J Geophys Res 108. doi:10.1029/2003JD003453 Richter A, Burrows JP, Nüß H, Granier C, Niemeier U (2005) Increase in tropospheric nitrogen dioxide over China observed from space. Nature 437. doi:10.1038/nature04092 Vestreng V, Breivik K, Adams M, et al (2005) Inventory review 2005, emission data reported to LRTAP convention and NEC Directive, Initial review of HMs and POPs. Technical report MSC-W 1/2005, ISSN 0804-2446
Detection of Desertification Zones Using Multi-year Remote Sensing Data Lev Spivak, Irina Vitkovskaya, Madina Batyrbayeva, and Alex Terekhov
Abstract Desertification is one of the most important problems for Central-Asia countries. Since 2000 space monitoring of vegetation has been being done in Kazakhstan with NOAA AVHRR data. Integral Normalized Difference Vegetation Index (IVI) was used for identification of desertification area. This index was calculated by summation of decadal NDVI composites for the vegetative season. This paper presents the results. Keywords Desertification • Integrated NDVI • Vegetation degradation
Introduction Kazakhstan Republic occupies 2.7 million km2 area. Most the area is located in arid and semi-arid zones and is used as pastureland. Desertification is one of the most important problems which is connected to the impacts of climate variation/changes and increased anthropogenic activities in the recent 30–40 years. Since 2000, space monitoring of the republic’s area has been implemented in order to register vegetation condition changes and estimate desertification dynamics. In order to identify desertification areas it is necessary to separate seasonal changes caused by weather conditions from the long lasting sustainable vegetation degradation. Special method was developed for analysis and of the long-term component of vegetation dynamics. This paper describes the methodology and its application.
L. Spivak (*), I. Vitkovskaya, M. Batyrbayeva, and A. Terekhov Join-stock Company National Centre of the Space Research and Technology, Almaty, Kazakhstan e-mail:
[email protected];
[email protected] F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_26, © Springer Science+Business Media B.V. 2011
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Methodology NOAA AVHRR data were used for vegetation and desertification monitoring. Reflectances in the visible (VIS) and near infrared (NIR) parts of solar spectrum observed by the AVHRR instrument were used. The method includes the following procedures (Spivak et al 2006; Spivak et al 2007): 1. Processing of NOAA satellite images and a reference of images in Geographic Lat/Long Projection for WGS-84 spheroid 2. Calculation of daily Normalized Difference Vegetation Index NDVI = (NIR − VIS)/(NIR VIS) 3. Construction of decadal composite values of NDVI for each pixel based on maximal value of NDVI during a 10-day period 4. Calculation of the Integral vegetation index (IVI) following the equation below 27 IVI = ∑ NDVI i , i =10
where i is the number of decades from the beginning of a year. IVI describes seasonal volume of green biomass in each pixel and is effective for long-term analysis.
Results Based on the IVI for the 2000–2008 period Kazakh territory was divided into the following five zones with different vegetative productivity: Zone E – a very low (desert) Zone D – low Zone C – medium Zone B – temperate Zone A – high Figure 1 shows IVI-based 5-zone distribution during 2000–2008. As seen, the zones have latitudinal distribution and clearly show higher vegetation productivity in the south, deserttype of the environment in the central and mid-vegetation productivity in the north. As seen in Fig. 2 the size of the zone depends on seasonal weather conditions. The most favorable for vegetation year was 2002 and the worst year was 2006. Considerable increase in zone A and corresponding contraction of zone E are observed in years with favorable weather conditions. In unfavorable years, desert zone expands very much and the high productivity zone contract. In order to eliminate seasonal weather contribution we used the method of “transit zone”, which is the zone with minimal weather impact. Location of the “transit zone” with the area less than 13% (from the Kazakhstan total) is shown in Fig. 3. Note that such area as Semipalatinsk (eastern Kazakhstan) nuclear site is not taken into account.
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The “transit zone” is shown in Fig. 3 in the central Kazakhstan. The IVI-based estimates in Fig. 4 indicate that this zone is quite stable in size and equal approximately average long-term integral vegetative index value. Figure 5 shows dynamic of IVI/(IVImax)med for moderate weather conditions years.
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%, normaliz. IVI 15
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Fig. 3 Location of the “transit zone”, Kazakhstan
IVI/(IVI)maxmed, averaged by tranzit zone territory
Fig. 4 Dynamics of integrated vegetative index for the “transit zone” 0,290 0,280 0,270 0,260 0,250 0,240 0,230 0,220 0,210 2000
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Fig. 6 Areas of “transit zone” with low vegetation level during 2000–2007
Figure 6 shows a part of Kazakhstan map’s “transit zone” only and confirms worsening of vegetation conditions in this zone (dark color). The area with a low level of vegetative productivity in the transit zone gradually increases.
Conclusion Zoning of Kazakhstan territory with the seasonal Integrated NDVI was carried out during 2000–2008. Location of “transit zone” where weather impact on vegetation productivity is minimal was determined. Sites of sustainable vegetative degradation which is typical for desertification were identified.
References Spivak L, Vitkovskaya I, Batyrbayeva M (2006) Space monitoring of desertification processes in Kazakhstan with the use of long-term remote sensing data. IGARSS Proceeding, 2006, Anchorage, Alaska, pp. 23–27 Spivak L, Vitkovskaya I, Batyrbayeva M, et al (2007) Analysis of inter seasons vegetation dynamic on Turkmenistan area. Deserts assimilation problems. Ashgabad. 4(65):25–29
Satellite Desertification Monitoring in Sahara Mikhail A. Popov, Sergey A. Stankevich, Alexei I. Sakhatsky, Menny O. El Bah, Daoud Mezzane, and Igor A. Luk’yanchuk
Abstract Combating desertification in Sahara is important due to immense territory and lack of infrastructure. The objective of this article is the application of RS satellite methods for desertification monitoring in Sahara countries Mauritania and Morocco. It was demonstrated that RS methods is a useful tools to monitor desertification in Mauritania and Morocco. They require: dynamic nonlinear model of the ecosystem for long-time desertification forecast; database of the multi spectral satellite images, thematic maps correlated with other geo-meteo-data; and passive microwave and optical satellite data fusion procedures. Keywords Desertification monitoring • Microwave and optical data fusion
Introduction Desertification is land degradation, reduction of the bio-potential, intensification of aridity and wind-induced sand-mass transfer. This problem is very important in the Sahara countries Mauritania and Morocco leading to socio-economic problems. Being incapable to combat with desertification alone, Mauritania emerges as M.A. Popov (*), S.A. Stankevich, and A.I. Sakhatsky Scientific Centre for Aerospace Research of the Earth NAS of Ukraine, Kiev, Ukraine e-mail:
[email protected];
[email protected];
[email protected] M.O. El Bah University of Nouakchott, Nouakchott, Mauritania D. Mezzane Cadi, Ayyad University, Marrakech, Morocco e-mail:
[email protected] I.A. Luk’yanchuk University of Picardy, Amiens, France e-mail:
[email protected] F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_27, © Springer Science+Business Media B.V. 2011
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the important geo-political link between Central and North Africa. An explosive growth of Moroccan economy substantially stimulates the cultivation of virgin dry lands (ORMVAT 2007). The modern remote sensing (RS) satellite-image technology is the cheapest and most appropriate tool to deliver the reliable information about decertification, especially in remote regions (Campbell 2007). RS observations are normally used to take decisions about land exploitation and management especially in agriculture, hydrology, water management, geology, mineral resources, cartography, national security and, environmental changes. RS methods are used occasionally in Mauritania and Morocco due to a lack of experience, coordination and administrative difficulties (Abdellaoui 1989). The major problem is the interpretation of RS images together with geo-informatic data. Following our RS application in Emirates, Libya, Nigeria, Algeria, Kazakhstan and Turkmenistan (Lyalko and Popov 2006) we present the distributed network of desertification monitoring to transfer, locally adopt and apply the progressive RS technology to Mauritania and Morocco. The program of counter-desertification with RS methods should include: (a) evaluation of desertification with RS and geo-information data; (b) acquisition of multi-spectral and radar satellite images and creation of distributed database; (c) development of the nonlinear dynamical model of ecosystems; (d) development of image recognition algorithms to classify the elements of desert landscape (dunes, massifs, barchans) and to parameterize their dynamics; (e) collection of in situ measurements and matching them with geo-meteo-cartographical information; (f) development of long-range forecasts of eco-dynamics, availability of water and mineral resources. Implementation of this program will help to evaluate the risks of desertification and address water and natural resources. This article’s goals are to present the application of RS satellite methods for desertification monitoring in Sahara countries Mauritania and Morocco.
Implementation of RS Technology in the Sahara Countries Morocco and Mauritania having a very different level of economic development have common historical, social language and cultural traditions. Moreover they form a common block with European – African commercial, human, transportation and technological links. In the last decade Morocco had explosive economic development which triggered exploration and monitoring of natural resources and environment. Several national and international programs and centres introduced the application of RS methods for desertification monitoring (ORMVAT 2007; Web 1 2010; Web 2 2010; Web 1 2010). The economical and political situation in Mauritania is less stable then in Morocco that creates problems application of RS methods. The Nouakchott University created a laboratory for processing satellite images to address such natural hazards as desertification, flood, landslide, insect invasion etc. Figure 1 demonstrates application of RS methods in Mauritania.
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Fig. 1 Desertification monitoring of town Nouakchott: (a) general view, (b) RS-based GIS image
Fig. 2 AMSR-E/MODIS data fusion for desertification mapping in Morocco on May 28, 2009: (a) AMSR-E 26 km soil moisture product; (b) AMSR-E 1 km wrapped soil moisture product; (c) MODIS 1 km band 4, band 6 and land surface temperature product RGB synthesis (13:35); (d) MODIS 1 km band 4 (visible)/band 6 (SWIR) NWI product; (e) AMSRE-E/MODIS microwave/optical fused 1 km soil moisture product
Passive microwave data were applied to obtain soil moisture using inverted microwave radiometer response model (Chauhan et al.1994, Chauhan 2002). These data were enhanced by applying optical satellite data. For this purpose the Normalized water index (NWI) was used (Sakhatsky 2006):
NWI =
Eλ = 0.55 µ m − Eλ =1.65 µ m Eλ = 0.55 µ m + Eλ =1.65 µ m
(1)
where El = 0.55 mm – spectral radiance in visible band and El = 1.65 mm – spectral radiance in SWIR one. In addition land surface temperature is also applied for desertification monitoring (Liu et al. 2002). Figure 2 demonstrates the land cover moisture spatial resolution enhancement using AMSR-E (passive microwave 26 km resolution) and MODIS (multispectral optical 1 km resolution) data products. The algorithm for enhanced resolution land
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cover moisture assessment by microwave/optical fusion has been preliminary validated over north-west Sahara under non-cloudiness condition (correlation coefficient is 0.62). This technique is useful also for land cover classification (Popov et al. 2008).
Conclusion To combat desertification in agricultural and industrial regions of west part of Sahara a reliable and relatively non-expensive method is required to control the components of Sahara ecosystem (vegetation, sand and water). It was demonstrated that RS methods is a useful tools to monitor desertification in Mauritania and Morocco. They require: dynamic nonlinear model of the ecosystem for long-time desertification forecast; electronic database of the multi-spectra satellite images, thematic maps correlated with other geo-meteo-data; and passive microwave and optical satellite data fusion. Acknowledgement This research was supported by the NATO Science for Peace and Security (SPS) Programme, Grant Reference SPS MD SFP 984085.
References ORMVAT Office of agriculture (2007) Monographie agricole de la region Meknès – Rafilalet”. http://www.aui.ma/enhanced/regionmeknes/html/monogr_de_la_rmt_oct_2007.pdf Campbell JB (2007) Introduction to remote sensing. Taylor & Francis, New York, 626 p Abdellaoui A (1989) Développement et télédétection des ressources naturelles au Maghreb Central Télédétection en francophonie. AUPELF-UREF John Libbey Eurotext, Paris. Linsenbarth A (1996) A Lyalko VI, Popov MO (eds) (2006) Multi-spectral remote sensing for nature management. Naukova dumka, Kiev, 360 p Web 1 (2010) http://www.crts.gov.ma/desertification/forma_suivi_global.pdf Web 2 (2010) http://www.ucam.ac.ma/cners/ Web 3 (2010) http://doc.abhatoo.net.ma/doc/img/pdf/desertification-2.pdf Chauhan N, LeVine D, Lang R (1994) Discrete scatter model for radar and radiometer response to corn: comparison of theory and data. IEEE Trans Geosci Remote Sens 32:416–426 Chauhan N (2002) Soil moisture inversion at L-band using dual-polarization technique: A model based sensitive analysis. Int J Remote Sens 23:3209–3227 Sakhatsky AI (2006) Satellite data application for the water exchange modeling in geosystems (Ukrainian). Proc NAS Ukraine 4:118–126 Liu L, Zhang B, Xu G, Zheng L, Tong Q (2002) Vegetation classification and soil moisture calculation using land surface temperature (LST) and vegetation index (VI). Proc SPIE 4730:319–323 Popov MA, Stankevich SA, Sakhatsky AI, Kozlova AA (2008) Land cover contextual classification using space imagery for wetland and forest monitoring. Proceedings of the United Nations/ Austria/European Space Agency Symposium Space Tools and Solutions for Monitoring the Atmosphere and Land Cover, Graz (Austria)
Part V
Satellite & In Situ Long Records For Trend Analysis, Modeling & Monitoring
Global Vegetation Health: Long-Term Data Records Felix Kogan, Wei Guo, and Aleksandar Jelenak
Abstract The new Global Vegetation Health (GVH) data set has been developed for operational and scientific purposes. The GVH has advantages before other long-term global data sets, being the longest (30-year), having the highest spatial resolution (4-km), containing, in addition to NDVI, data and products from infrared channels, originally observed reflectance/emission values, no-noise indices, biophysical climatology and what is the most important, products used for monitoring the environment and socioeconomic activities. The processed data and products are ready to be used without additional processing for monitoring, assessments and predictions in agriculture, forestry, climate change and forcing, health, invasive species, deceases, ecosystem addressing such topics as food security, land cover land change, climate change, environmental security and others. Keywords Vegetation health • 30-year 4-km data records • Vegetation Condition Index (VCI) • Temperature Condition Index (TCI) and Vegetation health indices (VHI) • NDVI and BT
Introduction One of the most important long-term (30 years) satellite-based data records characterizing land surface, air near the ground and climate were created from the Advanced Very High Resolution Radiometer (AVHRR) flown on the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Several F. Kogan (*) NESDIS/NOAA, Center for Satellite Application and Research (STAR), Washington DC, USA e-mail:
[email protected] W. Guo IMSG Inc., Washington DC, USA A. Jelenak University Corporation for Atmospheric Research, Washington DC, USA F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_28, © Springer Science+Business Media B.V. 2011
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global data sets were developed from the AVHRR records since the early 1980s. They were NOAA’s Global Vegetation Index (GVI and GVI-2), NASA’s Pathfinder and GIMMS (Tarpley et al. 1984; James and Kalluri 1994; Kidwell 1997; Tucker et al. 2004). These data were focused only on the Normalized Difference Vegetation Index (NDVI), ignoring infrared measurements, which are very useful for monitoring land, climate and socioeconomics. Therefore, NOAA has developed new data set entitled the Global Vegetation Health (GVH). The GVH has advantages before other long-term global data sets, being the longest (30-year), having the highest spatial resolution (4-km), containing, in addition to NDVI, data and products from infrared channels, originally observed reflectance and emission, many indices with suppressed noise, biophysical climatology and what is the most important, products used for monitoring the environmental and socioeconomic activities (Kogan 1995, 1997). This paper describes the new, considerably improved and currently available to users the NOAA’s global AVHRR-based operational GVH data set at 4-km (0.036°) resolution.
Satellites, Sensor, Data, Noise, Noise Removal, GVH Method The 30-year, 4-km, 7-day composite GVH data records were developed from the measurements made by the AVHRR instrument flying on board NOAA polarorbiting operational satellites. The NOAA/AVHRR is a cross-track scanning system (Kidwell 1995, 1997; Cracknell 1997) sensing the Earth and the atmosphere near the ground continuously through the 30-year history (from the early 1980s to the present) in the following wavelength of the solar spectrum: the visible (VIS, 0.58–0.68 mm, channel 1 (Ch1)), near infrared (NIR, 0.725–1.1 mm, channel 2 (Ch2)) and two infrared (IR, 10.3–11.3 mm, channel 4 (Ch4) and 11.5–12.5 mm, channel 5 (Ch5)). The AVHRR instrument scans the Earth continuously at a 1.1-km ground resolution and the measurements are sampled and recorded for the entire globe at 4-km resolution contributing continuously to the NOAA’s Global Area Coverage (GAC) data set (Cracknell 1997). From the 14 NOAA satellites flying in sun-sinchronous orbit and carrying the AVHRR instruments, the GVH system and data sets were developed from seven afternoon satellites: NOAA-7, 9, 11, 14, 16, 18 and 19 launched on June 23, 1981 (local day time at launch 14:30), December 12, 1984 (14:20), September 24, 1988 (13:30), December 30, 1994 (13:30), September 21, 2000 (13:44), May 20, 2005 (13:50) and June 2, 2009 (13:44), respectively. These satellites operated during 1981–1985, 1986–1989, 1989–1994, 1995–2000, 2001–2005, 2005 to present and 2009 to present, respectively. During September 1994–January 1995 no afternoon operational observations were produced since NOAA-11 satellite was malfunctioned and new NOAA-13 satellite failed soon after launch. Also, during January–June 2005, NOAA-16 was malfunctioning from time to time and its data were replaced with NOAA-17 (morning satellite) preliminary calibrated to the
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NOAA-16 data. From the indicated satellites, NOAA-7 and 9 carried AVHRR-1 instrument, NOAA-11 and 14 – AVHRR-2 and the rest – AVHRR-3. All of them have identical design but with slightly different response functions. The VIS and NIR channels were pre-launch calibrated for converting counts into reflectance. The IR channels were in-flight calibrated and their count values were converted to brightness temperature. Due to chlorophyll and carotenoid pigments, leaf interior (mesophyll structure) and water content in green vegetation, the VIS and NIR measurements provide a means of monitoring vegetated surfaces. Difference between the NIR and VIS increases when vegetation becomes greener, more vigorous (more water) and denser. This is the main principle for the vegetation indices. The (NIR-VIS) difference was normalize representing the Normalized Difference Vegetation Index (NDVI = (NIR − VIS)/(VIS + NIR)). The IR channels were corrected for non-linear behavior of the instrument (Cracknell 1997). The GVH system algorithm starts form data extraction from the AVHRR/ CLAVR-x processing system (Jacobowitz et al. 2003; Heidinger and Pavolonis 2009) and collating the data onto a global GVH grid. This grid is based on the Plate Carre map projection. The global data spans from 75.024° (north edge) to −55.152° (south edge) in the latitudinal and from −180° (west) to 180° (east) in longitude directions. This processing supports nominal grid cell length of 4-km (3,616 * 10,000 grid elements). The GVH input includes the CLAVR-x navigation (NAV), observation (OBS), and geo-location (GEO) files for each Global Area Coverage (GAC) Level 1b orbit. Daily data are aggregated to a 7-day period using compositing method (saving the day which has the highest NDVI during the period). The compositing starts on the first day of a year and a period must have at least 4 days in the same year. The GVH output is a single file for each processing period containing metadata for each output variable, sensor and solar zenith angle, relative azimuth angle and ch1, ch2 counts and ch4, ch5 brightness temperature in the Hierarchical Data Format (HDF) similar to CLAVR-x output. One of the important steps in the primary data processing is radiometric calibration of visible and correction of thermal channels. Visible channels’ calibration consists of generally two steps: pre- and post-launch calibration. Based on Kidwell (1995), the following pre-launch linear formula (A = S * C + I ) is applied, where (A) is albedo, (S) is slope and (I) is intercept. Since the instrument output does not remain the same after launch, post-launch calibration was applied (R = S * (C − cd)) to NOAA-7 to 14 satellites, where C is 10-bit radiance count and cd – dark count) following Rao and Chen (1993, 1999). For NOAA-16 through 19 a dual slop calibration method was applied. Long- and Short-term Noise. Noise in AVHRR data creates fundamental constraints to the remote sensing of the Earth. The noise sources are physical, geometrical, mechanical, mapping, environmental, random etc.; some of them long-term, some short-term and some both (Kogan et al. 1996; Rao and Chen 1993, 1999; Cracknell 1997; Kidwell 1997). Clouds and other atmosphere constituents (aerosol, water vapor etc.) obscure the land surface reducing NDVI considerably. In case of unusual events, such as sharp volcanic aerosol increase, NDVI can be
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depressed for a long time (Kogan et al. 1994). Changes in viewing and illumination geometry can lead to both an increase and decrease in NDVI depending on location, type of vegetation, position of sun and sensor. Satellite orbital drift, sensor degradation, and satellite change create long-term noise in NDVI data, especially after a satellite has been in service for more than 3 years. A few techniques have been designed to detect and reduce some noise in AVHRR data such as cloud and aerosol-screening, bi-directional effects, pre- and post-launch calibration, geometry effects, sensor degradation satellite orbital drift. Regardless of all these achievements, a complete physically based correction for all effects over various land surfaces, able to eliminate high, medium and low frequency noise, is not available. Unfortunately, NDVI and IR annual time series values experience large fluctuations (Kogan 1995) introducing some errors when this data is used for monitoring purposes. Many of these fluctuations are associated with non-physical causes, such as method of data sampling and processing, satellite navigation and orientation, observation and communication errors, and other random noise. It is unlikely to develop corrections for this type of noise. Besides, if clouds are detected the data are discarded, leaving a hole on a map. This put additional constraint on AVHRR data utility. In the development of GVH method and algorithm, major long- and short-term noise creating problem for satellite data interpretation and applications was removed from the data. They are (a) satellite orbit and sensor degradation; (b) jumps between the satellites; (c) excessive stratospheric aerosols; (d) difference in Equator crossing time; (e) difference between AVHRR sensors; (f) high frequency (short-term) noise; (g) random noise. Some of the noise sources are interrelated, some have additive impact, some has either short- or long-term contribution and some provides a combine input. Visible channels are affected by all noise sources, while infrared mostly by short-term because they are calibrated on board. A large satellite data distortion in the visible channels and NDVI occurs due to such long-term noise as satellite orbital drift, AVHRR sensor degradation and excessive stratospheric aerosols from volcanoes. The orbital drift and sensor degradation affected visible measurements on all space platforms; they normally start 2–3 years after a satellite was in space. NDVI reduction was observed on the data collected from NOAA-9 during 1987 and 1988, NOAA-11 (1993 and 1994) and NOAA-14 (1999 and 2000). In addition, reflectance/emission measured from NOAA-11 were distorted due to elevated stratospheric aerosols during 1991–1993 resulted in NDVI reduction. The aerosol was built after eruption of Mt Pinatubo volcanoes in the Philippines in mid-June 1991. In a few weeks after eruption, the aerosols encircled the Earth with the major air flow and stayed in the area between 30–20°N and 20–25°S for nearly 1.5–2 years. As the result the NDVI in that area dropped almost in half during second part of 1991 through 1992 (Kogan et al. 1996; Vargas et al. 2009). Similarly, NDVI reduced in tropical latitudes in April 1982 for 1–2 years after eruption of El Chichon volcano in Mexico (Stowe et al. 1992). Some difference between NDVI calculated from NOAA-16 and 18 satellites reflectance and from NOAA-14 and 11 existed during 2000–2010. This occurred because the AVHRR-3 instrument has slightly different characteristics than
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AVHRR-2. Specifically, the area under response function curve for the first two was 8–12% narrower than for the last two. As the result the NDVI from NOAA-16 and 18 turned out to be higher than for the previous satellites. Moreover, in addition to NDVI reduction for NOAA-7 and 9 due to stratospheric aerosol and sever orbit degradation, general level of NDVI for the 1981–1988 period was 10–15% smaller than for the next two satellites because the time of the equator crossing for the first two satellites was almost one hour later. As the result of these problems and also orbit-degradation errors, large jumps in NDVI were observed between the end of previous and the beginning of the next satellites. The IR channels are also affected by stratospheric aerosol and orbit degradation (although less than the VIS) and must be corrected. Finally, high frequency noise inside each year created by clouds, variable transparency of the atmosphere (water vapor, dust, chemicals etc.), surface anisotropy, geometry of the sun and sensor, position of satellite, methods of data processing, random noise (including human errors) and long-term sources of noise distort considerably reflectance/emission of both NDVI and BT creating difficulties for satellite data application. Noise removal. As it has been mentioned, quite often different sources of noise affect NDVI and BT: either reducing their values (sensor degradation, orbital drift, equator crossing time, atmospheric attenuation, volcanic eruptions etc.) or increasing (spectral response function, off-nadir view etc.) or both depending on parameters (sensor type, surface anisotropy, forward/backscattering, sampling, random errors etc.). Moreover, quite often, several sources of noise affect the measured parameters the same time and with different intensity. It is hard to develop individual procedure for each source of noise because it is unknown how to separate them (for example elevated stratospheric aerosol and sensor/orbit degradation). Therefore, the noise in GVH data was removed empirically by comparing satellite and in situ observations applying the methods of mathematical statistics plus validation. Bias related to sensor degradation, satellite orbital drift, jumps in the indices while transitioning from one satellite to the next/previous and an elevated stratospheric aerosol were removed by applying the Empirical Distribution Function (EDF) method. (Crosby et al. 1996). An EDF is based on the assumption that for large areas, the NDVI reduction due to technical and external forces (orbital drift, volcanic eruptions, etc.) is larger than the weather-related NDVI changes from year to year. This can be expressed through probability that a random variable X is less than a given value x [F(x) = Pr{X < x}]. Following this assumption, large and stable changes in NDVI and BT can signal unexpected disturbances due to non-weather related causes discussed here. As a rule, sensor and satellite orbital degradation, increase and aerosol reduce NDVI and BT. This negative effect can be reduced by adjusting the EDF of parameters for the affected years with a benchmark EDF for the non-affected. The benchmark EDF for NDVI and BT were statistically composited from data of the 5 non-affected years: 1989, 1990, 1995, 1996, and 1997 (Vargas et al. 2009). The benchmark EDF’s for NDVI and BT were developed for each latitude lines and week of the year. The normalization of
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the distorted data was performed for each pixel inside every latitude line and for each week by adding the difference between distorted and benchmark EDFs to each distorted pixel values of NDVI and BT for each latitude line and week. The thresholds (0.01 for NDVI and 2°C for BT) for the correction was selected based on data analysis. A stable long-term bias between NDVI from NOAA-16, 18 (2000–2010) and previous satellites due to response function differences was removed by calibrating the distorted NDVI against the NDVI measured by an on-ground radiometer during the growing season of 2002 over soybeans and corn at the experimental station of the University of Nebraska-Lincoln. During that period the fields’ vegetation fraction (VF) changed from 10 at the beginning of the season to 90% at the end. The in situ NDVI were compared with the top-of-canopy NDVI (obtained by running radiative transfer model) from AVHRR-3 sensor on NOAA-16. The comparison showed that NOAA-16 and later NOAA-18 NDVI must be reduced by 10% for all VF in order to remove response function curve differences with previous satellites. Another stable long-term bias, lower NDVI level for NOAA-7 and 9 (1981– 1989) compared to the later satellites, was investigated. A half an hour data collected from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the Meteosat Second Generation (MSG-2) European satellites were used to investigate NDVI dynamics in relation to the local time of observations. The results indicated that the bias developed because for the earlier satellites the equator crossing time was half to one and a half hours later than for the following satellites. SEVIRI’s NDVI analysis for the 2009 indicated that NDVI drops between 6% and 11% if observation local time moves from 13:00 to 14:30. In order to correct this distortion the EDF method was used. High frequency noise (clouds, aerosol, bi-directional reflectance, sun and sensor angles, human errors, other random noise) were removed from NDVI and BT by applying statistical methods. The vegetation-oriented method for a comprehensive noise reduction stems from a statistical approximation of the vegetation and temperature annual time series. The idea was to (a) single out the seasonal cycle; (b) suppress high frequency noise, and (c) enhance medium and low frequency variations related to large-scale and persistent weather fluctuations. This technique considers smoothing the weekly time series with a combination of a compound median filter and the least squares technique (Kogan et al. 1997). Numerous tests showed that this smoothing eliminated completely the high frequency outliers, including random, approximated accurately the annual NDVI and BT cycles, and, more importantly, singled out medium-to-low frequency weather-related fluctuations (valleys and hills in the NDVI and BT time series) during the annual cycle (Kogan 1995). Figure 1 shows time series of completely processed no noise NDVI averaged over three 1.0° latitudinal circles. Each diagram covers several ecosystems: broadleaf forest (54.5–55°N), mostly desert with some contribution from Southeast Asia forest (24.5–25°N) and mostly tropical forest (5.0–5.5°S). As seen, the time series (a) after do not show neither long-term trend nor short-period (5–10 years) trends;
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Fig. 1 Processed mean NDVI (left) and BT (right) for three global latitude bands
(b) there are no any jumps between the satellites (1985–1986, 1988–1989, 1994–1995); (c) no NDVI reduction due to satellite orbit, sensor degradation and excessive stratospheric aerosols (1982–1984 and 1991–1994); (d) no difference due to Equator crossing time; (e) no difference due to AVHRR sensors changes; and (f) no high frequency and random noise are observed. GVH Method. After noise removal, weather-driven differences in NDVI and BT between the years become apparent: lower NDVI and higher BT in dry years and opposite in normal and wet years. This principle of comparing NDVI and BT for a particular year with their dry–wet range calculated from 30-year observations was laid down in the GVH algorithm development. The absolute maximum and minimum of NDVI and BT during 1981–2005 were calculated for each of the 52 weeks and for each pixel. They were then used as the criteria to estimate the upper (favorable weather) and the lower (unfavorable weather) limits of the ecosystem resources. Further, for estimation of weather impacts on vegetation condition, NDVI and BT values for a particular time (1 week or several weeks) were normalized relative to the absolute max/min interval. Following this procedure, NDVI and BT were rescaled based on Eqs. (1–3). They were named the Vegetation Condition Index (VCI), Temperature Condition Indices (TCI) and Vegetation Health Index (VHI) designed to characterize moisture (VCI), thermal (TCI) and total vegetation health (VHI) conditions in response to weather impacts
VCI = 100 * (NDVI − NDVI min ) / (NDVI max − NDVI min ) TCI = 100 * (BTmax − BT )/ (BTmax − BTmin ) VHI = a * VCI + (1 − a )* TCI
(1) (2) (3)
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Fig. 2 Dynamics of vegetation health indices during the 2009 spring drought in southern Taxes (for a box with longitude 98.0–97.7°W, latitude 28.0–28.3°N) from NOAA-18 satellite
where NDVI, NDVImax, and NDVImin (BT, BTmax, and BTmin) are the smoothed weekly NDVI (BT), their multi-year absolute maximum, and minimum, respectively. The VCI, TCI and VHI approximate the weather component in NDVI, BT and their combination values. They fluctuate from 0 to 100, reflecting changes in vegetation conditions from extremely bad to optimal. An example of VH assessment and analysis of the 2009 spring drought in the southern Taxes is shown in Fig. 2. As seen, all indices show drought (below 40) from January 2009. The worse conditions developed due to extremely high temperatures (TCI close to 0), which continued through the entire spring (almost 4 months). As the result of the extreme heat, VCI (moisture index), which indicated mild (30–40) drought in January start deteriorated quickly reaching extreme drought level of 5–10 during March and April. Since both VCI and TCI showed extreme drought conditions the total vegetation health conditions were extremely unfavorable. In May, drought recovery has started, because the temperature cooled off.
Conclusions The new Global Vegetation Health (GVH) data set has been developed for operational and scientific purposes. The GVH has advantages before other long-term global data sets, being the longest (30-year), having the highest spatial resolution (4-km), containing, in addition to NDVI, data and products from infrared channels, originally observed reflectance/emission values, no-noise indices, biophysical climatology and what is the most important, products used for monitoring the environment and socioeconomic activities. The processed data and products are ready to use without additional processing, since the noise was completely removed and the products were developed based on vegetation requirements and the laws governing the vegetation. In addition to monitoring, the GVH data can be used for predictions since some parameters such as NDVI, VCI and partially VHI have “memory” reflecting cumulative impacts of the environment on vegetation canopy (Kogan 1995). GVH time series can be also used for climate-related trend analysis
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such as vegetation green up, start/end of the growing season, gross primary production, losses in vegetation productivity, land cover change etc. It is important also to mention a few data correction procedures ignored in other global data. A few of them are removal of high frequency noise, also difference in response functions between AVHRR-3 and previous versions of the instruments, inconsistencies between the satellites in equator crossing time parameter. Moreover, some corrections were verified versus in situ data to see if the data quality is improving. The next steps in this research is comparison of GVH with other global datasets and analysis of climate induced vegetation green up.
References Cracknell AP (1997) The advanced very high resolution radiometer. Taylor & Francis, USA, 534 p Crosby DS, Goldberg MD, and Chung W (1996) Inter-satellite calibration using empirical distribution functions. 8th Conference on Satellite Meteorology and Ocean, Atlanta GA. American Meteorological Society, pp 188–190 Heidinger AK, Pavolonis MJ (2009) Global daytime distribution of overlapping cirrus cloud from NOAA’s Advanced Very High Resolution Radiometer. J Climate 18(22):4772–4784 Jacobowitz H, Stow LL, Ohring G, Heidinger A, Knapp K, Nalli N (2003) The advanced very high resolution radiometer PATHFINDER Atmosphere (PATMOS) climate data set: a resource for climate research. Bull Am Meteorol Soc June:785–793 James ME, Kalluri SN (1994) The Pathfinder AVHRR land data set: an improved coarse resolution data set for terrestrial monitoring. Int J Remote Sensing 15:3347–3363 Kidwell KB (ed) (1997) Global vegetation index user’s guide. National Oceanic and Atmospheric Administration, National Environmental Satellite Data and Information Services, National Climatic Data Center, Camp Springs MD, USA Kidwell KB (ed) (1995) NOAA polar orbiter data users guide. National Oceanic and Atmospheric Administration, National Environmental Satellite Data and Information Services, National Climatic Data Center, Camp Springs MD, USA Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78:621–636 Kogan FN, Sullivan JT, Ciren PB (1996) Testing post-launch calibration for the AVHRR sensor on world desert targets during 1985–1993. Adv Space Res 17(1):47–50 Kogan FN (1995) Droughts of the late 1980s in the United States as derived from NOAA Polar Orbiting Satellite Data. Bull Am Meteorol Soc 76:655–668 Kogan FN, Sullivan J, Carey R, Tarpley D (1994) Post-pinatubo vegetation index in Central Africa. Geocarto Int 3:51–58 Rao CRN, Chen J (1993) Calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer (AVHRR) after launch. Proceedings the International Society of Optical Engineering, Orlando, FL, pp 56–66 Rao CRN, Chen J (1999) Revised post-launch calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer on the NOAA-14 spacecraft. Int J Remote Sensing 20:3485 Stowe LL, Carey RM, and Pellegrino PP (1992), Monitoring the Mt.Pinatubo aerosol layer with NOAA/11 AVHRR data, Geophys. Res. Lett., 19:159–162. Tarpley JP, Schneider SR, Money RL (1984) Global vegetation index from NOAA-7 meteorological satellite. J Climate Appl Meteorol 23:491–494 Vargas M, Kogan F, Guo W (2009) Empirical normalization for the effect of volcanic stratospheric aerosols on AVHRR NDVI. Geophys Res Lett 36:L07701 Tucker CJ, Pinzon JE, Brown MB, Slayback DA, Pak EW, Mahoney R, VermoteEF, El Salcous N (2004) An extanded AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sensing 15(7):340–354
Aero-Space Radar Online Monitoring of Disasters in Ukraine Mariya Belobrova, Dmitry Bychkov, Anatoly Boev, Alexandre Gavrilenko, Valentin Efimov, Alexandre Kabanov, Ivan Kalmykov, Alexandre Matveev, and Valery Tsymbal
Abstract This paper provides examples of utilization of space radar (SLRARS) data for natural and anthropogenic disasters prevention. Ukrainian scientists have a good expertise in using low-cost, high-performance spaceborne and airborne sidelooking radars (SLRARS). These instruments help to keep tracking different natural and technogenic catastrophes and evaluate the most essential parameters characterizing these phenomena and events.
Environmental Phenomenon In October 1983, the heavy masses of multiyear near-polar ice started moving southward, and the convoy of 22 vessels got nipped in the Longa strait to the south of the Wrangel Island. Soon the most powerful atomic ice-breaker headed to the distressed vessels. But the multiyear ice whose thickness was >5 m was really “hard nut to crack”. Finally, one vessel was mercilessly crushed by the ice and sank and the other was badly damaged. The lost ships with cargoes were estimated at over $8 billion plus thousand people had to be airlifted from those far-flung areas of Chukotka. The first radar images of this area showed that100 km to the north of the Wrangel Island there was a vast zone covered with thin ice, which could open the clear way to the long-suffering convoy. The radar images allowed one to discern the fissures and patches of ice-free water in heavy multiyear ice shown
M. Belobrova () Usikov Institute of Radiophysics and Electronics, NAS of Ukraine, Kharkov, Ukraine e-mail:
[email protected] D. Bychkov, A. Gavrilenko, V. Efimov, A. Kabanov, I. Kalmykov, A. Matveev, and V. Tsymbal Kalmykov Center for Radiophysical Sensing of the Earth, NAS of Ukraine, Kharkov, Ukraine A. Boev Institute of Radioastronomy, NAS of Ukraine, Kharkov, Ukraine
F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_29, © Springer Science+Business Media B.V. 2011
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Fig. 1 (a) Initial SLRAR image of the Longa Strait (acquired from “Cosmos-1500”, October 20, 1983). A large “polynia” (ice-free path) is clearly seen near Wrangel island. It is located 100 km northward of the area where the convoy had been ise-trapped; (b) an interpretation map of the same area; different types of sea ice and convoy route are indicated
in (Fig. 1), right up to the Wrangel Island. The patches of ice-free water stretching out as far as that zone were found in the hummocked ice fields. The convoy promptly altered its course and headed north ward (Kalmykov et al. 1993). As the fleet of cargo ships followed the ice-breakers, it soon succeeded in getting closer to the young ice zone and sailing further safely arriving to the port of Pevek in a few days. Another example helped to prevent disaster in the Dnieper basin. In spring 1988, fast and vigorous snow melting was predicted in Ukraine based on synoptic situation. In order to forestall the possible failures of the hydrotechnical facilities on the Ukrainian rivers an initial desire was to drain water from the man-made storages of the Dnieper cascade. However, this option was rejected because the water from one of the man-made reservoir was saturated with radioactive nuclides from the Chernobyl disaster. The data from the SLRAR of the “Cosmos-1766” satellite showed that snow melting rate is going to be far below the predictable the overflowing of those artificial water storages was not expected. Similar snow melt monitoring was successfully performed in the spring 1996.
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Monitoring of Extratropical and Tropical Cyclones, Fronts, Squalls and Other Dangerous Phenomena in Sea–Air System The SLRAR X-band data enable one to evaluate the intensity at which the energy from atmosphere to ocean is transferred. In other words, the intensity of a scattered radio signal is directly related to the spectral density of short ocean waves (ripples). The simultaneous application of both radar and optical data allows the energy exchange between ocean and atmosphere to be adequately monitored (Mitnik and Viktorov 1980). Figure 2 illustrates two radar images of swiftly developing young polar cyclone. The images were acquired by the spaceborne “Cosmos-1500” SLRAR several days apart. No optical images are given, because the radar surveying operations were performed during the polar night and the cloud cover was not visible. As seen from Fig. 2a, the radar image shows that in the Norwegian Sea, close to the ice sheet, a young polar cyclone (size 80–100 km) is in its initial formation phase and the near-sea surface wind inside it does not exceed 12–15 m/s. In this early phase the typical vortex pattern of this cyclone is pronounced much in the same way as its structural elements: specifically, a small-size, calm, windless area in the centre and the atmospheric fronts. The same polar cyclone occurs (Fig. 2b) south-west of the Spitsbergen. Its size is 300 km, with wind speed 17–20 km/s.
Fig. 2 Radar images of the young polar cyclone: (a) the initial incipience phase in the Norwegian Sea close to the ice sheet, the near-sea surface wind inside it (a according to the SLR data) does not exceed 12 to 15 m/sec. Its size varies between 80–100 km. A spin-like structure of this cyclone and its constituent elements are pronounced: a small-size calm area in the centre and the atmospheric fronts; (b) the same cyclone is observed south-west of the Spitsbergen. Its size has grown up to 300 km and the wind speed is between 17 and 20 m/sec
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Fig. 3 Satellite images of occluded cyclone above the Okhotsk Sea. from spaceborne “Cosmos-1500” SLRAR: (a) radar image; (b) optical image of the visible range
Figure 3 shows both the SLRAR and optical images of the occluded cyclone above the Okhotsk Sea. In the optical image a cloud spiral is clearly discernible, which is originated from the small-size cloudless space at centre 1. The radar image illustrates the central zone 1, which 20 × 25 km area is shifted nearly 25 km northward (clearly seen by the south ward-inclined axis). According to estimates, zone 1 looks dark, the wind speed inside is less than 5 m/s. Dark patch 1 is surrounded by a brighter horseshoe-shaped cloud where the wind speed V ≅ 7–10 m/s. In the optical image this area is featured by the white cumulus clouds. Still further away from the centre the wind speed tends to decrease. In the radar image V ≅ 4 ÷ 5 m/s is seen in region 3. Part of this area as shown in the optical image is marked by stratus clouds 3 with a slightly elevated upper boundary interspersed with separate thicker cumulus-shaped elements. No disturbances occur in the sea surface wind field; even at a boundary of cloud-free area 4 and cloud cover 3 the values V remain unchanged. Tropical Cyclone “Diana” was formed on September 8, 1984 near Bahamas Islands, and soon was upgraded to a highly intense tropical storm (Mitnik and
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Fig. 4 Radar (right) and optical (left) images of the tropical cyclone “Diana” in its initial development phase from “Cosmos-1500” satellite SLRAR
Viktorov 1980). Figure 4 presents radar and optical images of tropical cyclone “Diana” in its initial stage. Among the features that are quite discernible in the SLRAR image is a vortex-like near-sea surface wind structure of the large-scale cyclone. In this development phase the cyclone-driven near-sea surface wind is 17–20 m/s. The cloud shape was not typical for tropical cyclones. The second radar surveying of the tropical cyclone “Diana” was made just at a time when it had reached its peak developed. The radar image shown in Fig. 4b was acquired on September 11, 1984 when the atmospheric pressure within the cyclone centre dropped to 952 gPa, whereas the maximum wind speed increased to 56 m/s. Radar images features of the “Diana” hurricane, dated September 11, showed a dark area of 13–14 km in diameter, typical hurricane “eye”. The wind speed decreased, gravitational-capillary waves reduced resulted in a reduced level of a sea surface-scattered radar signal. The outer diameter of the “eye” wall is about 30 km, similar to visible image. Analysis of Fig. 5c indicates that the “calm” zone in the hurricane “eye” is diminished. Two spiral-shaped chains of rain cells inside the cloud appear to be brighter than the surrounding background. The chains are located at 120 and 270 km from the tropical cyclone centre, with the width 10–12 km. The SLRAR images acquired 12 h later indicated that “Diana” hurricane has shifted to north-northeast by about 85 km with the speed 7 km/h (Utkin et al. 1986).
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Fig. 5 Satellite images of the tropical cyclone “Diana”: (a) visible image at 18 h 30 min September 11, 1984; (b) and (c) SLRAR images at 18 h 30 min September 11, 1984 and 8 h 30 min September 12, 1984 from “Cosmos-1500”
To determine the “Diana” hurricane parameters two near-sea surface wind field cross-sections were produced (to the east and to south of the “eye” centre). Data processing was performed according to the algorithms presented in (Kalmykov et al. 1989). Figure 6 shows the module of the “Diana” hurricane wind speed as a
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|V|, m/s
50 1 40 30 20
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Fig. 6 Dependences of wind speed module of the “Diana” hurricane from distance to its centre (SLRAR (1 – latitudinal and 2 – meridian crosscuts) and NOAA reconnaissance aircraft (3 – latitudinal and 4 – meridian crosscuts))
Fig. 7 Images of the eastern part of the Black Sea acquired by EOS “Sich-1” on March 17 (a) 20 (b), 1996 (07 h 33 min and 19 h 19 min GMT). March 17 (a) and (c) on March 20 (07 h 11 min GMT). Illustration of jet-like eastern wind from the Transcaucasian region, caused by the terrain
function of the distance to its centre based on the SLRAR and data provided by NOAA reconnaissance aircraft (Lawrence and Clark 1984). As seen the wind speed is reduced considerably moving from the hurricane centre.
Widespread Effects of Hazardous Atmospheric Processes in Coastal Areas and Locked-Land Seas Figure 7 shows SLRAR images of the eastern part of Black Sea acquired by EOS “Sich-1” on March 17 (07.33 GMT, (a) and (b) with 12 h interval) and 20 (07.11 GMT), 1996. All three show jet-like wind (“wind tunnel”-type) resulting from the impact of terrain. The wind jet extended up to 150 km and was around ~40–50 km wide. Figure 8 presents SLRAR image (acquired by the EOS “Sich-1” October 26, 1996) atmospheric cyclone in the eastern part of the Black Sea. The spatial scale of
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Fig. 8 An image of the cyclone in the eastern part of the Black Sea (acquired by the SLR EOS “SICH1”, orbit N6226). The near-sea surface wind speed shows a sharp increase from 2 to 3 m/s and may run up to more than 20 m/s at the boundary of the atmospheric front in its southern part, close to the coast of Turkey. White arrows – marked of the atmospheric fronts; black arrows – run-down winds
the developed vortex was well in excess of 250 km. The vortex structure is clearly visible, especially the most hazardous front in the southern part, close to the coast of Turkey. The near-sea surface wind speed at the boundary of this front can reach more than 20 m/s in a zone 5–10 km long. This particular phenomenon is thought of as highly dangerous to sea-going vessels.
Conclusion The 20-years experience of the Ukrainian spaceborne SLRARs application convincingly testifies that such systems (characterized by a wide swath can be effectively used to create the real satellite system for on-line warning and monitoring
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natural catastrophes. The group of 3–4 satellites equipped with SLRAR systems will be capable of providing real-time information about hazardous processes. This data will be updated between 2 and 3 h apart.
References Kalmykov A, Pichugin A, Tsymbal V (1989) Determination of the driving wind using the side looking radar system of the Cosmos-1500 satellite. Sov J Remote Sens 5:668–690 Kalmykov A, Velichko S, Tsymbal V (1993) Observations of the marine environment from spaceborne side-looking real aperture radars. Remote Sens Environ 45:193–208 Lawrence M, Clark G (1984) Atlantic hurricane season of 1984. Mon Weather Rev 113:1228–1237 Mitnik L, Viktorov S (1980) Radiolocation of the Earth Surface from the Space. Gidrometeoizdat, Leningrad (in Rusian) Utkin V, Shestopalov V, Kalmykov A et al (1986) Determination of the characteristics of tropical cyclones from spaceborne radar images. Dokl Akad Sci USSR 286:331–333 (in Russian)
Comparison of AVHRR-Based Global Data Records Felix Kogan, Marco Vargas, and Wei Guo
Abstract Several global data sets have been developed from the AVHRR instrument measuring reflectance/emission of the Earth since the early 1980s. The longest datasets currently available for users are NOAA’s Global Vegetation Health (GVH), NASA’s Global Inventory Modeling and Mapping Studies (GIMMS) and Land Long Term data Records (LTDR). The GVH has 30-year records (1981–2010), GIMMS – 26 (1981–2006) and LTDR – 19 (1981–1999). These datasets have different spatial and temporal resolutions, processing methods (sampling, calibration, noise removal, mapping, gap treatment etc.), applicability, availability, distribution etc. They have been used frequently for monitoring earth surface, atmosphere near the ground and analysis of climate related land surface trends. Since one of the common features of these datasets is the Normalized Difference Vegetation Index (NDVI) this paper is focusing on comparison of NDVI time series, specifically comparing time series dynamics and trends. It is shown that GIMMS NDVI is two to three times higher and has steeper long-term trend compared to GVH and LTDR. Keywords Vegetation health • 30-year 4-km data records • Vegetation condition index (VCI) • Temperature (TCI) and Vegetation health (VHI) indices • NDVI and BT
Introduction The Advanced Very High Resolution Radiometer (AVHRR) has been in space on NOAA operational polar-orbiting satellites for the past 30 years observing earth surface and atmosphere and will continue this endeavor in the future. The AVHRR F. Kogan (*) and M. Vargas NESDIS/NOAA, Center for Satellite Application and Research (STAR), Washington DC, USA e-mail:
[email protected] W. Guo IMSG Inc., Washington DC, USA
F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_30, © Springer Science+Business Media B.V. 2011
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data are extremely useful for monitoring weather and weather-related disasters, land ecosystems, agriculture, forestry, human health, invasive species, climate forcing and other human activities. In the recent 20 years, following such a wide spectrum of applications, AVHRR-based Normalized difference Vegetation Index (NDVI) was used for the analysis of climate trend and land surface changes (Zhou et al. 2001; Myneni et al. 1997; Nemani et al. 2003). That stimulated development of AVHRR-based historical records. Several global data sets were developed from AVHRR measurementss since the early 1980s. The most popular were NOAA’s Global Vegetation Index (GVI, since 1985 and GVI-2 since the early 1990s) and Global Vegetation Health (GVH since 2000), NASA’s Pathfinder (since the early 1990s), Global Inventory Modeling and Mapping Studies (GIMMS, from the late 1990s) and currently under development the Land Long Term data Records, LTDR (Tarpley et al. 1984; James and Kalluri 1994; Kidwell 1995; Tucker et al. 2005; LTDR 2010; GIMMS 2010; GVH 2010; Kogan et al. 2010). These data were focused (except GVI-2 and LTDR) on NDVI, ignoring infrared measurements, which are very useful for monitoring land, climate and socioeconomics (Kogan 1995). The available data have different spatial and temporal resolutions and processing methods (sampling, calibration, noise removal, mapping, gap treatment etc.), applicability, availability, distribution and others. Although the data were produced from the same original source they are often mismatch characterizing the same phenomena. However, NDVI from each dataset was available for the global community (GVH 2010; LTDR 2010; GIMMS 2010) and was used frequently, especially for analysis of climate related land surface trends (Zhou et al. 2001; Nemani et al. 2003; Myneni et al. 1997). Therefore, this paper is focused on comparison of NDVI time series and specifically comparing time series dynamics and trends.
Data Sets Description Global AVHRR data of the Global Area Coverage (GAC) format at 4 km special and daily temporal resolution have been archived from 1981 through present for all NOAA series polar-orbiting operational satellites (Kidwell 1995; Cracknell 1997). The observations for the entire period are available for four channels (Ch): visible (VIS, 0.58–0.68 mm, Ch1), near infrared (NIR, 0.725–1.1 mm, Ch2) and two infrared (IR, 10.3–11.3 mm, Ch4 and 11.5–12.5 mm, Ch5). The data are presented in 10-bit digital count values for each 4-km pixel between latitudes 75.024° N and 55.152° S and longitudes 180°W and 180° E. The GAC data were used in the development of all datasets. Three data sets GIMMS, LTDR and GVH were selected for further analysis. Below is a brief description of the data. GIMMS dataset provides NDVI only calculated from the VIS and NIR; satellites – NOAA-7, 9, 11, 14, 16 and 17; period – 1981–2006; resolution: 8 km (0.072°) special and 15-day maximum value composites (MVC) temporal; calibration – vicarious from Vermote and Kaufman (1995); corrections – volcanic stratospheric aerosol during 1982–1984 and 1991–1994, and satellite orbital drift using empirical
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mode decomposition/reconstruction (EMD) method; projection – Albers; web http://glcf.umiacs.umd.edu/data/gimms/. LTDR dataset: provides daily NDVI, daily surface reflectance (five channels), three angles and quality flag; satellites – NOAA-7, 9, 11 and 14; period – 1981– 1999; resolution – 5.5 km (0.05°), daily temporal; calibration – Vermote and Kaufman (1995); satellites NDVI product available from 1981 to 1999. web – http://ltdr.nascom.nasa.gov/ltdr/products2007.html. GVH dataset: provides – original measurements (VIS, NIR, IR4, IR5, NDVI, Brightness temperature (BT), three angles); no noise NDVI and BT, climatology of NDVI and BT, products (Vegetation (VCI), Temperature (TCI) condition indices and Vegetation health index (VHI), Fire risk index, Drought index; satellites – NOAA-7, 9, 11, 14, 16, 17, 18 and 19; period – 1981–2010; resolution – 4-km (0.036º) and 7-day MVC; calibration – vicarious from Rao and Chen (1999); corrections – volcanic stratospheric aerosol during 1982–1984 and 1991–1994, satellite orbital drift, difference between AVHRR-2 and AVHRR-3 instruments; difference in equator crossing time, high frequency noise; projection – Plate Carree (latitude–longitude); validation – in 27 countries; focus: – globe; web – http://www .star.nesdis.noaa.gov/smcd/emb/vci/VH/index.php.
Results and Discussion The currently available three datasets have the same start year (1981) for the time series but different end year (for GVH 2010; GIMMS 2006; and LTDR 1999). The comparison was done for the period 1982–1999, considering the earliest year (1999) of LTDR data end and that the 1981 data covered only the last 3 months of the year. Since the description of GIMMS and LTDR data processing is quite sketchy the comparison analysis does not explanation causes of the differences. Three regions in Asia, Africa and South America were selected (Table 1) to characterize all major ecosystems. Figure 1 shows 19-year average NDVI time series for the major areas indicated in Table 1 for the three continents Asia, South America and Africa. As seen, the GVH and LTDR time series are quite similar while the GIMMS data are very different having two to three times higher NDVI. Moreover, the amplitude of NDVI variation inside a year is also two times larger for GIMMS compared to two other data sets. These differences are consistent for the three continents. Although LTDR data are daily and GVH – weekly they match quite well though LTDR has slightly smaller NDVI and larger variation over time, which can be explained by GVH’s Table 1 Regions selected to average NDVI
Region Asia Africa South America
Coordinates (º) Latitude 40°N–60°N 15°N–30°S 15°N–25°S
Longitude 15°W–30°E 15°W–40°E 80°W–35°W
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y1 = 0.00088*x - 1.5
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Fig. 1 1982–1999 NDVI time series for GIMMS, LTDR and GVH
Fig. 2 1982–1999 slope trend (*10−3) for GIMMS, LTDR and GVH datasets
temporal data sampling with MVC procedure which gives preference to a larger NDVI compared to no sampling (daily values) for LTDR. Also, more pronounced seasonal cycle is observed in LTDR and GVH compared to GIMMS in South America. Evaluation of the three datasets for the trend existence during 1982–1999 indicates that all three have small mostly upward trend since the slope is positive, Fig. 2 shows slope change for the NDVI averaged for each degree latitude. As seen, the
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trend is changing for both latitude and for the area. The northern regions have slightly larger trend. Moreover, in sub Sahara Africa the trend for GIMMS and GVH is changing from positive above equator to slightly negative below the equator. From the three datasets, GVH shows the smallest trend since it is closer to zero line compared to the other two datasets.
Conclusion The three datasets produced from the same GAC data showed different NDVI values since they have different sampling, calibration and processing. GVH dataset is the longest, has the highest special resolution, also has all original measurements (VIS, NIR, IR4, IR5, NDVI, Brightness temperature (BT), three angles); no noise, climatology and what is the most important products (Vegetation (VCI), Temperature (TCI) condition indices and Vegetation health index (VHI)). All three datasets have small mostly upward trend; the GVH has the smallest slop. In Africa, the GVH and GIMMS data change slope from positive to negative while crossing the equator.
References Cracknell AP (1997) The advanced very high resolution radiometer. Taylor & Francis, USA, 534 p GIMMS (2010) http://glcf.umd.edu/data/gimms/ GVH (2010) http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_browse.php James ME, Kalluri SN (1994) The Pathfinder AVHRR land data set: an improved coarse resolution data set for terrestrial monitoring. Int J Remote Sensing 15:3347–3363 Kidwell KB (ed) (1995) NOAA polar orbiter data users guide. National Oceanic and Atmospheric Administration, National Environmental Satellite Data and Information Services, National Climatic Data Center, Camp Springs MD Kogan FN (1995) Droughts of the late 1980s in the United States as derived from NOAA polar orbiting satellite data. Bull Am Meteorol Soc 76:655–668 Kogan F, Guo W, Jelenak A (2010) Global vegetation health: long-term data records. In: Kogan F, Powell A, Fedorov O (eds) Use satellite and in situ data to improve sustainability. Springer, New York (in this book) LTDR (2010) http://ltdr.nascom.nasa.gov/cgi-bin/ltdr/ltdrPage.cgi Myneni RB et al (1997) Increased plant growth in the northern high latitudes from 1981–1991. Nature 386:698–702 Nemani RR, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ, Myneni RB, Running SW (2003) Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300(5625):1560–1563 Rao CRN, Chen J (1999) Revised post-launch calibration of the visible and near-infrared channels of the advanced very high resolution radiometer on the NOAA-14 spacecraft. Int J Remote Sensing 20:3485 Tarpley JP, Schneider SR, Money RL (1984) Global vegetation index from NOAA-7 meteorological satellite. J Climate Appl Meteorol 23:491–494
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Tucker CJ, Pinzon JE, Brown ME, Slayback D, Pak EW, Mahoney R, Vermote E, El Saleous N (2005) An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sensing 26(20):4485–5598 Vermote EF, Kaufman YJ (1995) Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views. Int J Remote Sensing 16(13):2317–2340 Zhou L, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB (2001) Variation in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J Geophys Res D17:20069–20083
Merging Remote Sensing and In Situ Data for Estimation of Energy Balance Components Under Climate Change Conditions: Ukrainian Steppe Zone Tatiana Ilienko and Elena Vlasova
Abstract Climate change is recognized as one of the most important natural events in the past decades. Energy balance components (surface temperature, evapotranspiration, etc.) are closely related with climate change. Therefore adaptation of the existent models is needed for future climate. A shortage of surface weather observations due to the reduction of permanent weather networks requires combining remote sensing and in situ observations. This work is devoted to merging of these two data types in estimation of energy balance components of the Ukrainian steppe zone under climate warming condition. Keywords Climate change • Energy balance • Evapotranspiration • Remote sensing data
Introduction Climate change is recognized as one of the most important natural events in the past decades. The Intergovernmental Panel on Climate Change (IPCC) concluded that the global surface temperature increased 0.74 ± 0.18°C during the last century. A warmer climate will affect both environmental and sustainable development of the world. The climate system reacts by adjusting the earth’s energy balance to a new equilibrium. These processes include a release of latent heat through increase of evaporation, plant transpiration and precipitation accelerating hydrologic cycle. Regarding water resources, the difference between precipitation and evapotranspiration determines the amount of water available for runoff and groundwater recharge. T. Ilienko () Agroecological Institute, Kyiv, Ukraine e-mail:
[email protected] E. Vlasova Institute for Hydraulic Engineering and Land Reclamation Ukrainian Academy of Sciences, Kyiv, Ukraine
F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_31, © Springer Science+Business Media B.V. 2011
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Precipitation changes will be critical, but evapotranspiration which is controlled by changes in other climate variables such as temperature, humidity, radiation and wind speed will also play a major role. Evapotranspiration being a key component of the energy and water balance, plays also important role in the water cycle of irrigated lands of Ukraine. Scientific community has formulated the demands for data frequency, update and accuracy (Sellers 1993). One of the important requirements is to validate energy balance components’ calculation. Shortage of surface weather observation due to a reduction of permanent weather network requires combining remote sensing and in situ observations. Unlike point weather station observations, satellite sensors provide spatial information on energy balance components. Satellite remote sensing is able to provide the required frequency of data update with high accuracy (Li and Garand 1994). Therefore it is expedient to merge both in situ and remote sensing data. Over the last two decades a considerable number of research has been undertaken to determinate actual evapotranspiration, regional distribution of energy balance components over heterogeneous land surfaces using a combination of satellite and in situ data (Kustas et al. 2003; Roerink et al. 2000). The aim of this research is to estimate energy balance components under warming climate conditions by merging in situ and satellite data. The study of evapotranspiration as an element of water balance will give basic knowledge of water consumption of vegetation, water supply and demand as well as water shortage.
Data and Methods Meteorological data were collected for the steppe zone of Ukraine in the Zaporozhsky region (oblast), Kamenko-Dneprovsky district during 1939–2004. Analysis of data in Fig. 1 shows a decrease in total annual precipitation (Fig. 1a) and an increase in average temperature (Fig. 1b). Satellite data of the cloud-free Landsat images of Zaporozhsky region (August 21, 2000 and July 17, 2001) have been used for the study. Mathematical and statistical analysis was used for data processing. FAO techniques of evapotranspiration calculation (Allen et al. 1998) and Surface Energy Balance Algorithm (SEBAL) with modification (Bastiaanssen et al. 1998, Roerink et al. 2000) were also used in this research. Satellite data processing included image acquisition and preprocessing (mapping, radiometric and geometrical correction, data conversion to specified format, assessment of cloud conditions), thematic image processing: hybrid classification for crop identification; specification of field boundaries, evapotranspiration modeling, energy balance components calculation. Multispectral satellite images and in situ data were used for evapotranspiration calculation (Ilienko 2009). Energy balance components were calculated by radiation balance method using space images. The relationship between radiation and energy balance was approximated by the Eqs. (1) and (2) and is shown in Fig. 2.
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Annual precipitation amount (1939-1969) 650
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Fig. 1 Meteorological data for Zaporozhsky oblast (1939–1969): (a) annual total precipitation and trend; (b) average monthly temperature and precipitation amount
R n = K ↓ −K ↑ + L ↓ −L ↑,
(1)
R n = G 0 + H + λ E,
(2)
where Rn – net radiation, K↓, K↑ – short wave radiation, L↓, L↑ – long wave radiation, G0 – soil heat flux, Н – sensible heat flux, lЕ – latent heat flux. Net radiation and radiation balance components (1) is derived from satellite image surface reflectance (G0), surface temperature (Т0), incoming solar radiation (К↓) and long wave incoming radiation (L↓) by the Eq. (3) written as
R n = (1 − ρ0 ) τ K ↓ −σε T0 4 + L ↓,
(3)
where t is atmosphere transmissivity coefficient, s – Stephan–Boltzmann constant,e0 – surface emissivity coefficient. Energy balance components (2) are derived from radiation balance components because G0 = f1(Rn,r0, T0NDVI), H = f2(Rn, G0, L), lЕ = f3(Rn, G0, L), L = (TH – T0)/
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Input data Albedo – ρ0
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Vegetation index – NDVI
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Rn Energy balance soil heat flux
G0
sensible heat flux
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evaporative latent heat fraction flux
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Actual evapotranspiration
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Fig. 2 Relation between radiation, energy and evapotranspiration
(TH – TlE), where TH, TlE are boundary temperatures which can be distinguished in the reflectance–temperature relationship: for wet and dry surface conditions (Roerink et al. 2000). The flow chart of the energy balance components calculation is shown in Fig. 3. Daily net radiation Rn,24 and daily potential evapotranspiration ЕТр,24 = 0.408(Rn – G0) are calculated (G0 = 0 daily) using the Penman–Monteith method (Allen et al. 1998). Daily evapotranspiration ЕТа,24 = LЕТр,24 was derived from the energy balance components.
Study Area The investigated Kamenko-Dneprovsky district of Zaporozhsky is located in Ukrainian steppe zone and is characterized by a shortage precipitation, their irregular distribution through the year, high temperature and low air humidity. The highest temperature and the lowest precipitation amount are in August when the main crops are maturing. The main activity in the region is agriculture and some crops
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Fig. 3 Flow-chart of energy balance components calculation
are irrigated. There are five irrigation systems which irrigate about 27,460 ha of land. Stepovy farm was selected as pilot territory because its environment is typical for the entire region.
Results and Discussion Landsat ETM 7 images (August 21, 2000 and July 16, 2001) of the entire region and farm Stepovy are shown in Fig. 4. Hybrid classification for crop identification; specification of field boundaries is shown in Fig. 5. Using regression analysis the expression for surface albedo (r0) through planetary albedo (rp) was obtained:
( = (ρ
( ) − 0.048) ⁄ 0.516 (R
)
ρ 0 = ρp + 0.0746 ⁄ 0.432 R 2 = 0.905, August 2000 ,
ρ0
2
p
)
= 0.9425, July 2001 .
Albedo for different surface types is presented in Table 1.
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Fig. 4 Landsat ETM 7 images of the entire region (left) and farm Stepovoy (right) for August 21, 2000 and July 16, 2001 Land use maps (Stepovy farm)
a
N W
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corn_silo fallow pears soybean sunflower corn_beans spring barley winter barley winter wheat alfalfa water 0
3
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water pease corn spring barley winter wheat fallow sainfoin sunflower silo corn soybean perennial herbs
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Fig. 5 Hybrid crop classification, farm Stepovy: (a) August 2000, (b) July 2001
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Table 1 Albedo and evapotranspiration for different surfaces August 21, 2000 July 16, 2001 Actual daily Actual daily evapotranspi evapotranspi Relative daily evapotranspi Surface ration Surface ration ration albedo (mm/day) Surface type albedo (mm/day) Water 0.071 10.35 0.92 0.099 10.5 Fallow 0.185 3.06 0.51 0.189 1.18 Winter wheat 0.194 1.03 0.11 0.258 3.73 Corn 0.22 4.3 0.43 0.198 7.08 Sunflower 0.23 2.9 0.2 0.21 7.32 Alfalfa 0.209 2.6 0.19 0.163 2.07
a
Relative daily evapotranspi ration 0.91 0.12 0.46 0.73 0.79 0.24
Daily net radiation Rn
−2 −1
MJm d
36.7- 47.78 47.79- 49.99 50- 51.57 51.58- 52.94 52.95- 54.42 54.43- 57.9 57.91- 61.59 61.6- 63.59
b −2 −1
MJm d
42.63-50.65 50.66- 53.01 53.02-54.66 54.67- 56.07 56.08- 57.25 57.26-62.44 62.45-68.57 68.58-102.8
Fig. 6 Daily net radiation: (a) August 21, 2000, (b) July 16, 2001
The relationship between boundary temperatures TH and TlE and surface albedo was obtained using satellite data: TH = 325 − 50 ρ0 , Tλ E = 291.47 + 41.17 ρ0 (August 2000 ) and TH = 321.77 − 26.06 ρ0 , Tλ E = 294.21 + 17.87ρ0 (July 2001). Energy balance components were derived using the above mentioned method and input parameters calculated from satellite data. Figures 6–9 present energy
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Instantaneous soil heat flux G0
a
-2 -1
Jm s
3.25 - 4.72 4.73 - 5.4 5.41 - 5.96 5.97 - 6.55 6.56 - 7.09 7.1 - 7.63 7.64 - 8.2 8.21 - 9.01
b -2 -1
Jm s 4.41 - 5
5.01 - 5.59 5.6 - 6.19 6.2 - 6.99 7 - 7.58 7.59 - 8.18 8.19 - 8.78 8.79 - 9.78
Fig. 7 Instantaneous soil heat flux: (a) August 21, 2000, (b) July 16, 2001 Instantaneous sensible heat flux H
a -2 -1
Jm s
2.928-6.209 6.21-12.77 12.78-19.34 19.35-22.62 22.63-25.9 25.91-29.19 29.2-32.47 32.48-42.31
b -2 -1
Jm s
3.645-7.728 7.729-12.15 12.16-16.23 16.24-19.98 19.99-23.38 23.39-27.12 27.13-30.87 30.88-34.27 34.28-81.57
Fig. 8 Instantaneous sensible heat flux: (a) August 21, 2000, (b) July 16, 2001
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Instantaneous latent heat flux lE -2 -1
Jm s
8.64 - 16.5 16.6 - 21.8 21.9 - 27 27.1 - 32.3 32.4 - 37.5 37.6 - 48 48.1 - 58.6 58.7 - 69.1
b -2 -1
Jm s
6.85 - 11.4 11.5 - 16.4 16.5 - 21.5 21.6 - 26.1 26.2 - 30.7 30.8 - 36.1 36.2 - 42.4 42.5 - 49.5 49.6 - 58.2
Fig. 9 Instantaneous latent heat flux: (a) August 21, 2000, (b) July 16, 2001
balance components: net radiation Rn, instantaneous soil heat flux G0, sensible heat flux H and latent heat flux lE, respectively. Daily net radiation Rn,24, daily potential evapotranspiration ЕТр,24 and daily actual evapotranspiration ЕТа,24 were also calculated. Relative evapotranspiration ЕТrel was determined as ЕTa,24/ЕТр,24. The results are presented in Table 1. The resulting and maps of daily actual (ЕТа,24) and relative (ETrel) evapotranspiraration for August 21, 2000 and July 16, 2001 are shown in Figs. 10 and 11.
Conclusions This research was based on mathematical and statistical analysis, satellite data processing and energy balance component calculations. For estimation of energy balance satellite and in situ data were merged with GIS techniques. The method was adapted to the steppe zone of Ukraine (Kamenko-Dneprovsky district, Zaporozhsky oblast). For pilot territory components of energy balance, actual and relative evapotranspiration for different crops were calculated and mapped.
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Actual daily evapotranspiration ETa,24
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mm/d 0.7562 - 2.377 2.378 - 3.673 3.674 - 4.808 4.809 - 5.942 5.943 - 7.401 7.402 - 9.508 9.509 - 12.75 12.76 - 15.99 16 - 18.42 18.43 - 23.93
0
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b
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mm/d 0.1047 - 1.352 1.353 - 2.599 2.6 - 3.846 3.847 - 4.47 4.471- 5.094 5.095 - 6.341 6.342 - 7.588 7.589 - 9.459 9.46 - 10.71 10.72 - 11.95
0
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Fig. 10 The resulting maps of actual daily evapotranspiration: (a) August 21, 2000, (b) July 16, 2001 Relative daily evapotranspiration ETrel
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0
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0.08 - 0.2 0.21 - 0.29 0.3 - 0.4 0.41 - 0.49 0.5 - 0.58 0.59 - 0.67 0.68 - 0.76 0.77 - 0.88 0.89 - 1.12 1.13 - 1.85
Kilometers
24
b
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0.01 - 0.14 0.15 - 0.22 0.23 - 0.3 0.31 - 0.4 0.41 - 0.47 0.48 - 0.55 0.56 - 0.63 0.64 - 0.7 0.71 - 0.83 0.84 - 1.01
0
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Fig. 11 The resulting maps of relative daily evapotranspiration: (a) August 21, 2000, (b) July 16, 2001
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References Allen RG (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56, Rome, Italy Bastiaanssen WGM, Menenti A et al. (1998) A remote sensing surface energy balance algorithm for land (SEBAL), part 1: Formulation. J Hydrol 212–213:198–213 Ilienko TV (2009) Using satellite and in situ data for evapotranspiration assessment. Ukrainian steepe zone case. J Agroecol June:122–126, Special edition. Kyiv Kustas WP, Diak GR, Moran MS (2003) Evapotranspiration. Remote sensing of earth. Encyclopedia of water science. Marcel Dekker, New York, pp 267–274 Li ZQ, Garand L (1994) Estimation of surface albedo from space – a parameterization for global application. J Geophys Res 99:8335–8350 Roerink GJ, Su Z et al. (2000) S-SEBI: a simple remote sensing algorithm to estimate the surface energy balance. Phys Chem Earth B 25(2), 147–157 Sellers PJ (1993) Remote sensing of the land surface for studies of global change, NASA/GSFC International Satellite Land Surface Climatology Project Report, Columbia, MD
Atmosphere Aerosol Properties Measured with AERONET/PHOTONS Sun-Photometer over Kyiv During 2008–2009 Vassyl Danylevsky, Vassyl Ivchenko, Gennadi Milinevsky, Michail Sosonkin, Philippe Goloub, Zhengqiang Li, and Oleg Dubovik
Abstract The PHOTONS network, as a part of the AERONET ground network for aerosol remote sensing of Earth’s atmosphere, covers more than 40 sites in Europe, Africa and Asia, providing sun-photometer measurements, calibration and data processing. Within the framework of scientific cooperation between the Lille 1 and the National Taras Shevchenko Kyiv Universities, the CIMEL CE 318-2 sun-photometer has been operated at Kyiv from the end of March, 2008. This article describes the AERONET/PHOTONS measuring equipment, procedure, data processing and the preliminary analysis of columnar aerosol properties retrieved during April 2008–March 2009. Spectral aerosol optical thickness (AOT), Angström parameter and precipitable water vapor thickness were measured and analysed. Keywords Aerosol remote sensing • AERONET/PHOTONS network • Aerosol optical thickness • Angström parameter • Precipitable water vapor
Introduction In recent years, scientific community, governments and non-government organizations are giving much attention to research of the atmospheric aerosols content, dynamic and physical properties since it is one of the air pollutants that can be potentially hazard for biosphere and also contributor to global climate change (Penner et al. 2001; Forster et al. 2007). Present increase in the amount of aerosols V. Danylevsky (*), V. Ivchenko, and G. Milinevsky National Taras Shevchenko University of Kyiv, Kyiv, Ukraine e-mail:
[email protected] M. Sosonkin Main Astronomical Observatory of National Academy of Science of Ukraine, Kyiv, Ukraine P. Goloub, Z. Li and O. Dubovik Université de Lille, France F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_32, © Springer Science+Business Media B.V. 2011
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in the atmosphere creates negative radiative forcing counteracting to global warming (Forster et al. 2007). Key parameters for determining both direct and indirect radiative forcing are: (1) the aerosol optical properties, which vary as a function of a wavelength and relative humidity, (2) the atmospheric loading and geographical distribution of the aerosols, which vary as a function of time, and (3) the aerosol particles sizes, shapes and chemical compositions. Lack of aerosol temporal and spatial data and insufficient accuracy of aerosol properties determination create some problem for accurate estimation of aerosol radiative forcing (Penner et al. 2001; Forster et al. 2007; Kokhanovsky 2008; Dubovik et al. 2002). It is important to separate radiative forcing created by anthropogenic aerosol contribution from radiative forcing created by the natural aerosol. The atmosphere aerosol particle properties are usually estimated by an inverse problem solution (King et al. 1999; Dubovik et al. 2002; Kokhanovsky 2008). The Earth atmosphere-surface system is characterized by great number of parameters which have to be retrieved simultaneously. The best results are obtained by joint analysis data of space-borne and ground-based remote sensing. In order to monitor aerosol properties and dynamics at regional and global scales, a network of ground-based sites, equipped with standardized measuring devises was set up. Ground-based network for passive aerosol measurements is the AERONET (AERosol Optical NETwork, http://aeronet.gsfc.nasa.gov/)–established in early 1990 by NASA and Laboratoire d’Optique Atmosphérique (LOA) University Lille 1, the Centre National d’Etudes Spatiales (CNES) and Centre National de la Recherche Scientifique (CNRS) of France (Holben et al. 1998). The AERONET consists of hundreds of automatic sun-photometers. The PHOTONS (PHOtométrie pour le Traitement Opérationnel de Normalisation Satellitaire, http:// loaphotons.univ-lille1.fr) is French subdivision of the AERONET, operates about 45 observational sites: about 30 in Europe (France), 10 in Africa and 5 in Asia. They provide sun-photometer measurements, calibration and data processing. But AERONET/PHOTONS sites distributed unevenly, especially in East Europe (in Ukraine particularly). At the end of 2007, following scientific cooperation between LOA, Lille 1 (France) and National Taras Shevchenko (Kyiv, Ukraine) universities, the AERONET/PHOTONS site was set up. This article describes equipment, data reduction procedures and the preliminary analysis of columnar aerosol properties retrieved from Kyiv site.
Instrument and Data Currently, automatic sun photometers (spectral radiometers) CIMEL CE-318 (http://www.cimel.fr/photo/sunph_us.htm) are used by the AERONET/PHOTONS as the main instrument (Holben et al. 1998). The CE-318 sun tracking photometers have been designed and realized to be a very accurate motorized, portable, autonomous (powered by solar battery) and automatic instruments. The most
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currently widespread models of CIMEL sun-photometers over AERONET/ PHOTONS sites are standard СЕ 318-1 and polarized model СЕ 318-2. In order to derive total column aerosols properties, water vapor and ozone, these photometers measure solar irradiance, sky radiance (aureole brightness), polarization (if polarized model is used), along the almucantar and vertical of the Sun with certain angle intervals. The photometers have two optical channels with two collimators and independent detectors of different sensitivities to measure bright direct sun irradiance and dark sky radiance in the standard wavebands. The photometers equipped with sets of optical filters, which wavelengths were selected to avoid strong influence of gaseous constituents of atmospheric extinction and to take into consideration water vapor absorption. The CE 318-1 model is equipped with filters for wavelengths of 340, 380, 440, 675, 870, 940, 1020 nm, and the CE 318-2 model for wavelengths 440, 675, 870 (2 polarization analyzers added), 940, 1020 nm. Spectral bandwidth of each channel is equal to 10 nm at half maximum. The 940 nm channel is used to determine the water vapor amount in atmosphere column because water vapor has a maximum of absorption at this wavelength. The data are transferred from sun-photometer to the AERONET/PHOTONS data base in two ways: via the special data collection systems on a geostationary satellite, or via Internet. Description of the sunphotometers characteristics is provided by http://www.cimel.fr, http://aeronet. gsfc.nasa.gov/new_web. The polarized CE 318-2 sun-photometer model has been installed at Kyiv site. The pre-programmed microprocessor control measurement procedure provides several scenarios depending on Sun position on celestial sphere, season and time of the day (Holben et al. 1998). The data obtained from observations are used both for aerosol optical thickness (AOT), water vapor content measurements and for the instrument self-calibration. Following Holben et al. (1998) and Li et al. (2008), calibration techniques is used to convert the instrument outputs to AOT and radiance (W/m2 sr mm). Two types of calibration procedures are used: direct-Sun irradiance and diffuse-sky radiance. Also different techniques are used to calibrate reference (master) and field instruments. Sun-channels of reference sun-photometers are usually calibrated at special high-altitude sites with clear stable atmosphere conditions by the Langley plot method, which uses the Sun as a reference light source. Field instruments are generally calibrated by comparison with the master instrument at low-altitude calibration sites (e.g., Goddard Space Flight Center, USA, and Carpentras, France). Sky-radiance channels are calibrated in the laboratory by using an integrating sphere or a “vicarious” calibration method (Li et al. 2008). The errors is less than 2% for solar channels and less than 5% for sky-radiance channels. These values correspond to the total uncertainty in AOT from a newly calibrated field instruments under cloud-free conditions typically not more than 0.01 for l ³ 440 nm. The aerosol optical depth is computed for three data quality levels: level 1.0 – unscreened data, level 1.5 – screened for cloud contamination, and level 2.0 – cloud-screened and quality-assured data. Level 2.0 data are also corrected after photometer’s yearly recalibration.
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Method For the most reliable Level 2.0 data the following parameters are determined: spectral AOT, Angström exponent and water vapor content (thickness of precipitation water layer) in atmosphere column over observational site (Holben et al. 1998, 2001). AOT is proportional to the number of aerosol particles in sun-photometr’s field of view and is a proper measure of aerosol content in atmosphere over the observational site. Spectral AOT approximation is based on the Beer–Lambert–Bouguer law: E (λ ) = E0 (λ ) · exp (−τ (λ )) where E(l) is spectral solar irradiance measured with the calibrated sun-photometer at the time of observations, E0(l) is solar irradiance at the top of the atmosphere, computed using the sun-photometer calibration coefficients, and t(l) is atmosphere optical thickness in the direction of the Sun, it is computed from the equation above. To obtain AOT the optical thickness due to water vapor, Rayleigh scattering and trace gases (O3, CO2, NO2 etc.) must be subtracted from t(l): AOT (λ ) = τ (λ ) − τ (λ )water − τ (λ )Rayleigh − τ (λ )CO − 2
Water vapor content is a very important factor for deriving AOT. The total column water vapor is derived from three spectral channels: 675, 870 and 940 nm. Firstly atmosphere optical thickness is computed for 675 and 870 nm using Rayleigh optical thickness and AOT only. Then the atmosphere optical thickness for 940 nm is computed extrapolating the data obtained above. Hence, the water vapor optical thickness tW for 940 nm is found using measured and extrapolated data:
(
)
ln (τ W ) = ln (τ 940 measured )− ln τ 940 extrapolated . The total thickness TW of the precipitable water layer in atmosphere column is determined using equation: 1
− ln (τ W ) b a TW = mW where a and b are filter-dependent constants, and mW is water vapor optical air mass. Angström parameter a is power exponent in equation that is used for calculation of AOT dependence on light wavelength: AOT (λ ) = B · λ − α where B is AOT at l = 1 mm. The parameter a is calculated from data measured at two or more wavelengths, using a least squares fit, as a=
d ln (AOT (λ )) d ln (λ )
.
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AOT obtained for 440 and 870 nm is used for a calculations, as a rule. Angström parameter determined in this way is the simplest qualitative indicator of aerosol particle size averaged on atmosphere column over observational site because aerosol particles optical properties and, as a consequence, spectral extinction coefficient of aerosol depend on the ratio 2p · a/l, where a is the characteristic size of the particle. The coefficient a increases when the particles’ size decreases. Studies of optic atmosphere properties show the Angström parameter change range from −0.1 (coarse particles with a ~ 1–10 mm) to 2.5 (fine aerosol fraction with a ~ 0.01–0.1 mm). Representative value of a for inland aerosol of various sources is about 1.3, but its peak value for practically molecular atmosphere (with a 0.6 MeV, Ee > 2 MeV) and protons (Ep > 1 MeV) fluxes are noted in an external radiation belt and in most cases on L > 4RE during intensive magnetic storms. Their spectra and fluxes are measured on low-altitude and geostationary satellites LANL, GOES-8, 10 (L = 6.6 RE) (Reeves et al. 2003; Kanekal et al. 1999). Magnetosphere magnetic field model calculations are extremely necessary at identification of precipitating energetic particles areas borders in the ionosphere, auroral electrojets locations and establishment of criteria of their correct identification with of sources borders in corresponding magnetosphere plasma domains (Feldstein et al. 2006; Newell et al. 2004). In this paper the global distribution of model (Tsyganenko’s Т01) magnetic fields on distance 0.6 MeV and Ee > 2 MeV and protons Ep > 1 MeV and Ep > 10 MeV) during magnetic storms their daily data of geostationary satellites GOES 08, 10 (www.swpc.noaa.gov/Data/goes.html) were used. Model calculations were carried out using empirical data-based Tsyganenko’s model Т01 for the inner magnetosphere magnetic field. Input parameters of storm magnetic field model include the geodipole inclination angle, Ву, Bz IMF components and solar wind dynamic pressure, Dst-index. Also in this model changes IMF and solar wind velocity for the previous period of the calculations moment are considered by introduction of two functions g1 and g2, depending on Bz and By IMF and solar wind velocity. As a result the total magnetosphere disturbances field is represented as the sum of the basic current sources fields: B = BCF + BRC + BTC + BFAC1 + BFAC 2 + Binf , where BCF – magnetopause currents field, BRC – ring current field including separately the symmetric BSRC and the partial BPRC ring currents fields, BTC – cross-tail currents field, field-aligned currents of areas 1 BFAC1 and 2 BFAC2 fields. Last member Binf represents an interaction field between the geomagnetic field and IMF.
eomagnetic Variations During April 6–7, 2000 G Magnetic Storm The April 6–7, 2000 magnetic storm was the second strongest in the year 2000 if quantified by the peak of the Dst-index. It has arisen after coronal mass ejection (CME) on April 4, 2000 observed near to the western Sun limb. The front of shock wave CME has reached the Earth’s magnetosphere on April 6. The magnetic cloud has touched magnetosphere only by the flank in recovery phase. After analyzes this sequence of events using observations of several spacecraft in the solar wind and at geostationary orbit as well as recordings from more than 80 magnetometer stations at latitudes higher than 40°N, it was determined that such intensive storm (Dst = −320 nT, Kp = 9) was caused by the very large solar wind magnetic pressure, which compressed the dayside magnetopause inside geostationary orbit for a period of more than 6 h (Huttunen et al. 2002). Changes IMF and SW parameters and also the geomagnetic field on low-latitude (ABG) and mid-latitude (AIA) observatories are presented on Fig. 1. The storm sudden commencement SC has been registered by ground observatories at 16:40 on April 6. Before SC, about 16:20, the solar wind velocity V has increased by 200 km/s, the magnitude B IMF has grown from 5 to 28 nT, By from −7 nT to −27 nT, component Bz from positive became negative (−10 nT). Such changes also have served as the reason of the ring current development on the Earth almost right after SC. The solar wind dynamical pressure Psw finds out two accurate maxima: at 23:00–24:00 on April 6 (I) and at 02:00–04:00 on
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Fig. 1 Variation of interplanetary magnetic field components (Bz, By), dynamic pressure (Psw) and solar wind velocity (V) according to satellites WIND measurements; variations geomagnetic field horizontal components (dH) based on INTERMAGNET observatories and the index of intensity magnetic field H-components of symmetric ring current SYM based on World Data Centre for Geomagnetism, Kyoto
April 7 (II). The first of them was observed at negative Bz and accurately showed in the form of the substorm in the American sector (in AIA +700 nT). In the maximum II at 02:00–04:00 such effect is not shown, because Bz is positive. The substorm is observed at the entire Australian-Asian sector (in ABG +60 nT). Difference
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between substorms at 23:00–24:00 and at 02:00–04:00 is caused by change Bz IMF which from negative became positive at 24:00UT. The first substorm proceeds in a peak of the storm main phase; the second is appeared on the recovery phase. This storm has not the initial phase and the main phase begins right after SC and then, after 6 h, the recovery phase begins. During this storm several cases short-term (less than 8 mines) huge induced currents (to 34А) in high-latitude regions were fixed (Huttunen et al. 2002).
hanges in Relativistic Particles Fluxes C at Geosynchronous Orbit The analysis of the relativistic protons changes and electron fluxes on L = 6.6 RE in a storm vicinity on an example of magnetic storms with the sudden commencement, caused by a shock wave from the rapid propagation of the СМЕ is made. The intensification of protons fluxes 1 day before the storm, (April 4, 2000), is well visible on schedules of variations of relativistic particles daily fluxes (Fig. 2). The proton fluxes with Ep > 10 MeV was more in 6.6 times in comparison with its value (3.7 × 105 pr/sm2-day-sr) in day of the storm main phase 6.04.2000. For protons concerning low energy (Ep > MeV) the flux reduction in 1.2 times to 5.9 × 107 pr/sm2-day-sr 6.04.2000 is noticed also. We will especially pay attention to particles
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Fig. 2 Changes of the relativistic particles daily fluxes registered on geostationary satellites GOES-8, GOES-10 (http://www.ngdc.noaaa.gov), during 2 months near to magnetic storms: – for a moderate storm on May 15, 1997 (Fig. 2a) and for the strong storm on April 6–7, 2000 (Fig. 2b). On the top panels variations of daily proton flux with energy Ep > 1 MeV (an empty circle, an axis at the left) and Ep > 10 MeV, and on bottom – the flux of relativistic electron with energy Ee > 0.6 MeV (an empty circle, an axis at the left) and Ee > 2 MeV are shown
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flux recession for next days after the storm main phase, in a recovery phase 7.04.2000: almost 10 times for protons with Ep > 1 MeV and in 25 times for electron with energy Ee > 2 MeV. It is interesting that after storm flux of a electron (Ee > 2 MeV) increases with peak 7.4 · 107 el/sm2-day-sr in 4 days after the storm main phase and flux of electron with low energy (Ee > 0.6 MeV) is appeared for the third day April 9, 2000 to 2.7 × 1010 el/sm2-day-sr. It is in 13 times larger for the day of April 6, 2000 of the main phase. The total flux of protons with Ep > 1 MeV has decreased by 50 times and with Ep > 10 MeV by 44.8 times while similar electron flux with Ee > 0.6 MeV and Ee > 2 MeV have grown by three and seven times, accordingly. The more energetic relativistic protons fluxes did not change almost after a storm compare to its level before the storm. On the other hand, on a day of the storm main phase (April 6, 2000) the increase in daily fluxes of protons with Ep > 1 MeV and Ep > 10 MeV are 40 and 6.6 times, accordingly (mainly in low energy range) in comparison with their values after a storm was observed. Daily electron fluxes with Ee > 0.6 MeV and Ee > 2 MeV have on the contrary decreased by 50 and 100 times, accordingly. Thus, it has been revealed by geostationary orbit that the strong magnetic storm was accompanied by loss of electron and increase in the protons fluxes in the storm main phase and the general recession of relativistic particles fluxes in the storm recovery phase when vertical component Bz IMF has changed the direction for the northern. However, through 3–4 days after the storm main phase the electrons fluxes not only has exceeded reference values, but also there were additional energetic particles, is possible owing to non-adiabatic acceleration mechanism (Reeves et al. 2003). For comparison, the dynamics of relativistic particles on May 1997 has been analysed (Fig. 2а), when the moderate magnetic storm on May 15, 1997 (SC at 02:00UT; Dst = −115 nT at 12:40 UT) was observed. The beginning of its main phase has coincided with the arrival of long (9:06–01:00 UT) magnetic cloud (http://lepmfi. gsfc.nasa.gov/mfi/mag_cloud_pub1.html). The electron fluxes increase in the storm recovery phase with a maximum (1.1 × 108el/sm2-day-sr) for 5 days after the magnetic cloud, 20.05.1997, is well visible as well as the enough long interval (11–15.05.1997) its minimum level before a storm. The protons fluxes with Ep > 10 MeV strongly increases 3 days prior to the storm during solar flare. We pay attention that growth of protons fluxes (Ep > 1 MeV) was observed in day of the storm main phase, 5.15.1997 (to 9.6 × 105 pr/sm2-day-sr) and for 7 days after a storm, 22.05.1997 (1.3 × 105 pr/sm2-day-sr).
patial Distribution of Modelled Magnetic Fields in Inner S Magnetosphere (
