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Participating programs

Examples of Global to Regional Agricultural Monitoring Systems

 

The UNFAO Global Information and Early Warning System (GIEWS)

The GIEWS was established in 1975 to monitor food supply and demand at the global scale and to provide early warning of serious regional food shortages. Information from GIEWS is used to identify impending food security crises so that the UN World Food Programme and other international and national agencies can develop country-specific needs assessments (Figure X.). GIEWS integrates satellite-derived information on land cover and land use with in situ data on agricultural statistics, livestock, agricultural markets, and weather. GIEWS monitoring is designed to enable direction of ground-based sampling to validate crop production estimates and development of quick, early, partial indemnity for immediate action.

Figure: GIEWS map of countries in need of food aid in October 2007

 

The USDA Foreign Agricultural Service (FAS)

The goal of the Office of Global Analysis (OGA) of FAS, specifically within the International Productions Assessment Branch, is to produce reliable, objective, timely, transparent, accurate data on global agricultural production. FAS monitors world agricultural production and world supply and demand for agricultural products to provide baseline market information and information for US domestic early warning. FAS analyses rely upon a combination of meteorological data, field reports and satellite observations at moderate and high spatial resolutions to aid in crop and growth stage identification and yield analysis. These data are used to confirm or deny unsubstantiated information about forecast crop yields and to identify unreported events likely to impact crop yields. To bring these disparate sources of data together, FAS has developed the Crop Explorer, a GIS-based decision support system. The Global Agricultural Monitoring (GLAM) Project jointly funded by USDA and the NASA Applied Sciences Program, is updating the FAS decision support system with the new generation of NASA satellite observations.

Figure: FAS decision support system images showing vegetation stress predominantly in croplands, during the 2006 drought, in southeastern Australia . The anomaly image compares NDVI values for the September 14 to September 29, 2006 timestep, to average NDVI measured during the same 16-day period from 2000-2005. The NDVI timeseries graph shows the reduced NDVI relative to the mean and to a productive year (2003) during the 2006 drought.

 

Monitoring of Agriculture with Remote Sensing –FOODSEC

The mission of MARS FOODSEC, an Action within the European Commission's Joint Research Centre, is to monitor food security for at-risk regions world-wide. The information produced contributes to EU external aid and development policies, in particular food aid and food security policy. The desired outcome is to avoid food shortages and market disruptions and to better calibrate and direct European food aid. Satellite observations and meteorological data are integrated with baseline data on regional agronomic practices into crop growth models to develop MARS FOODSEC monthly bulletins with yield forecasts by crop. Trends, similarity analysis, regression, and expert assessments are used to produce the monthly reports that are intended to be directly used by food security administrators. In addition to qualitative and quantitative crop yield assessments, several indicators, like rainfall, radiation, temperature, and water satisfaction indices are published in the bulletins. They are compared to long-term historical average and to last year indicators so that food security administrators can have a complete picture of the conditions occurring in the food-insecure areas. MARS AGRI4CAST is a partner Action focused on providing early, independent, and objective statistical estimates about the production of the main crops in Europe and in other strategic areas of the world production.

Diagram of the MARS FOODSEC Crop Assessment Process

Figure: Diagram of the MARS FOODSEC Crop Assessment Process

 

USAID Famine Early Warning Systems Network (FEWS NET)

The U.S. Agency for International Development (USAID) Famine Early Warning Systems Network (FEWS NET) is an information system designed to identify problems in the food supply system that potentially lead to famine or other food-insecure conditions in sub-Saharan Africa, Afghanistan, Central America, and Haiti. FEWS NET is a multi-disciplinary project that collects, analyzes, and distributes regional, national, and sub-national information to decision makers about potential or current famine or other climate hazard-, or socio-economic-related situations, allowing them to authorize timely measures to prevent food-insecure conditions in these nations. Regions and countries with FEWS NET representatives include sub-Saharan Africa (Angola, Burkina Faso, Chad, Djibouti, Ethiopia, Kenya, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria, Rwanda, Somalia, (southern) Sudan, Tanzania, Uganda, Zambia, and Zimbabwe), Central America (Guatemala, Honduras, and Nicaragua), Afghanistan, and Haiti.

Figure: Example of FEWS NET Food Security Bulleti

ESA Global Monitoring for Food Security (GMFS) Programme

The objective of the GMFS project developed by the European Space Agency (ESA) is to improve the provision of operational and sustainable information services, derived at least partly from earth observation data, to assist food aid and food security decision-makers from local to global level. GMFS aims to consolidate, support and complement existing regional information and early warning systems on food and agriculture. Together with other key players in the sector, GMFS is establishing a European Service for Food Security to guarantee state-of-the-art operational monitoring and forecasting for agricultural production and food security issues in direct support to European food security policy objectives. The longer-term goal of GMFS is to develop a network of geographically distributed service providers capable of contributing to and benefiting from satellite observations related to agricultural production monitoring. One activity that is being leveraged to advance the GMFS project is the broadcast of VEGETATION data to Africa through EUMETCast, aimed at promoting data utilization and develop capacities of regional participants.

Figure: Example GMFS crop monitoring products for Ethiopia and Senegal

 

UN FAO Food Insecurity and Vulnerability Information and Mapping Systems (FIVIMS)

The FIVIMS initiative responds to a request from developing countries for better coordination of international development support and food aid. FIVIMS addresses several key questions: Who are the food insecure and vulnerable people? Where are they? How many are they? Why are they hungry? What should be done to address the immediate and underlying causes of their food insecurity? Since the degree of vulnerability of people to under nourishment is determined by both their exposure to risk factors and their ability to cope with those risks, FIVIMS undertakes analyses that integrate information from across different sectors to assess both supply of and demand for food. An important FIVIMS product is the FIVIMS Global GIS Database, which illustrates the spatial and environmental contexts for agricultural productivity and accessibility and poverty maps derived using socio-economic data and satellite imagery.

Figure: Map of Percent Undernourished Poputions from FIVIMS GIS database

 

The World Food Program Vulnerability Analysis and Mapping (VAM) unit

The VAM unit identifies and monitors potential threats and risks to household food security and to provide timely information to enable decision makers to initiate assessments and to develop policies and strategies related to food security interventions. The VAM unit utilizes spatial analysis of survey and remotely sensed data to address: who the hungry people are, how many there are, where they live, the reasons they are hungry, how food aid can make a difference and what sorts of preparedness measures can be put in place to prevent them from being hungry in the future. Household, nutritional, and market price survey data are the primary information sources for VAM, but satellite derived information on vegetation conditions and land cover are also integrated into the spatial analytical framework.

Figure: WFP Map of food insecurity levels in Sri Lanka in 2002

 

SADC Regional Remote Sensing Unit Drought Monitoring Center

The Regional Remote Sensing Unit (RRSU) is a program coordinated by the Southern African Development Community (SADC) designed to support early warning for food security of its fourteen member nations. The goal of the program is to promote sustainable natural resource use and to enhance information for disaster risk management.

Figure: Percent of Average Rainfall in SADC region between January and April 2007 highlighting regions with drought conditions.

 

The Consortium for Spatial Information (CSI) of the Consultative Group on International Agricultural Research (CGIAR)

The CSI-CGIAR applies geospatial science to sustainable agricultural development, natural resource management, biodiversity conservation, and poverty alleviation in developing countries. The CGIAR system of research centers has been monitoring global agriculture since the early 1970's. The CSI is comprised of 15 CGIAR centers plus the International Centre for Integrated Mountain Development (ICIMOD). CSI-CGIAR activities include analyses of agricultural biodiversity and genetic resources, food security and food policy, water and soil resource conservation and agricultural and natural resources management. The centers are modelling the spatial dimensions of crop growth, irrigation, pests and pathogens and forests and fisheries. The consortium coordinates the many efforts of the individual research centers, developing mechanisms to share and disseminate spatial data and GIS and remote sensing software and tools. The CSI has a strong presence in developing countries, with "on-the-ground" research projects with national agricultural research systems and farmers themselves. Scientists directly involved in the CSI work with more than 8,000 other CGIAR scientists and staff to monitor and evaluate agricultural conditions and to conduct research and development for improving developing-country agriculture.

Figure: The 16 CSI-CGIAR centers: Bioversity International, International Center for Tropical Agriculture (CIAT), Center for International Forestry Research (CIFOR), International Maize and Wheat Improvement Center (CIMMYT), International Potato Center (CIP), International Center for Agricultural Research in Dry Areas (ICARDA), The International Centre for Integrated Mountain Development (ICIMOD), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), International Food Policy Research Institute (IFPRI), International Institute for Tropical Agriculture (IITA), International Livestock Research Institute (ILRI), International Rice Research Institute (IRRI), International Water Management Institute (IWMI), African Rice Center (WARDA), World Agroforestry Center, World Fish Center.

 

Examples of National Agricultural Monitoring Systems

Agricultural Monitoring in Kazakhstan

Kazakhstan agriculture specializes in grain production. Spring wheat and barley present regional steppe monoculture characterized by high variability of annual crop productivity. Large field sizes (400 hectares) in Kazakhstan provide optimal conditions for satellite crop monitoring. At present croplands cover ~14 million hectares of land and produced 20 million tons of grain in 2007. In Kazakhstan, the research and application of remote sensing techniques to agricultural monitoring began in 1997. Since 2002, an operational crop remote sensing monitoring system was established by the Ministry of Agriculture implemented by The National Center of Space Research and Technologies of National Space Agency of Kazakstan. The program involves monitoring spring soil water storage, calendar dates of cereals sowing, crop-fallow rotation system with control of 4 year's historical period, crop state, and forecasting wheat/barley grain production. Final outputs of the system present the thematic maps and satellite-based crop statistics. The data supporting monitoring system is acquired by several coarse and moderate resolution remote sensing satellite systems, including NOAA-AVHRR, MODIS, IRS: AwiFS, LISS III, and annual in situ observations, including data collected at established monitoring stations and multiple transects collected yearly. Full coverage of IRS LISS III data is used to map spatial extent of individual fields. In addition, daily coarse resolution satellite data are used for thematic processing. Straightforward agricultural structure of Kazakhstan enables high accuracy of the satellite estimates of the basic agricultural parameters.

Kazakhstan cropland and regional arable land

 

Figure: Kazakhstan cropland and regional arable land.

Agricultural Monitoring in Brazil

Brazilian agriculture is extensive, with some 54 million hectares of agricultural land and 131 million tons of grain production in 2007. Due to regional differences in soils, relief, climate, management practices, diseases, calendar, rotation and area expansion, crop monitoring and forecasting is a great challenge for the government. The need for more precise, less subjective and timely information led the Ministry of Agriculture, through its National Supply Agency (CONAB – Companhia Nacional de Abastecimento), which is responsible for the official crop production figures, to create a national agricultural monitoring and forecasting project in 2003, called "Geosafras".

The objectives of the "Geosafras Project" are:

  • to improve the crop monitoring/forecasting methods
  • to diminish subjectivity in area and yield estimates
  • to develop specialists in crop monitoring/forecasting
  • to operationalize new crop monitoring/forecasting methodologies

The two main components of the project are area estimates, using statistics and remote sensing, and yield estimates, using agrometerorological, spectral and mixed models. The strategy to put together such a project, and gather results in a relatively short period of time, was to create a network of c. 18 partner institutions (universities, research institutes, local and federal government agencies) to develop methodologies for area estimates and yield forecast, based mainly on geospatial technologies.

Initially, the area estimates were developed by selecting agricultural municipalities stratified based on their production, and random sampling points for field observation within the municipalities, followed by expansion estimates. This methodology worked well for state level estimates in the southern states, but is still being evaluated for central and northern states which have bigger agricultural fields. For perennial (e.g. coffee) and semi-perennial (e.g. sugar cane) crops, moderate resolution imagery (e.g. Landsat and CBERS-2) are being used for field mapping and area estimates. Coarse spatial resolution sensors, such as MODIS and NOAA/AVHRR are being used to generate cropland masks. Yield estimates are mainly based on agrometeorologial models ( Figure ), spectral models and mixed (agromet + spectral) models.

The project is in its 4 th year, funded by CONAB with fund management by UNDP (United Nations Development Programme), and now, becoming operational at the agency level, within the official calendar of the Crop Evaluation System (SAS) ( Figure ), which releases crop forecasts monthly.

Figure: Results of an agrometeorological model for yield/production estimates

Figure: Information flow for "Geosafras" and the Crop Evaluation System at CONAB

 

Agricultural Monitoring in USA

The mission of the National Agricultural Statistics Service (NASS) is to provide timely, accurate, and useful statistics in service to U.S. agriculture. These statistics cover virtually every facet of U.S. agriculture, from production and supply of food and fiber to prices paid and received by farmers and ranchers. Every five years NASS conducts the Census of Agriculture, which provides a comprehensive statistical summary of many aspects of U.S. agriculture. Remote sensing data and techniques are valuable tools used to improve the accuracy of some NASS statistics. NASS uses remote sensing data to construct and sample area frames for statistical surveys, estimate crop area, and create crop-specific land-cover data layers for geographic information systems (GIS). For example, NASS uses Landsat imagery, digital orthophoto quadrangles, and other remotely sensed inputs for all 48 continental states and Puerto Rico to select the yearly area-based samples and supplemental samples which will be used to measure the completeness of the Agricultural Census in 2007 and provide the basis for the annual June Agricultural Survey. In addition, NASS constructs a new area-based sampling frame for approximately two states each year. The remote-sensing acreage estimation project analyzes Resourcesat-1 AWiFS data over the major corn and soybean producing states to produce independent crop acreage estimates at the state and county levels and a crop-specific categorization called the Cropland Data Layer. The Cropland Data Layer program produced crop specific land cover products in over 29 states to date, with annual repeat coverage of 13 agriculturally intensive states (Figure:). NASS is also in a continuing partnership with the USDA/Agricultural Research Service using NASA MODIS sensor data as an input for setting early season small-area yield estimates in several mid-western states. NASS also produces vegetation condition products based on the normalized difference vegetation index during the growing season from the NOAA-AVHRR sensor, providing policymakers in the Department of Agriculture with an independent look at growing conditions across the nation.

Figure: NASS Cropland Data Layers (CDLs)

 

Agricultural Monitoring in Europe

The mission of the crop production forecasts activities of the European Commission at the Joint Research Centre (MARS-Stat ) is to provide accurate, independent and timely crop yield forecasts and crop production biomass (including bio fuel crops) for the union territory and other strategic areas of the world.

MARS-Stat has been developing and operationally running a Crop Forecasting System since 1992 in order to provide timely crop production forecasts at European level. This system is able to monitor crop vegetation growth (cereal, oil seed crops, protein crops, sugar beet, potatoes, pastures, rice) and include the short-term effects of meteorological events on crop productions and to provide yearly forecasts on European crop predictions. The MARS-Stat system is made by remote sensing (NOAA-AVHR, SPOT-VGT, MODIS, MSG) and meteorological observations (observed station data and ECMWF data), agro-meteorological modeling (Crop Growth Monitoring System, CGMS) and statistical analysis tools.

Results are regularly published in the form of bulletins and via the MARSOP website including maps of weather indicators based on observations and numerical weather models, maps and time profiles of crop indicators based on agro-meteorological models and maps of vegetation indices and cumulated dry matter based on remote sensing images (http://www.marsop.info).

In addition, MARS-Stat is the depositary of techniques developed using remote sensing and area frame sampling at the European level to estimate crop areas. MARS-Stat will continue the development of new improvements (spatially and methodological) for the Crop Yield Forecasting and Area Estimate System. A new world-wide crop production estimation activity has started with the Black Sea area and will extend to Russia and China with a focus on wheat production.

Figure: The MARS Crop Monitoring in Europe - examples of the published bulletins

 

Agricultural Monitoring in Australia

Earth Observation for agricultural monitoring in Australia ranges from broad-scale monitoring of vegetation for greenhouse gas accounting through to sub-paddock precision agriculture applications across numerous industries such as cropping, livestock grazing, viticulture, rice and sugar industries. In addition to earth observation there are a number of static national data sets (e.g. land tenure, remnant vegetation, land use, soils) as well as historical and near real-time climate information and census statistical data that can be integrated with earth observations for interrogation and modelling purposes.

Earth observation applications at the regional and local scale are numerous. They are often developed for particular agro-climatic zones and tailored for specific market segments. Products are both qualitative indices (e.g. NDVI) through to quantitative remote sensing (e.g. pasture biomass and growth rate), and in many instances are integrated with models. Near real-time applications are of increased interest such as fire monitoring, biosecurity surveillance, extreme events, and precision agriculture.

Temporal resolution also remains varied – cropping applications for the within-paddock strategic application of fertilizers utilizes a few key images per year, whereas livestock grazing applications for stock movement decisions utilizes weekly imagery. There are often trade-offs with spatial resolution, with frequent observations using predominantly MODIS imagery; less frequent and within-paddock observations tend to use moderate resolution imagery such as Landsat, Spot, Ikonos, Quickbird and airborne platforms.

The business model for the provision of remote-sensed products is not well established in Australia . The diversity of the market necessitates tailoring of:

(i) the products,

(ii) the way they are delivered to the market and

(iii) packaging with other information streams and decision support software.

Advances in sensor networks and information communication technologies are increasing the capacity to deliver. In the face of the global pressures of climate change, extreme events, food security, biosecurity and environmental stewardship, earth observation remains critical for monitoring, understanding and managing Australia 's agricultural and natural ecosystems.

Figure: Example image of Australian pasture growth rates (kg/ha/day) delivered weekly at the paddock level

 

Agricultural Monitoring in Argentina

The Dirección de Coordinación de Delegaciones (DCD) is the national Argentine government agency responsible for agricultural estimates within the Secretary of Agriculture, Livestock, Fisheries and Food (SAGPyA). This mission is accomplished by a network of 34 offices throughout the main agricultural areas of the country, gathering information about different statistics concerning agriculture including: area, yield, seeding and harvesting progress, crop condition, etc. Each of these offices reports periodically to the DCD in Buenos Aires , where the information is checked, summarized and released to the user community.

Traditionally, subjective estimates were used. However, since 1981 remote sensing has been applied to improve the estimates through four approaches:

Land use/land cover stratification as a basis of area sampling frames.
Digital analysis of satellite imagery (Landsat) to estimate area planted of extensive crops (i.e. wheat, corn, soybean) using classification techniques ( Figure )

Figure: Area planted estimates from Landsat

 

Support of qualitative estimates of land-use through coarse resolution (SAC-C, 175 m pixel size) imagery and e-mailing results in .xls format to local offices.
Area assessment of non- extensive crop (i.e. potatoes in SE province of Buenos Aires. ).
All the satellite imagery used in this work, are surveyed freely by the National Commission of Spatial Activities (CONAE).

 

Agricultural Monitoring in Russia

Agricultural production is monitored in Russia to foster sustainable agricultural development, for environmental assessment and protection, and to monitor compliance with international environmental and trade conventions. The national agricultural monitoring system, established within the Ministry of Agriculture in 2003, relies on combined use of information from regional agricultural committees, satellite remote sensing data and ground agro-meteorological observations. The remote sensing component of the agricultural monitoring system is developed by Russian Academy of Sciences Space Research Institutes and involves daily MODIS observations as primary source of the satellite data. The primary user of the information is the Federal Ministry of Agriculture, while the Ministry of Natural Resources, the Federal Statistical Agency and Hydro-Meteorological Service, regional agricultural committees and administrators, and local agricultural producers and enterprises are considered as potential users in near future. The main foci of the agricultural monitoring system are arable land area, crop land use mapping, crop rotation and seasonal crop development. In future, the system is likely to contribute to crop production forecasts, greenhouse gas flux monitoring, and soil erosion risk assessment. The current research areas in support of this agricultural monitoring system include: expanding the monitoring system over the entire northern Eurasia region, better attribution of crop rotation characteristics, operationalizing land use change monitoring, combining moderate and high resolution data to improve monitoring accuracy, and developing links to national reporting under international environmental conventions.

 

Agricultural Monitoring in China

In China , the research and application of remote sensing technology to agricultural monitoring began in late 1970s. In 1990, an operational crop remote sensing monitoring system was set up and put into operation based on the achievements of several previous research projects dating from 1984. Since the late 1990s with the rapid development of earth observing instruments and technology, increasing attention has been given in China to agriculture monitoring with remote sensing. Several departments and research agencies have focused their research on this topic and a number of these set up their own remote-sensing-based crop or agriculture monitoring systems. Current operational systems include: the Ministry of Agriculture (MOA) China Agriculture Remote Sensing Monitoring System (CHARMS), the Chinese Academy of Sciences (CAS) China CropWatch System, and the China Meteorological Administration (CMA) crop growth monitoring and yield prediction system. CHARMS developed by the Remote Sensing Application Centre of MOA has been operational since 1999. It monitors crop acreage change, yield, production, crop growth, drought and other agriculture-related information for 5 main crops in China . It provides this information to the MOA and related agriculture management sectors in the form of ad hoc reports according to MOA Agriculture Information Dissemination Calendar more than 5 times per month during the growing season. It provides critical information to inform decision making in MOA. The CAS China Crop Watch System (CCWS) was developed by the Institute of Remote Sensing Applications , CAS in 1998. The CCWS covers China as well as 46 main grain growing countries around the world. The CCWS monitors crop growing conditions, production, drought, crop plantation structure and cropping index. The CCWS publishes 7 monthly bulletins and 20 newsletters every year, which have become an important information source for various government bodies. In 2004, the National Statistics Bureau began to use remote sensing technology to improve agriculture statistics. Remote sensing technology has been extensively applied to the monitoring and management of agriculture in China.

Figure: An example of rapid agricultural land-use change in China , from wheat fields to urban.

 

Agricultural Monitoring in India

The National Crop Forecasting Centre (NCFC) of the Department of Agriculture & Cooperation (DAC), of the Government of India was established in 1998, with a mandate to develop a framework for providing crop production forecasts at district, state and national levels. In addition, to support the high-level decision making and planning it is responsible for providing information on crop sowing progress, crop condition throughout the growing period, and on the effect of episodic events such as floods, drought, hail storms, pests, disease etc. on crop production. Use of remote sensing has been an important consideration by the DAC which sponsored the Crop Acreage and Production Estimation (CAPE) project. The Space Applications Centre (SAC) of the Indian Space Research Organization (ISRO) has led the project in developing: i) a remote sensing based procedure for crop acreage estimation at district level, ii) spectral and weather models for yield forecasting, iii) semi-automatic s/w package CAPEMAN (later renamed CAPEWORKS) for analysis of RS data, iv) technology transfer to teams across the country that use these procedures and make in-season crop production forecasts. LISS-III data from the Indian Remote Sensing satellites (IRS) are being regularly used to make crop production forecasts.

To address the DAC requirement of multiple in-season, national level assessments of crops and production forecasting, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been developed by SAC (Fig.X). FASAL envisages providing information on crop prospects at the beginning of the crop season with econometric models, followed by weather based models to forecast crop acreage early in the season, and later on yield. Moderate spatial resolution remote sensing data from WiFS/AWiFS will be used to provide area estimates under crops about 6-8 weeks after sowing. By the middle of the crop growing season, higher spatial resolution data like AWiFS and LISS-III will be used to provide area estimates under selected crops. Crop condition and crop area estimates will be repeated about a month before crop maturity. Weather based models will be implemented independently as well as with spectral data to provide crop yield forecasts at different crop stages. Use of crop growth simulation models with spatial coverage and parameters derived from remote sensing data is also planned.

As a part of FASAL, national level multiple assessments of wheat and Kharif (Monsoon) rice acreage estimates are being made using AWiFS and Radarsat ScanSAR Narrow Beam-2 temporal data, respectively. An example of use of temporal AWiFS data from Resourcesat for crop area estimation is given in Fig. XX. Weather models have been developed and are used for production forecasting at the state and national level. Winter-potato acreage estimation is performed using data from LISS-III and AWiFS, weather and crop growth simulation models are used for yield forecasting. Besides providing crop statistics, changes in crop area due to low soil moisture and rainfall and changes in cropping pattern are also mapped.

Procedures for estimation of Leaf Area Index (LAI), NDVI, insolation, albedo, and LST are under development using IRS (AWiFS) and INSAT/Kalpana (AVHRR and CCD) data. Validation of these products with the support of well distributed in-situ field measurements has been performed. Crop growth simulation models such as WTGROWS and WOFOST have been adapted with a spatial framework to use the remote sensing derived parameters along with other data.

Cropping system analysis of the Indo-gangetic Plains region of India has been done. First a gross crop rotation mapping was done using SPOT-VGT data. Subsequently seasonal cropping patterns for Kharif, winter and summer seasons have been mapped using AWiFS and Radarsat ScanSAR Narrow Beam-2 data at larger scale. Crop rotation maps have been generated using the cropping pattern data. Field survey has been carried out to identify and characterise the cropping systems of the region. The example of multi-scale and region coverage with data of different spatial resolutions is shown in Fig. XXX A comprehensive data base of cropping systems and associated parameters has been created at a 50m pixel size. A cropping system simulation model (Cropsyst) has been validated with the field and remote sensing data. Cropping system performance indicators such as the Area Diversity Index (ADI), Cultivated Land Utilisation Index (CLUI) and Multiple Cropping Index (MCI) have been developed.

Figure: An example of use of multi-temporal Resourcesat AWiFS data for area estimation in India.

Figure: Use of Multi-spatial resolution data for crop rotation mapping at varying scales and coverage in Indo-Gangetic Plains Region of India.

 
 
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