USDA, National Agricultural Statistics Service CEOS Committee on Earth Observing Satellites (CEOS) EDS Euclidean distance similarity FPA Full pixel areas GCAD Global cropland area database GCE Global cropland extent GCE V1.0 Global cropland extent version 1.0 GDEM ASTER-derived digital elevation data GEO Group on Earth Observations GEOSS Global Earth Observation System of Systems GFSAD Global food security support analysis data GIMMS Global Inventory Modeling and Mapping Studies JERS SAR Japanese Earth Resources Satellite-1 (JERS-1) ISDB IA Ideal Spectra Data Bank on Irrigated Areas LEDAPS Landsat Ecosystem Disturbance Adaptive Processing

System MFDC Mega File Data Cube MODIS Moderate-resolution imaging spectroradiometer MSAS Modi¤ed spectral angle similarity NASS National Agricultural Statistics Service of USDA NDVI Normalized di¦erence vegetation index NOAA National Oceanic and Atmospheric Administration SAR Synthetic aperture radar SCS Spectral correlation similarity SIT Strategic Implementation Team SMT Spectral matching techniques SPA Subpixel areas SPOT Système Pour l’Observation de la Terre SSV Spectral similarity value USDA United States Department of Agriculture USGS United States Geological Survey VGT Vegetation sensor of SPOT satellite VHRI Very-high-resolution imagery VHRR Very-high-resolution radiometer

Ÿe precise estimation of the global agricultural croplandextents, areas, geographic locations, crop types, cropping intensities, and their watering methods (irrigated or rain-fed; type of irrigation)—provides a critical scienti¤c basis for the development of water and food security policies (Ÿenkabail et al., 2010, 2011, 2012, Turral et al., 2009). By year 2100, the global human population is expected to grow to 10.4 billion under median fertility variants or higher under constant or higher fertility variants (Table 6.1) with over three-quarters living in developing countries and in regions that already lack the capacity to produce enough food. With current agricultural practices, the increased demand for food and nutrition would require about 2 billion hectares of additional cropland, about twice the equivalent to the land area of the United States, and lead to signi¤cant increases in greenhouse gas emissions (GHG) associated with agricultural practices and activities (Tillman et al., 2011). For example, during 1960-2010, world population more than doubled from 3 to 7 billion. Ÿe nutritional demand of the population also grew swi¬ly during this period from an average of about 2000 calories per day per person in 1960 to nearly 3000 calories per day per person in 2010. Ÿe food demand of increased population along with increased nutritional demand during this period was met by the “green revolution,” which more than tripled the food production, even though croplands decreased from about 0.43 ha per capita to 0.26 ha per capita (FAO, 2009; Funk and Brown, 2009). Ÿe increase in food production during the green revolution was the result of factors such as: (1) expansion of irrigated croplands, which had increased in 2000 from 130 Mha in the 1960s to between 278 Mha (Siebert et al., 2006) and 467 Mha (Ÿenkabail et al., 2009a,b,c), with the larger estimate due to consideration of cropping intensity; (2) increase in yield and per capita production of food (e.g., cereal production from 280 to 380 kg/person and meat from 22 to 34 kg/person (McIntyre, 2008); (3) new cultivar types (e.g., hybrid varieties of wheat and rice, biotechnology); and (4) modern agronomic and crop management practices (e.g., fertilizers, herbicide, pesticide applications).