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Principal Investigator (PI): Prasad Thenkabail, US Geological Survey (USGS)

Monitoring global croplands (GCs) is imperative for ensuring sustainable water and food security to the people of the world in the twenty-first century. However, the currently available cropland products suffer from major limitations such as:

  1. Absence of precise spatial location of the cropped areas;
  2. Coarse resolution nature of the map products with significant uncertainties in areas, locations, and detail;
  3. Uncertainties in differentiating irrigated areas from rainfed areas;
  4. Absence of crop types and cropping intensities; and
  5. Absence of a dedicated web\data portal for the dissemination of cropland products.

Therefore, our project aims to close these gaps through a Global Cropland Area Database (GCAD) at nominal 30m (GCAD30) with 4 distinct products:

  1. Cropland extent\area,
  2. Crop types with focus on 8 crops that occupy 70% of the global cropland areas,
  3. Irrigated versus rainfed, and
  4. Cropping intensities: single, double, triple, and continuous cropping.

The project will disseminate these data and products through the USGS Powell Center Global Croplands Working Group web portal which will also include web mapping for user interaction, feedback, and improvements.

First, the above 4 products will be generated for GCAD for nominal year 2010 (GCAD2010) based on Landsat 30m Global Land Survey 2010 (GLS2010) fused with Moderate Resolution Imaging Spectroradiometer (MODIS) 250m NDVI monthly maximum value composites (MVC) of 2009-2011 data, and suite of secondary data (e.g., long-term precipitation, temperature, Global Digital Elevation Model (GDEM) elevation).

GCAD30 will be produced using three mature cropland mapping algorithms (CMAs):

  1. Spectral matching techniques (SMT; Thenkabail et al., 2009a, b, 2007);
  2. A cropland classification algorithm (ACCA) that is rule-based: (Thenkabail et al., 2012; e.g., http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548); and
  3. Hierarchical segmentation (HSeg) algorithm: (Tilton et al., 2012; http://science.gsfc.nasa.gov/606.3/TILTON/).

The SMTs will be preferred for parts of the world with large volumes of field-plot and other geo-specific map data (section 12.1). ACCA will be applied in regions with sparse or unreliable field-plot data, but where numerous other sources of data (see section 7.1) and large volume of training data generated from HSeg (Tilton et al., 2012) exist. Further, HSeg will be used in conjunction with SMTs and ACCAs to help improve classification accuracies and generate training data over highly fragmented croplands.

Second, the same 4 products will be generated for GCAD1990 which will combine GLS1990, advanced very-high-resolution radiometer (AVHRR) 1989-1991, secondary climate and topographic data and national statistical data. Third, GCAD four decades will characterize the global cropland dynamics from the 1980s to present based on AVHRR 8 km (1982-2000) and MODIS 250m (2001-2017) continuous monthly time-series. All the products will be extensively evaluated for accuracies, errors, and uncertainties using data such as: (i) 25% of 20,000+ in-situ data, (ii) several thousand globally well distributed very high resolution (sub-meter to 5 meter) Commercial Remote Sensing Space Policy Imagery Derived Requirement Database of the USGS, available free of cost to the project through the National Geospatial Intelligence Agency (iii), our ongoing collaborative work over large areas (e.g., rice map of Asia; Figure 7), and (iv) maps from national systems (e.g., USDA CDL; see global letters of support).

GCAD30 will make significant contributions to Earth System Data Records (ESDRs), Group on Earth Observations Food Security and Sustainable Agriculture and Water Resources Management Societal Beneficial Areas (GEO Ag. SBAs), GEO Global Agricultural Monitoring Initiative, and the recent Big Data Initiative by the White House. The project has the support of USGS Working Group on Global Croplands (https://powellcenter.usgs.gov/view-project/4f79edbbe4b0009bd827f517).