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Global Land Analysis & Discovery (GLAD) Global Cropland Extent

posted on 2024-02-13, 13:56 authored by Kyle Pittman, Matthew C. Hansen, Inbal Becker-Reshef, Peter V. Potapov, Christopher O. Justice

This study utilized 250m MODIS (MODerate Resolution Imaging Spectroradiometer) data to map global production cropland extent. A set of multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to depict cropland phenology over the period 2000-2008. Sub-pixel training datasets were used to generate a set of global classification tree models, resulting in a global per-pixel cropland probability layer. The probability product was then thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service) Production, Supply and Distribution (PSD) database describing per-country acreage of production field crops.

Five global land cover classifications were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principal global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification.

This study was conducted as part of the Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, University of Maryland and South Dakota State University initiative. GLAM has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) scientifically-validated, near-real-time, earth observations products, and analysis tools for crop-condition monitoring and production assessment.

With a spatial resolution of 250m, the Global Cropland Extent product represents the finest-scale global cropland map derived using synoptic inputs, and due to the inclusion of 9 years of MODIS data it is designed to be relatively insensitive to inter-annual variability in depicting core cropland production areas. These products will be incorporated into the decision support system used by FAS analysts to produce global crop production forecasts.

The probability and discrete cropland/non-cropland data are available for download by MODIS tile at the full ~250m resolution or as global mosaics at ~1km resolution.

Resources in this dataset:


National Aeronautics and Space Administration: Applied Science Program


University of Maryland

South Dakota State University



Data contact name

Hansen, Matthew

Data contact email


University of Maryland, Department of Geographical Sciences

Temporal Extent Start Date


Temporal Extent End Date



  • Not specified

Geographic location - description


ISO Topic Category

  • biota
  • boundaries
  • economy
  • elevation
  • environment
  • farming
  • geoscientificInformation
  • imageryBaseMapsEarthCover
  • inlandWaters
  • location
  • planningCadastre

National Agricultural Library Thesaurus terms

cropland; moderate resolution imaging spectroradiometer; normalized difference vegetation index; phenology; data collection; models; probability; databases; acreage; field crops; land cover; food crops; corn; soybeans; wheat; rice; crop production; intensive farming; Africa; agricultural productivity; monitoring; USDA; decision support systems

Pending citation

  • No

Public Access Level

  • Public

Preferred dataset citation

Pittman, Kyle; Hansen, Matthew C.; Becker-Reshef, Inbal; Potapov, Peter V.; Justice, Christopher O. (2019). Global Land Analysis & Discovery (GLAD) Global Cropland Extent. University of Maryland, Department of Geographical Sciences.

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