Global Food Security-Support Analysis Data at 30 m (GFSAD)
Science Center Objects
The GFSAD30 is a NASA funded project to provide high resolution global cropland data and their water use that contributes towards global food security in the twenty-first century. The GFSAD30 products are derived through multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland dynamics from 1990 to 2017.
Monitoring global croplands is imperative for ensuring sustainable water and food security to the people of the world in the Twenty-first Century. The currently available cropland products suffer from major limitations such as:
- Absence of precise spatial location of the cropped areas;
- Coarse resolution nature of the map products with significant uncertainties in areas, locations, and detail;
- Uncertainties in differentiating irrigated areas from rainfed areas;
- Absence of crop types and cropping intensities; and
- Absence of a dedicated web\data portal for the dissemination of cropland products.
Thereby, the overarching goal of GFSAD30 project is to produce consistent and unbiased estimates of global agricultural cropland products such as:
1) Cropland extent\area;
2) Crop types with focus on 8 crops that occupy 70% of the global cropland areas;
3) Irrigated versus rainfed;
4) Cropping intensities: single, double, triple, and continuous cropping;
5) Cropland change over space and time: 1990-2017.
Once the above products are established, other products such as the following can be derived using the above products and certain other inputs:
6) Crop productivity (productivity per unit of land; kg\m2)
7) Water productivity (crop per drop or productivity per unit of water; kg\m3).
For additional information and data, go to Croplands.org
This map shows cropland distribution across the world in a nominal 30-meter resolution derived primarily with Landsat imagery for the year 2015. The map uses machine learning algorithms on Google Earth Engine cloud computing platform. This is the baseline product of the GFSAD30 Project. There is a total of 1.874 billion hectares (roughly 12.6 percent of the global terrestrial area) of croplands in the world. View the global map or zoom-in further to see individual farms at https://www.croplands.org/app/map.
(Public domain.)
The spatial distribution of global cropland areas (~1.5 billion hectares) and five dominant crop types (wheat, rice, maize, barley and soybeans). This composite map was produced by Thenkabail and Gumma through spatial modeling involving remote sensing derived global irrigated and rainfed croplands (Thenkabail et al., 2011, 2009a, 2009b) and five dominant global crop types from other sources (Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009)). The 5 crops constitute about 60% of all global cropland areas.
Cover page credits: Dr. Prasad S. Thenkabail, U.S. Geological Survey (USGS) and Dr. Murali Krishna Gumma, International Rice Research Institute (IRRI) with inputs from the USGS Powell Center working group on global croplands (WGGC) team members (http://powellcenter.usgs.gov/current_projects.php#GlobalCroplandMembers). For more information contact: pthenkabail@usgs.gov or thenkabail@gmail.com. The image was produced for the PE&RS Special issue on global croplands, Vol. 78, No.8; August 2012
(Courtesy: Dr. Prasad Thenkabail)
Global cropland water use. Country-wise agricultural crop water use in km3/yr. In India, China, and Pakistan as a result of double and triple cropping that are irrigated, the water use is dominated by irrigated croplands (blue water use). In USA, the water use is dominated by rainfed croplands (green water use). Data source: Gleick (2011).
(Courtesy: Dr. Prasad Thenkabail)
Hyperspectral signature bank of world’s major crops. The initial goal of a global cropland monitoring system should consist of developing hyperspectral signature bank of major world crops (e.g., this figure) along with crop phonologies (e.g., Figure 4) in order to: (a) establish improved models of crop biophysical and biochemical quantities, (b) increase crop classification accuracies, and (c) produce accurate crop and water productivity models. The six leading world crops (Table 1) cover 64% of the global cropland areas. Sample hyperspectral signatures of these six World crops are illustrated in the figure. The background image is irrigated and rainfed croplands of the world (Thenkabail et al., 2009a, 2009b, 2011).
(Courtesy: Dr. Prasad Thenkabail)
Rice map of South Asia. Crop phenologies and intensities studied using time-series remotely sensed data illustrated for rice crop in South Asia. A clear and deep understanding of phenologies and intensities will require us to develop a temporal (e.g., this figure) and spectral knowledge base of each crop in different agro-ecosystems of the world leading to mapping distinct classes within a crop, which in turn will lead to accurate assessments of green water use (rainfed croplands) and blue water use (irrigated croplands).
(Courtesy: Dr. Prasad Thenkabail)
Global croplands and other land use and land cover. This baseline product is derived based on AVHRR time-series, SPOT Vegetation, and a number of secondary data by using the Data published by Thenkabail et al. (2009, 2011; see publications). [Credits: Prasad Thenkabail and Zhuoting Wu].
(Courtesy: Dr. Prasad Thenkabail)
Key global cropland area products that will support food security analysis in the twenty-first century. [Credits: GFSAD30 project team]
(Courtesy: Dr. Prasad Thenkabail)
An aggregated three class global cropland extent map at nominal 1-km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013) and Friedl et al. (2010). Class 1 is total cropland extent based on pixels with 100% cropland cover (2.3 billion hectares). Class2 and Class3 have minor fractions of croplands.
(Courtesy: Dr. Prasad Thenkabail)
A disaggregated twelve class global cropland extent map derived at nominal 1-km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013) and Friedl et al. (2010). Classes 1-9 are cropland classes and show the dominance of irrigated and rainfed agriculture. Classes 10-12 have minor/very minor fractions of croplands.
(Courtesy: Dr. Prasad Thenkabail)
Center image of global cropland (irrigated and rainfed) areas @ 1 km for year 2000 produced by overlying the remote sensing derived product of the International Water Management Institute (IWMI; Thenkabail et al., 2012, 2011, 2009a, 2009b; http://www.iwmigiam.org) over 5 dominant crops (wheat, rice, maize, barley and soybeans) of the world produced by Ramankutty et al. (2008). The 5 crops constitute about 60% of all global cropland areas. The IWMI remote sensing product is derived using remotely sensed data fusion (e.g., NOAA AVHRR, SPOT VGT, JERS SAR), secondary data (e.g., elevation, temperature, and precipitation), and in-situ data. Total area of croplands is 1.53 billion hectares of which 399 million hectares is total area available for irrigation (without considering cropping intensity) and 467 million hectares is annualized irrigated areas (considering cropping intensity). Surrounding NDVI images of irrigated areas: The January to December irrigated area NDVI dynamics is produced using NOAA AVHRR NDVI. The irrigated areas were determined by Thenkabail et al. (2011, 2009a, b).
(Courtesy: Dr. Prasad Thenkabail)
Global agricultural dynamics over 2 decades (1982-2000) illustrated here for some of the most significant agricultural areas of the World. The baseline data is the global irrigated and rainfed croplands of the world for nominal year 2000 produced by Thenkabail et al. (2012). The time-series graphics is derived from AVHRR 8-km NDVI monthly data from 1982-2000.
(Courtesy: Dr. Prasad Thenkabail)
Available field data points from various sources. The IWMI, Gumma, and Thenkabail collections are collected by this team or the former teams as in publications Thenkabail et al. (2009, 2011). These have detailed ground data at every point with coordinates, photos, and land cover types. The Geo_wiki_validation, Geo_wiki_competetion, and USAID_Project collections are sourced from various people from around the world as part of the Geo-Wiki Project (www.geo-wiki.org) which aims to improve the quality of global land cover maps. The project is currently adding more data through field visits during the period of the project as well as data sourced from various sources. (Credits for organizing the data: Mutlu Ozdogan and Matthew Bougie).
Disclaimer: This data, compiled from various sources, is not for distribution at this stage. When we decide to disseminate this, that will be done through this website.
(Courtesy: Dr. Prasad Thenkabail)
Cropland Percent to Total Global Cropland
Figure 1. Cropland area % by Country as a % of the total net cropland areas of the world (1.873 billion hectares). Percentage cropland areas by country for the nominal year 2015, derived using Landsat 30-m time-series data, machine learning algorithms (e.g., Random Forest, support vector machines, recurssive heirarchical segmentation), and petabyte-scale big-data computing on the Google Earth Engine (GEE) cloud. Croplands as percentage of the total net global cropland area (1.873 billion hectares). The product is produced for the NASA and USGS funded global food security-support analysis data @ 30-m (GFSAD30). For further details visit:
https://lpdaac.usgs.gov/about/news_archive/release_gfsad_30_meter_cropland_extent_products
(Courtesy: Teluguntla, P., Thenkabail, P.S, Xiong, J., and Oliphant, A. )
Cropland Percent to Country Land Area
Figure 2. Cropland area % by Country as a % of the total geographic area of the country. Percentage cropland areas by country for the nominal year 2015, derived using Landsat 30-m time-series data, machine learning algorithms (e.g., Random Forest, support vector machines, recurssive heirarchical segmentation), and petabyte-scale big-data computing on the Google Earth Engine (GEE) cloud. Croplands as percentage of the total national geographic area. The product is produced for the NASA and USGS funded global food security-support analysis data @ 30-m (GFSAD30). For further details visit:
https://lpdaac.usgs.gov/about/news_archive/release_gfsad_30_meter_cropland_extent_products
(Courtesy: Teluguntla, P., Thenkabail, P.S, Xiong, J., and Oliphant, A.)
Cropland per Capita
Figure 3. Cropland\hectares per person in each country. Map shows how much cropland area each person has for each country in the world for the nominal year 2015, derived using Landsat 30-m time-series data, machine learning algorithms (e.g., Random Forest, support vector machines, recurssive heirarchical segmentation), and petabyte-scale big-data computing on the Google Earth Engine (GEE) cloud. Croplands as percentage of the total national geographic area. The product is produced for the NASA and USGS funded global food security-support analysis data @ 30-m (GFSAD30). For further details visit:
https://lpdaac.usgs.gov/about/news_archive/release_gfsad_30_meter_cropland_extent_products
(Courtesy: Teluguntla, P., Thenkabail, P.S, Xiong, J., and Oliphant, A.)
Cropland by Continent
Figure 4. Cropland area % by Continent as a % of the total net cropland area of the world (1.873 billion hectares). Percentage cropland areas by continent for the nominal year 2015, derived using Landsat 30-m time-series data, machine learning algorithms (e.g., Random Forest, support vector machines, recurssive heirarchical segmentation), and petabyte-scale big-data computing on the Google Earth Engine (GEE) cloud. Croplands as percentage of the total global cropland area (1.873 billion hectares). The product is produced for the NASA and USGS funded global food security-support analysis data @ 30-m (GFSAD30). For further details visit:
https://lpdaac.usgs.gov/about/news_archive/release_gfsad_30_meter_cropland_extent_products
(Courtesy: Teluguntla, P., Thenkabail, P.S, Xiong, J., and Oliphant, A.)
Cropland Percent of Land Area by Continent
Figure 5. Cropland area % by Continent as a % of the total geographic area of the continent. Percentage cropland areas by continent for the nominal year 2015, derived using Landsat 30-m time-series data, machine learning algorithms (e.g., Random Forest, support vector machines, recurssive heirarchical segmentation), and petabyte-scale big-data computing on the Google Earth Engine (GEE) cloud. Croplands as percentage of the total geographic area of the continent. The product is produced for the NASA and USGS funded global food security-support analysis data @ 30-m (GFSAD30). For further details visit:
https://lpdaac.usgs.gov/about/news_archive/release_gfsad_30_meter_cropland_extent_products
(Courtesy: Teluguntla, P., Thenkabail, P.S, Xiong, J., and Oliphant, A.)
GFSAD30 Final Mosaic
Figure 6. Total net cropland area (TNCA) of the world for the nominal year 2015, derived using Landsat 30-m time-series data, machine learning algorithms (e.g., Random Forest, support vector machines, recurssive heirarchical segmentation), and petabyte-scale big-data computing on the Google Earth Engine (GEE) cloud. Croplands as percentage of the total global cropland area (1.873 billion hectares). The product is produced for the NASA and USGS funded global food security-support analysis data @ 30-m (GFSAD30). For further details visit:
https://lpdaac.usgs.gov/about/news_archive/release_gfsad_30_meter_cropland_extent_products
(Courtesy: Teluguntla, P., Thenkabail, P.S, Xiong, J., and Oliphant, A.)
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Date published: April 12, 2018Status: CompletedAutomated Cropland Mapping Algorithm (ACMA) for Africa using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI Time-series
This repo contains an automated cropland mapping algorithm (ACMA) for Africa using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI Time-series dataset, implemented by Google Earth Engine (GEE) JavaScript API. In general, this exact code was used by Jun Xiong to generate 250m global cropland product (GCP250m) for continental Africa....
Attribution: Western Geographic Science Center -
Date published: April 10, 2018Status: CompletedAutomated Cropland Classification Algorithm (ACCA) for Australia using MODIS 250-m Time-series
We have provided here automated cropland classification algorithm (ACCA) model (Figure 1) for Australia along with sample: (a) MODIS 250-m time-series data used to produce it, (b) cropland masks, and (c) output cropland product (Figure 2), and a readme file. User’s can download the ACCA algorithm (Figure 1, in .gmd format downloadable link below) that is in ERDAS Imagine modeler compatible...
Attribution: Western Geographic Science Center -
Date published: April 9, 2018Status: CompletedGFSAD30 - Archives
Automated Cropland Classification Algorithm (ACCA) is rule-based methodology for classifying cropland areas as well irrigated versus rainfed croplands areas using satellite remote sensing data. The algorithm has been applied for Tajikistan (http://www.mdpi.com/2072-4292/4/10/2890) and California (...
Attribution: Western Geographic Science Center
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Year Published: 2019Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud
The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland...
Murali Krishna Gumma; Thenkabail, Prasad; Pardhasaradhi Teluguntla; Oliphant, AdamAttribution: Western Geographic Science Center
Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud
The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland...
Murali Krishna Gumma; Thenkabail, Prasad; Pardhasaradhi Teluguntla; Oliphant, Adam Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine
Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district)...
Oliphant, Adam; Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi; Xiong, Jun; Gumma, Murali Krishna; Russell G. Congalton; Kamini Yadav Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine
A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping...
Xiong, Jun N.; Thenkabail, Prasad S.; James C. Tilton; Gumma, Murali Krishna; Teluguntla, Pardhasaradhi G.; Oliphant, Adam; Congalton, Russell G.; Yadav, Kamini; Gorelick, Noel MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types
Innovative, open, and rapid methods to map crop types over large areas are needed for long-term cropland monitoring. We developed two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific...
Massey, Richard; Sankey, T.T; Congalton, Russ; Yadav, Kamini; Thenkabail, Prasad; Ozdogan, Mutlu; Meador, Sanchez A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform
Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are...
Teluguntla, Pardhasaradhi; Thenkabail, Prasad S.; Oliphant, Adam; Xiong, Jun N.; Gumma, Murali Krishna; Congalton, Russell G.; Yadav, Kamini; Huete, Alfredo Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data
Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the...
Teluguntla, Pardhasaradhi G.; Thenkabail, Prasad S.; Xiong, Jun N.; Gumma, Murali Krishna; Congalton, Russell G.; Oliphant, Adam; Poehnelt, Justin; Yadav, Kamini; Rao, Mahesh N.; Massey, Richard Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused...
Xiong, Jun N.; Thenkabail, Prasad S.; Gumma, Murali Krishna; Teluguntla, Pardhasaradhi G.; Poehnelt, Justin; Congalton, Russell G.; Yadav, Kamini; Thau, David Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data
The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing...
Gumma, Murali Krishna; Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Rao, Mahesh N.; Mohammed, Irshad A.; Whitbread, Anthony M. Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities
The precise estimation of the global agricultural cropland- extents, areas, geographic locations, crop types, cropping intensities, and their watering methods (irrigated or rainfed; type of irrigation) provides a critical scientific basis for the development of water and food security policies (Thenkabail et al., 2012, 2011, 2010). By year 2100,...
Teluguntla, Pardhasaradhi G.; Thenkabail, Prasad S.; Xiong, Jun N.; Gumma, Murali Krishna; Giri, Chandra; Milesi, Cristina; Ozdogan, Mutlu; Congalton, Russ; Tilton, James; Sankey, Temuulen Tsagaan; Massey, Richard; Phalke, Aparna; Yadav, Kamini Global land cover mapping: a review and uncertainty analysis
Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of...
Congalton, Russell G.; Gu, Jianyu; Yadav, Kamini; Thenkabail, Prasad S.; Ozdogan, Mutlu Hyperspectral Remote Sensing of Vegetation and Agricultural Crops
No abstract available.
Thenkabail, Prasad S.; Gumma, Murali Krishna; Teluguntla, Pardhasaradhi G.; Ilyas, Mohammed Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm
Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable...
Wu, Zhuoting; Thenkabail, Prasad S.; Mueller, Rick; Zakzeski, Audra; Melton, Forrest; Johnson, Lee; Rosevelt, Carolyn; Dwyer, John; Jones, Jeanine; Verdin, James P.-
Date published: April 22, 2017Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2010 North America 30 m
Massey, R., Sankey, T.T., Yadav, K., Congalton, R.G., Tilton, J.C., Thenkabail, P.S. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30m for North America: Cropland Extent Product (GFSAD30NACE). NASA EOSDIS Land Processes DAAC.
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 22, 2017Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Europe, Central Asia, Russia, Middle East 30 m
Phalke, A., Ozdogan, M., Thenkabail, P. S., Congalton, R. G., Yadav, K., Massey, R., Teluguntla, P., Poehnelt, J., Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for Europe, Middle-east, Russia and Central Asia: Cropland Extent Product (GFSAD30EUCEARUMECE). NASA EOSDIS Land Processes...
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 22, 2017Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South Asia, Afghanistan, Iran 30 m
Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Oliphant, A.J., Xiong, J., Congalton, R. G., Yadav, K., Phalke, A., Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for South Asia, Afghanistan and Iran: Cropland Extent Product (GFSAD30SAAFGIRCE). NASA EOSDIS Land Processes DAAC.
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 22, 2017Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast and Northeast Asia 30 m
Oliphant, A. J., Thenkabail, P. S., Teluguntla, P., Xiong, J. Congalton, R. G., Yadav, K., Massey, R., Gumma, M.K., Smith, C. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for Southeast & Northeast Asia: Cropland Extent Product (GFSAD30SEACE). NASA EOSDIS Land Processes DAAC.
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 22, 2017Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Validation 30 m
Congalton, R. G., Yadav, K., McDonnell, K., Poehnelt, J., Stevens, B., Gumma, M. K., Teluguntla, P., Thenkabail, P.S. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m: Cropland Extent Validation (GFSAD30VAL). NASA EOSDIS Land Processes DAAC.
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 22, 2017Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South America 30 m
Zhong, Y., Giri, C., Thenkabail, P.S., Teluguntla, P., Congalton, R. G., Yadav, K., Oliphant, A. J., Xiong, J., Poehnelt, J., and Smith, C. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for South America: Cropland Extent Product (GFSAD30SACE). NASA EOSDIS Land Processes DAAC.
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 16, 2017Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m
Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Congalton, R. G., Oliphant, A. J., Sankey, T., Poehnelt, J., Yadav, K., Massey, R., Phalke, A., Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD)
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 16, 2017Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m
Xiong, J., Thenkabail, P. S., Tilton, J.C., Gumma, M. K., Teluguntla, P., Congalton, R. G., Yadav, K., Dungan, J., Oliphant, A. J., Poehnelt, J., Smith, C., Massey, R. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m Africa: Cropland Extent Product (GFSAD30AFCE).
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 22, 2016Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km
Teluguntla, P., Thenkabail, P., Xiong, J., Gumma, M., Giri, C., Milesi, C., Ozdogan, M., Congalton, R., Tilton, J., Sankey, T., Massey, R., Phalke, A., Yadav, K. (2016). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km V001 [Data set]. NASA EOSDIS Land Processes DAAC.
Attribution: Land Resources, Western Geographic Science Center -
Date published: April 22, 2016Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km
Thenkabail, P., Knox, J., Ozdogan, M., Gumma, M., Congalton, R., Wu, Z., Milesi, C., Finkral, A., Marshall, M., Mariotto, I., You, S., Giri, C., Nagler, P. (2016). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km V001 [Data set]. NASA EOSDIS Land Processes DAAC.
Attribution: Land Resources, Western Geographic Science Center
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Date published: April 13, 2018GFSAD30 Products and Webmaps
The above map is an example of the 30 meter Cropland Extent products that are available for inspection on Croplands.org. Users can explore additional data sets such as 250 meter data and 1 kilometer data. Likewise, users have the ability to zoom in to examine individual pixels.
Attribution: Western Geographic Science Center
PubTalk 8/2012 — Global Food Security in the 21st Century
--the increasing need for food production, cropland areas, and agricultural water
by Prasad Thenkabail, Research Geographer
- Worldwide demand for food will require more than one billion hectares of new cropland to feed 9 billion plus people by 2050
- Presently, of all the water used by humans over 70% goes towards
A Meta-Analysis of Global Crop Water Productivity of 3 Leading Crops
Foley_A Meta-Analysis of Global Crop Water Productivity of 3 Leading World Crops (Wheat, Corn, & Rice) in the Irrigated Areas An Assessment from Remote Sensing & Non-Remote Sensing Studies Over 3 Decades
Hyperspectral remote sensing for global croplands: GFSAD Workshop
Aneece et al. 2017. Hyperspectral remote sensing for global croplands: A global spectral library of crops. Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia
RHSeg Image Segmentation: GFSAD Workshop, Reston, VA
Tilton et al. 2017. Approaches to Incorporating RHSeg Image Segmentation into Cropland Extent Mapping. Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia
North America Croplands: GFSAD Workshop, Reston, VA
Richard Massey and Teki Sankey. 2017. North American and Mongolian Croplands. Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia.
South Asia Croplands: GFSAD Workshop, Reston, VA
Gumma et al. 2017. Cropland Mapping of South Asia, Iran and Afghanistan @ Landsat 30m using Google Earth Engine (GEE) Cloud Computing. Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia.
Southeast Asia Croplands: GFSAD Workshop, Reston, VA
Oliphant et al. 2017. GFSAD 30 Southeast Asia Cropland Presence Absence at 30m. Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia.
30m Cropland Accuracy Assessment: GFSAD Workshop, Reston, VA
Kamini Yadav and Russell Congalton 2017. 30M CROPLAND ACCURACY ASSESSMENT.Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia.
Europe, Middle-east, Russia and Central Asia Croplands: GFSAD Workshop
Aparna Phalke and Mutlu Ozdogan. 2017. Europe, Middle-east, Russia and Central Asia Croplands. Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia.
Croplands of Australia, New Zealand and China: GFSAD Workshop
Teluguntla. et al. 2017. Cropland Mapping of Australia, New Zealand and China @ Landsat 30-m using Random Forest Algorithm and Google Earth Engine (GEE) Cloud Computing. Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia.
Map of Worldwide Croplands
This map shows cropland distribution across the world in a nominal 30-meter resolution derived primarily with Landsat imagery for the year 2015. The map uses machine learning algorithms on Google Earth Engine cloud computing platform. This is the baseline product of the GFSAD30 Project. There is a total of 1.874 billion hectares (roughly 12.6 percent of the global
...Africa Croplands: GFSAD Workshop, Reston, VA
Xiong. et al. 2017. An Overview of Africa Croplands Products: Crop Extent Mapping using Google Earth Engine. Workshop on Global Food Security-Support Analysis Data @ 30 m (GFSAD30) held @ Dallas Peck Auditorium, USGS HQ, 12201 Sunrise Valley Drive, Reston, Virginia.
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Date published: November 21, 2018
The GFSAD30 project accomplished their goal!
The global croplands mapping project has succesfully mapped croplands across the entire planet in order to understand the security of food world wide. Below are many press anouncments and interviews with the project leader Prasad Thenkabail.
Attribution: Western Geographic Science Center
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Date published: August 13, 2018Arizona Researchers to Compile First Ever High Resolution Global Cropland Map
A multi-disciplinary team of researchers scattered around the country is gearing up to piece together the world’s first high-resolution map of global croplands, in a cross-institutional collaboration. The team’s goal is to answer the question, “Where is all of our food going to come from when global population reaches 9 billion people?”
Attribution: Western Geographic Science Center -
Date published: November 14, 2017
New Map of Worldwide Croplands Supports Food and Water Security
India has the highest net cropland area while South Asia and Europe are considered agricultural capitals of the world.
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Date published: August 15, 2017
GFSAD30 mentioned on USGS's Twitter page
International scientists meet in VA this week on global food security.
Attribution: Western Geographic Science Center -
Date published: January 30, 2014
NASA to Map 3.7 Billion Acres of Cropland Around the World
Scientists at NASA acknowledged a long time ago the necessity of conducting more global food security studies, whose conclusions must be used for informing policies and developing mechanisms of ensuring that the world does...
Attribution: Western Geographic Science Center -
Date published: January 9, 2014
NASA Maps Earth's Croplands from Space
It takes a lot of land to grow food for the world's seven billion people. About a third of Earth's terrestrial surface is used for agriculture. And about a third of that, in turn, is used to grow crops. Now, a new NASA-funded effort aims to map crop fields worldwide, identify what's growing where, and determine whether it's irrigated or fed by rain.
Attribution: Western Geographic Science Center -
Date published: November 25, 2012
2 billion more mouths to feed
Some dozens of international satellites are orbiting the globe, mapping the landscape and the oceans in detail closer than 1 square yard.
A chunk of the data they're collecting over the next 5 years will be of top concern to Prasad Thenkabail, a U.S. Geological Survey research geographer in Flagstaff.
Attribution: Western Geographic Science Center
