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 2000 to 2025.
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: 2000-2025.
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).
The GFSAD30 Cropland Extent Products are available to download for 7 regions of the world from LPDAAC at https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-… view the GDSAD product in an interactive zoomable webpage, visit https://www.usgs.gov/apps/croplands/
A global synthesis work summarizing the methods and results of the entire GFSAD30 global cropland extent product was released as the USGS Professional Paper 1868 https://pubs.er.usgs.gov/publication/pp1868
Since the release of the GFSAD30 cropland extent product, the downloads from LPDAAC and citations have been tracked and published in the Open File Report 2022-1001 at https://pubs.er.usgs.gov/publication/ofr20221001
Relevant methodology including Models and Algorithms used by the group.
Automated Cropland Mapping Algorithm (ACMA) for Africa using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI Time-series
Automated Cropland Classification Algorithm (ACCA) for Australia using MODIS 250-m Time-series
GFSAD30 - Archives
Below are multimedia items associated with this project.
Below are news stories associated with this project.
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.
- Overview
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 2000 to 2025.
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: 2000-2025.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).The GFSAD30 Cropland Extent Products are available to download for 7 regions of the world from LPDAAC at https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-… view the GDSAD product in an interactive zoomable webpage, visit https://www.usgs.gov/apps/croplands/
A global synthesis work summarizing the methods and results of the entire GFSAD30 global cropland extent product was released as the USGS Professional Paper 1868 https://pubs.er.usgs.gov/publication/pp1868
Since the release of the GFSAD30 cropland extent product, the downloads from LPDAAC and citations have been tracked and published in the Open File Report 2022-1001 at https://pubs.er.usgs.gov/publication/ofr20221001
Sources/Usage: Public Domain.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.)
Sources/Usage: Some content may have restrictions. Visit Media to see details.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
Sources/Usage: Some content may have restrictions. Visit Media to see details.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).
Sources/Usage: Some content may have restrictions. Visit Media to see details.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).
Sources/Usage: Some content may have restrictions. Visit Media to see details.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).
Sources/Usage: Some content may have restrictions. Visit Media to see details.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].
Sources/Usage: Some content may have restrictions. Visit Media to see details.Key global cropland area products that will support food security analysis in the twenty-first century. [Credits: GFSAD30 project team]
Sources/Usage: Some content may have restrictions. Visit Media to see details.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.
Sources/Usage: Some content may have restrictions. Visit Media to see details.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.
Sources/Usage: Some content may have restrictions. Visit Media to see details.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; https://www.iwmi.cgiar.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).
Sources/Usage: Some content may have restrictions. Visit Media to see details.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.
Sources/Usage: Some content may have restrictions. Visit Media to see details.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. - Science
Relevant methodology including Models and Algorithms used by the group.
Automated 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. You can also read more on ACM2016 in a paper (Xiong and...Automated 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 format...GFSAD30 - 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 and California. - Multimedia
Below are multimedia items associated with this project.
- Publications
- Web Tools
- News
Below are news stories associated with this project.
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.
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