Global Food-and-Water Security-support Analysis Data (GFSAD)
The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland dynamics from 2000 to 2030
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 this NASA MEaSUREs GFSAD project is to develop global or large area agricultural cropland products at fine spatial resolution of 30m or better using multiple satellite sensor time-series data, big data analytics, machine learning and cloud computing in support of global food and water security in the twenty-first century. Specific objectives will be to produce four distinct cropland products for nominal years 2020 and 2025. The study will make use of a fusion of Landsat-8, 9, and Sentinel-2A&2B (S2) surface reflectance (SR) products already available in GEE, and NASA’s Harmonized Landsat Sentinel-2 (HLS) Landsat product (HLSL30) for 2013-present and HLS Sentinel-2 product (HLSS30) for 2015-present, that together have sub-5-days global coverage (Masek, et al., 2021, 2022) at nominal 30m resolution (Figure 4). These products are:
1. Landsat-derived Global cropland extent product @ 30m (LGCEP30-2020, LGCEP30-2025).
2. Landsat-derived Global rainfed & irrigated product @ 30m (LGRIP30-2020, LGRIP30-2025).
3. Landsat-derived Global cropping intensity product @ 30m (LGCIP30-2020, LGCIP30-2025).
4. Landsat-derived Global crop type product @ 30m for USA & Canada, and India (LGCTY30-2020USACAN, LGCTY30-2025USACAN, LGCTY30-025India).
The HLSL30 has 10 bands (aerosol, B, G, R, NIRnarrow, SWIR1, SWIR2, cirrus, TIR1, TIR2), and the HLSS30 S2 derived product has 13 bands (aerosol, B, G, R, RE1, RE2, RE3, NIRbroad, NIRnarrow, SWIR1, SWIR2, water vapor, cirrus) (Masek, et al., 2022) along with Fmask cloud mask band and other quality layers. In the earlier global cropland extent product @ 30m (GCEP30) Landsat 8/16-day data were used to time-composite 10-band (blue, green, red, NIR, SWIR1, SWIR2, TIR, EVI, NDWI, NDVI) Landsat 30m data cubes for every 2-4 months over a 3–4-year period (for nominal year 2015) along with 2 additional 30m bands (SRTM elevation and slope). However, the HLSL30 and HLSS30 provide significant advances such as the sub-5-day (2-3 days optimal possible as per Masek et al., 2022) coverage, presence of red-edge bands, many possibilities of data transformations like the vegetation indices, and improved processing and consistency of the HLS data (Masek et al., 2022, Wulder et al., 2019, Roy et al., 2017). We will create analysis ready data (ARD) cubes utilizing these bands to best process each of the cropland products. The ARD cubes, processed in GEE, will involve these bands, their transformations (e.g., vegetation indices), and numerous ancillary data (e.g., climate).
Once the above products are established, other products such as the following can be derived using the above products and certain other inputs:
5) Crop productivity (productivity per unit of land; kg\m2)
6) Water productivity (crop per drop or productivity per unit of water; kg\m3).
The GFSAD has released two global products that can be downloaded from NASA's LP DAAC. These products are:
- Landsat-derived Global Rainfed and Irrigated-Area Product @ 30m (LGRIP30) along with the algorithm theoretical basis document (ATBD) and User Guide:
https://lpdaac.usgs.gov/news/release-of-lgrip30-data-product/
- Landsat-derived global cropland extent product at 30m (LGCEP30) along with the algorithm theoretical basis document (ATBD) and user guide:
https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/
These products can also be viewed live at:
https://www.usgs.gov/apps/croplands/app/map
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
![The Landsat-derived Global rainfed & irrigated product @ 30m (LGRIP30) for the nominal year 2015.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/media/images/LGRIP30-irrigated-and-rainfed-croplands-v4-2d.png?itok=hxiIggWF)
![The spatial distribution of global cropland areas.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure1_0.jpg?itok=am1Rmwx6)
![Global cropland water use.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure2_0.jpg?itok=J3N-jOpF)
![Hyperspectral signature bank of world’s major crops.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure3_0.jpg?itok=g99lyMVN)
![Rice map of South Asia.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure4.jpg?itok=MQOvnDmF)
![Global croplands and other land use and land cover.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure-A.jpg?itok=gxegBnXj)
![Key global cropland area products that will support food security analysis.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure-B.jpg?itok=sjl2y2Fx)
![A disaggregated three class global cropland extent map.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/figure-C1.jpg?itok=sEQJW3tb)
![A disaggregated twelve class global cropland extent map.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/figure-C2.jpg?itok=Rs7HdvSk)
![Center image of global cropland (irrigated and rainfed) areas.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/figure-D.jpg?itok=8HwwQlkT)
![Global agricultural dynamics over 2 decades (1982-2000).](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/figure-E.jpg?itok=1QRvtccA)
![Available field data points from various sources.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Ground_reference_%20data_collection_2.jpg?itok=WVr3aAdb)
Relevant methodology including Models and Algorithms used by the group.
Below are multimedia items associated with this project.
Below are publications associated with this project.
Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine
Hyperspectral Remote Sensing of Vegetation and Agricultural Crops
Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing
Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm
Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission
Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help?
Hyperspectral remote sensing of vegetation
A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing
Remote sensing of global croplands for food security
Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium
A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields
Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data
Below are news stories associated with this project.
Release of LGRIP30 Data Product
The Land Processes Distributed Active Archive Center (LP DAAC) is pleased to announce the availability of the Landsat-Derived Global Rainfed and Irrigated-Cropland Product at 30 meters (LGRIP30). As an extension of the the Global Food Security-support Analysis Data (GFSAD) project, LGRIP30 provides high resolution, global cropland data to assist and address food and water security issues.
Below are partners associated with this project.
The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland dynamics from 2000 to 2030
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 this NASA MEaSUREs GFSAD project is to develop global or large area agricultural cropland products at fine spatial resolution of 30m or better using multiple satellite sensor time-series data, big data analytics, machine learning and cloud computing in support of global food and water security in the twenty-first century. Specific objectives will be to produce four distinct cropland products for nominal years 2020 and 2025. The study will make use of a fusion of Landsat-8, 9, and Sentinel-2A&2B (S2) surface reflectance (SR) products already available in GEE, and NASA’s Harmonized Landsat Sentinel-2 (HLS) Landsat product (HLSL30) for 2013-present and HLS Sentinel-2 product (HLSS30) for 2015-present, that together have sub-5-days global coverage (Masek, et al., 2021, 2022) at nominal 30m resolution (Figure 4). These products are:
1. Landsat-derived Global cropland extent product @ 30m (LGCEP30-2020, LGCEP30-2025).
2. Landsat-derived Global rainfed & irrigated product @ 30m (LGRIP30-2020, LGRIP30-2025).
3. Landsat-derived Global cropping intensity product @ 30m (LGCIP30-2020, LGCIP30-2025).
4. Landsat-derived Global crop type product @ 30m for USA & Canada, and India (LGCTY30-2020USACAN, LGCTY30-2025USACAN, LGCTY30-025India).
The HLSL30 has 10 bands (aerosol, B, G, R, NIRnarrow, SWIR1, SWIR2, cirrus, TIR1, TIR2), and the HLSS30 S2 derived product has 13 bands (aerosol, B, G, R, RE1, RE2, RE3, NIRbroad, NIRnarrow, SWIR1, SWIR2, water vapor, cirrus) (Masek, et al., 2022) along with Fmask cloud mask band and other quality layers. In the earlier global cropland extent product @ 30m (GCEP30) Landsat 8/16-day data were used to time-composite 10-band (blue, green, red, NIR, SWIR1, SWIR2, TIR, EVI, NDWI, NDVI) Landsat 30m data cubes for every 2-4 months over a 3–4-year period (for nominal year 2015) along with 2 additional 30m bands (SRTM elevation and slope). However, the HLSL30 and HLSS30 provide significant advances such as the sub-5-day (2-3 days optimal possible as per Masek et al., 2022) coverage, presence of red-edge bands, many possibilities of data transformations like the vegetation indices, and improved processing and consistency of the HLS data (Masek et al., 2022, Wulder et al., 2019, Roy et al., 2017). We will create analysis ready data (ARD) cubes utilizing these bands to best process each of the cropland products. The ARD cubes, processed in GEE, will involve these bands, their transformations (e.g., vegetation indices), and numerous ancillary data (e.g., climate).
Once the above products are established, other products such as the following can be derived using the above products and certain other inputs:
5) Crop productivity (productivity per unit of land; kg\m2)
6) Water productivity (crop per drop or productivity per unit of water; kg\m3).
The GFSAD has released two global products that can be downloaded from NASA's LP DAAC. These products are:
- Landsat-derived Global Rainfed and Irrigated-Area Product @ 30m (LGRIP30) along with the algorithm theoretical basis document (ATBD) and User Guide:
https://lpdaac.usgs.gov/news/release-of-lgrip30-data-product/
- Landsat-derived global cropland extent product at 30m (LGCEP30) along with the algorithm theoretical basis document (ATBD) and user guide:
https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/
These products can also be viewed live at:
https://www.usgs.gov/apps/croplands/app/map
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
![The Landsat-derived Global rainfed & irrigated product @ 30m (LGRIP30) for the nominal year 2015.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/media/images/LGRIP30-irrigated-and-rainfed-croplands-v4-2d.png?itok=hxiIggWF)
![The spatial distribution of global cropland areas.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure1_0.jpg?itok=am1Rmwx6)
![Global cropland water use.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure2_0.jpg?itok=J3N-jOpF)
![Hyperspectral signature bank of world’s major crops.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure3_0.jpg?itok=g99lyMVN)
![Rice map of South Asia.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure4.jpg?itok=MQOvnDmF)
![Global croplands and other land use and land cover.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure-A.jpg?itok=gxegBnXj)
![Key global cropland area products that will support food security analysis.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Figure-B.jpg?itok=sjl2y2Fx)
![A disaggregated three class global cropland extent map.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/figure-C1.jpg?itok=sEQJW3tb)
![A disaggregated twelve class global cropland extent map.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/figure-C2.jpg?itok=Rs7HdvSk)
![Center image of global cropland (irrigated and rainfed) areas.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/figure-D.jpg?itok=8HwwQlkT)
![Global agricultural dynamics over 2 decades (1982-2000).](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/figure-E.jpg?itok=1QRvtccA)
![Available field data points from various sources.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/thumbnails/image/Ground_reference_%20data_collection_2.jpg?itok=WVr3aAdb)
Relevant methodology including Models and Algorithms used by the group.
Below are multimedia items associated with this project.
Below are publications associated with this project.
Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine
Hyperspectral Remote Sensing of Vegetation and Agricultural Crops
Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing
Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm
Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission
Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help?
Hyperspectral remote sensing of vegetation
A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing
Remote sensing of global croplands for food security
Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium
A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields
Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data
Below are news stories associated with this project.
Release of LGRIP30 Data Product
The Land Processes Distributed Active Archive Center (LP DAAC) is pleased to announce the availability of the Landsat-Derived Global Rainfed and Irrigated-Cropland Product at 30 meters (LGRIP30). As an extension of the the Global Food Security-support Analysis Data (GFSAD) project, LGRIP30 provides high resolution, global cropland data to assist and address food and water security issues.
Below are partners associated with this project.