N: 90 S: -90 E: 180 W: -180
Description
The Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP) provides high resolution, global cropland data to assist and address food and water security issues of the twenty-first century. As an extension of the Global Food Security-support Analysis Data (GFSAD) project, LGRIP maps the world’s agricultural lands by dividing them into irrigated and rainfed croplands and calculates irrigated and rainfed areas for every country in the world. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2014-2017 time period to create a nominal 2015 product.
Each LGRIP 30 meter resolution GeoTIFF file contains a contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also available.
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Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
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File Naming Convention
The file name begins with Product Short Name (LGRIP30) followed by the Year of Acquisition (2015), the Latitude and Longitude of the lower left corner of the tile (N20E00), the Version of the data product (001), the Julian Date and Time of Processing designated as YYYYDDDHHMMSS (2023014175240), and the Data Format (tif).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Increased Dependency on Extreme Precipitation in a Warmer Climate | Ombadi, Mohammed, Nguyen, Lisa, Gaur, Srishti, Gronewold, Andrew, Reich, Peter B. | Crop/Plant Yields, Landscape Patterns, Cropland | |
| High-resolution maize yield mapping across Africa using earth observation and machine learning, deep learning, and foundation model | Halder, Krishnagopal, Ewert, Frank, Ghosh, Anitabha, Muduchuru, Kaushik, Sweet, Lily-belle, Elshawi, Radwa, Timko, Jan, Zheng, Wenhi, Alsafadi, Karam, Zhao, Gang, Maerker, Michael, Singh, Manmeet, Guoging, Lei, Gaiser, Thomas, Behrend, Dominik, Shi, Yue, Han, Liangxiu, Ryo, Masahiro, Srivastava, Amit Kumar | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Large-scale irrigation area mapping: Status and challenges | Zhu, Wanxue, Donmez, Elif, Storm, Hugo, Heckelei, Thomas, Siebert, Stefan | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Multi-model ensemble mapping of irrigated areas using remote sensing | Akbar, Muhammad Umar, Mirchi, Ali, Arshad, Arfan, Alian, Sara, Mehata, Mukesh, Taghvaeian, Saleh, Khodkar, Kasra, Kettner, Jacob, Datta, Sumon, Wagner, Kevin | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Field-scale irrigated winter wheat mapping using a novel cross-region slope length index in 3D canopy hydrothermal and spectral feature space | Zhang, Youming, Yang, Guijun, Thenkabail, Prasad S., Li, Zhenhong, Wu, Wenbin, Yang, Xiaodong, Song, Xiaoyu, Long, Huiling, Liu, Miao, Zhang, Jing, Zuo, Lijun, Meng, Yang, Gao, Meiling, Zhu, Wu | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Novel Spatiotemporal ConvLSTM-Based Cellular Automata Model for | Zhou, Ye, Qiu, Yu, Wu, Tao, Lv, Laishui | Crop/Plant Yields, Landscape Patterns, Cropland, Land Use Classes, Vegetation Cover, Reflectance | |
| GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep ... | Tian, Fuyou, Wu, Bingfang, Zeng, Hongwei, Zhang, Miao, Zhu, Weiwei, Yan, Nana, Lu, Yuming, Li, Yifan | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Global Rice Paddy Inventory (GRPI): A High-Resolution Inventory of | Chen, Zichong, Lin, Haipeng, Balasus, Nicholas, Hardy, Andy, East, James D., Zhang, Yuzhong, Runkle, Benjamin R. K., Hancock, Sarah E., Taylor, Charles A., Du, Xinming, Sander, Bjoern Ole, Jacob, Daniel J. | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Landsat-Derived Global Food Security-Support Analysis Data @ 30M (LGFSAD30) to Help Address World's Food and Water Security | Thenkabail, Prasad S., Teluguntla, Pardhasaradhi, Oliphant, Adam, Aneece, Itiya, Foley, Daniel | Crop/Plant Yields, Landscape Patterns, Cropland, Land Use Classes | |
| Landsat-Derived Rainfed and Irrigated-Area Product for Conterminous United States for the Year 2020 (LRIP30 CONUS 2020) Using Supervised and Unsupervised Machine Learning on the Cloud | Teluguntla, Pardhasaradhi, Thenkabail, Prasad S., Oliphant, Adam, Aneece, Itiya, Biggs, Trent, Gumma, Murali Krishna, Foley, Daniel, McCormick, Richard, Neelam, Rohitha, Long, Emerson, Lawton, Jake | Crop/Plant Yields, Landscape Patterns, Cropland, Land Use Classes, Vegetation Cover, Reflectance | |
| Land cover classification for Siberia leveraging diverse global land cover datasets | Beak, Munseon, Ichii, Kazuhito, Yamamoto, Yuhei, Wang, Ruci, Zhang, Beichen, Sharma, Ram C., Hiyama, Tetsuya | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Large-scale irrigation mapping at field level in Northern Germany with integrated use of Sentinel-2, Landsat 8 and Sentinel-1 time series | Ghazaryan, Gohar, Ernst, Stefan, Sempel, Farina, Nendel, Claas | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Joint assimilation of satellite soil moisture and vegetation conditions | Chakraborty, Arijit, Saharia, Manabendra | Crop/Plant Yields, Landscape Patterns, Cropland, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| An annual cropland extent dataset for Africa at 30 m spatial resolution from 2000 to 2022 | Lou, Zihang, Peng, Dailiang, Shi, Zhou, Wang, Hongyan, Liu, Ke, Zhang, Yaqiong, Yan, Xue, Chen, Zhongxing, Ye, Su, Yu, Le, Hu, Jinkang, Lv, Yulong, Peng, Hao, Zhang, Yizhou, Zhang, Bing | Crop/Plant Yields, Landscape Patterns, Cropland, Land Use/Land Cover Classification | |
| A reply to Lankford and Agol (2024). Irrigation is more than irrigating: agricultural green water interventions contribute to blue water depletion and the global water ... | Rockstrom, Johan, Barron, Jennie | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Catchment Attributes and MEteorology for Large-Sample SPATially | Knoben, Wouter J. M., Thebault, Cyril, Keshavarz, Kasra, Torres-Rojas, Laura, Chaney, Nathaniel W., Pietroniro, Alain, Clark, Martyn P. | Crop/Plant Yields, Landscape Patterns, Cropland, Land Use/Land Cover Classification, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Stratigraphic Sequence, Digital Elevation/Terrain Model (DEM), Soil Depth, Sediments, Maximum/Minimum Temperature, 24 Hour Precipitation Amount, Snow Water Equivalent, Shortwave Radiation, Vapor Pressure | |
| Artificial Neural Network Multi-layer Perceptron Models to Classify | McCormick, Richard, Thenkabail, Prasad S., Aneece, Itiya, Teluguntla, Pardhasaradhi, Oliphant, Adam J., Foley, Daniel | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Connecting the drops: A methodological challenge for the deployment of | Daudin, Kevin, Belaud, Gilles, Leauthaud, Crystele, Bouzidi, Zhour, Lejars, Caroline | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Quantifying Meltwater Contributions and Socio-Economy Impacts of Future | Liu, Hu, Wang, Lei, Chen, Deliang, Yao, Tandong, Bashir, Ahmad | Population Estimates, Socioeconomics, Crop/Plant Yields, Landscape Patterns, Cropland | |
| Remote sensing and TerraClimate datasets for wheat yield prediction using machine learning | Araghi, Alireza, Daccache, Andre | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Saltwater Intrusion Vulnerability of Soil and Groundwater Near Estuaries | Tackley, Hayden A., Kurylyk, Barret L., Lake, Craig B. | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Improved soil moisture estimation and detection of irrigation signal by incorporating SMAP soil moisture into the Indian Land Data Assimilation System (ILDAS) | Chakraborty, Arijit, Saharia, Manabendra, Chakma, Sumedha, Kumar Pandey, Dharmendra, Niranjan Kumar, Kondapalli, Thakur, Praveen K., Kumar, Sujay, Getirana, Augusto | Crop/Plant Yields, Landscape Patterns, Cropland | |
| A_OPTRAM-ET: An automatic optical trapezoid model for | Yao, Zhaoyuan, Li, Wangyipu, Cui, Yaokui | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Uncovering the impacts of depleting aquifers: A remote sensing analysis of land subsidence in Iran | Haghshenas Haghighi, Mahmud, Motagh, Mahdi | Crop/Plant Yields, Landscape Patterns, Cropland | |
| Sentinel-1 InSAR-derived land subsidence assessment along the Texas Gulf | Qiao, Xiaojun, Chu, Tianxing, Tissot, Philippe, Holland, Seneca | Crop/Plant Yields, Landscape Patterns, Cropland | |
| WorldCereal: a dynamic open-source system for global-scale, seasonal | Van Tricht, Kristof, Degerickx, Jeroen, Gilliams, Sven, Zanaga, Daniele, Battude, Marjorie, Grosu, Alex, Brombacher, Joost, Lesiv, Myroslava, Bayas, Juan Carlos Laso, Karanam, Santosh, Fritz, Steffen, Becker-Reshef, Inbal, Franch, Belen, Molla-Bononad, Bertran, Boogaard, Hendrik, Pratihast, Arun Kumar, Koetz, Benjamin, Szantoi, Zoltan | Crop/Plant Yields, Landscape Patterns, Cropland |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| Band 1 | Landsat-derived Global Rainfed and Irrigated-Cropland Product at nominal 30m of the World (LGRIP30) | N/A | uint8 | N/A | 0 to 3 | N/A | N/A |