Gumma, M K and Thenkabail, P S and Panjala, P and Teluguntla, P and Yamano, T and Mohammed, I (2022) Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security. GIScience & Remote Sensing, 59 (1). pp. 1048-1077. ISSN 1943-7226
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Abstract
Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
| Item Type: | Article |
|---|---|
| Divisions: | RP-Resilient Dryland Systems |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Crop types, irrigated crop, rainfed crop, cropping intensities, South Asia, Landsat, MODIS, remote sensing |
| Subjects: | Others > Rainfed Agriculture Others > Remote Sensing Others > South Asia Others > Irrigation |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 08 May 2024 05:44 |
| Last Modified: | 08 May 2024 05:44 |
| URI: | http://oar.icrisat.org/id/eprint/12665 |
| Official URL: | https://www.tandfonline.com/doi/full/10.1080/15481... |
| Projects: | NASA MEaSUREs project |
| Funders: | U.S. Geological Survey |
| Acknowledgement: | This research was supported by The National Aeronautics and Space Administration (NASA) MEaSUREs (Making Earth System Data Records for Use in Research Environments) Program, through the NASA Research Opportunities in Space and Earth Sciences (ROSES) solicitation, and funded for a period of 5 years (1 June 2013–31 May 2018) and the Consultative Group for International Agricultural Research (CGIAR) Research Program Water, Land and Ecosystems (WLE), the CGIAR Research Program Climate Change, Agriculture and Food Security (CCAFS). We are grateful for the funding provided by the National Land Imaging (NLI) Program, Land Change Science (LCS) program, and the Core Science Systems (CSS) of the United States Geological Survey (USGS). Some of the research was conducted in the science facilities of the USGS Western Geographic Science Center (WGSC). We would like to thank to Dr Sunil Dubey, Assistant director, Mahalanobis National Crop Forecast Centre (MNCFC) for providing sub-national statistics from his Institute’s database. We would like to thank Dr Anthony Whitbread and Mr Irshad Mohammed for comments and their support. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. |
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