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
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 intensi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2022.2088651 |
| _version_ | 1797679010478555136 |
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| author | Murali Krishna Gumma Prasad S Thenkabail Pranay Panjala Pardhasaradhi Teluguntla Takashi Yamano Ismail Mohammed |
| author_facet | Murali Krishna Gumma Prasad S Thenkabail Pranay Panjala Pardhasaradhi Teluguntla Takashi Yamano Ismail Mohammed |
| author_sort | Murali Krishna Gumma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T23:08:11Z |
| format | Article |
| id | doaj.art-25caed17dba64b93b6d1031e9d4d78d5 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:11Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-25caed17dba64b93b6d1031e9d4d78d52023-09-21T12:43:08ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262022-12-015911048107710.1080/15481603.2022.20886512088651Multiple 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 securityMurali Krishna Gumma0Prasad S Thenkabail1Pranay Panjala2Pardhasaradhi Teluguntla3Takashi Yamano4Ismail Mohammed5International Crops Research Institute for the Semi-Arid TropicsWestern Geographic Science CenterInternational Crops Research Institute for the Semi-Arid TropicsBay Area Environmental Research Institute (BAERI)Asian Development Bank (ADB)International Crops Research Institute for the Semi-Arid TropicsCropland 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.http://dx.doi.org/10.1080/15481603.2022.2088651crop typesirrigated croprainfed cropcropping intensitiessouth asialandsatmodisremote sensing |
| spellingShingle | Murali Krishna Gumma Prasad S Thenkabail Pranay Panjala Pardhasaradhi Teluguntla Takashi Yamano Ismail Mohammed 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 crop types irrigated crop rainfed crop cropping intensities south asia landsat modis remote sensing |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | crop types irrigated crop rainfed crop cropping intensities south asia landsat modis remote sensing |
| url | http://dx.doi.org/10.1080/15481603.2022.2088651 |
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