Record Details

Spatial Products for Crop Monitoring and Sustainable Agriculture

OAR@ICRISAT

View Archive Info
 
 
Field Value
 
Relation http://oar.icrisat.org/12629/
https://sarr.co.in/2023/02/10/spatial-products-for-crop-monitoring-and-sustainable-agriculture/
https://doi.org/10.58297/WZDG6244
 
Title Spatial Products for Crop Monitoring and Sustainable Agriculture
 
Creator Gumma, M K
 
Subject GIS Techniques/Remote Sensing
Drylands Agriculture
 
Description The spatial cropland products are of great importance in water and food security assessments, especially in India, which is home to nearly 1.4 billion people and 160 million hectares of net cropland area. In India, 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., our study was aimed at producing three distinct spatial products at 30m and 250m resolution that would be useful and needed to address food and water security challenges. The first of these, Product 1, was to assess irrigated versus rainfed croplands in India using Landsat 30 m data in GEE platform. The second, Product 2, was to map major crop types using MODIS 250 m data. The third, Product 3, to map cropping intensity (single, double and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in India, Jun-Oct), 9 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), 5 major crops (3 irrigated crops: rice, wheat, maize; and 2 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 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 highlights production of multiple cropland products to support food security studies using multiple satellite sensor big-data, and RF machine learning algorithm that were coded, processed, and computed.
 
Publisher Society for Advancement of Rice Research
 
Date 2022
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_attribution
 
Identifier http://oar.icrisat.org/12629/1/Journal%20of%20Rice%20Research_15_170-178_2022.pdf
Gumma, M K (2022) Spatial Products for Crop Monitoring and Sustainable Agriculture. Journal of Rice Research, 15. pp. 170-178. ISSN 2319-3670