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

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...

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Bibliographic Details
Main Authors: Murali Krishna Gumma, Prasad S Thenkabail, Pranay Panjala, Pardhasaradhi Teluguntla, Takashi Yamano, Ismail Mohammed
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:GIScience & Remote Sensing
Subjects:
crop types
irrigated crop
rainfed crop
cropping intensities
south asia
landsat
modis
remote sensing
Online Access:http://dx.doi.org/10.1080/15481603.2022.2088651

Internet

http://dx.doi.org/10.1080/15481603.2022.2088651

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