Multi-Sensor Remote Sensing Data Integration for Agricultural Droughts: A PRISMA-Based Review in the Indian Context

Ensuring a minimum crop yield becomes crucial with a major part of India’s population still employed in the agriculture sector. To ensure better crop yields and food security, especially in developing countries like India, it is important to utilise the latest cutting-edge technologies that are labour- and cost-saving. Multi-sensor remote sensing data integration solves the problem of field validation so that what could not be captured by one sensor could be easily captured by the other sensor. With the advancement in remote sensing technology and the launch of a multitude of remote sensing satellites operating in different regions of the electromagnetic spectra, the reliability of the findings from remote sensing datasets has increased in the last few years. Today, it is important to lay more stress on the successful integration of multispectral, thermal and Synthetic Aperture RADAR (SAR) datasets for temporal monitoring of soil and crop health and take immediate actions for mitigation of any abnormality in crop or soil health parameters. Multi-sensor remote sensing data integration combines the findings of two or more remote sensing datasets thus making the studies more reliable and reducing dependency on field data collection. The best example of this is soil moisture estimation by integrating the moisture indices from multi-spectral remote sensing datasets with the Synthetic Aperture RADAR (SAR) backscatter of remotely sensed data since the SAR signals are sensitive to moisture. There have been several studies for agricultural drought mapping and modelling in the country using various remotely sensed data and Agro-climatic parameters. This paper utilises the PRISMA technique to scientifically review the remote sensing-based studies conducted in India for agricultural droughts. This paper presents the need for multi-sensor remotely sensed data integration for more precise, accurate and less field-dependent agricultural drought assessment.