A new crop health measure for potatoes, derived from satellite and weather-based data, promises to minimize discrepancies between actual crop losses and insurance payouts, ensuring more precise compensation.
Researchers from multiple institutions, including the Mahalanobis National Crop Forecast Centre in Delhi, developed this measure using data from Sentinel-1 and Sentinel-2 satellites, weather datasets, and mobile app-based field data. They created a composite index called the Crop Health Factor (CHF).
The CHF incorporates several indicators of crop health, such as the Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI).
Validated with data from 2016 to 2019, the CHF was tested on potato crop losses in West Bengal during the 2020 cultivation season to calculate insurance payouts.
Potato crops have unique spectral response patterns due to their phenology, duration, and rapid growth with dense foliage. This distinctiveness aids in producing accurate satellite-based crop maps.
The study revealed that the CHF closely matched traditional yield estimates, demonstrating its potential as a reliable proxy for crop performance. The method was implemented across 500,000 hectares of potato fields in West Bengal, covering about 1,000 insurance units.
Researchers believe that using the CHF can address the primary drawback of physical yield measurements, where under-reported yields lead to exaggerated insurance payouts.
They suggest that the index could be further improved by integrating additional features.
