Data comparison provides 96% correlation
A journal on the mapping of dry land crops in India, including wheat, chickpea, mustard, and lentils for supporting the crop insurance program has been published by Murali Krishna Gumma, Head of Geographic Information System/ Remote Sensing (GIS/RS) Lab at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), along with his colleagues.
The team used the Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series data at 10 meter resolution with the Spectral Matching Technique (SMT). They achieved overall correlation of 96 percent when the district-wise national crop statistics and the remote sensing-based estimates were compared.
Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in dry lands where variability in crop output is higher because of varying weather conditions.
Reliable information on areas cultivated with such crops forms part of the national accounting of food production and supply in many Asian countries that are employing remote sensing tools to improve the accuracy of assessments of cultivated areas.
The journal assesses the capabilities and limitations of mapping cultivated areas in the rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The use of SMT is based on the relationship between temporal NDVI signatures and crop phenology.
