Sentinel-2 Derived Indices to Understand Rice Vegetation Dynamics at Pixel Level

Freely available Sentinel-2 data and software, SNAP, with several processors has enormous application potential for monitoring agriculture. A sum of 121 pixels were extracted by a boundary vector a rice field from 12 Sentinel-2 L2A images covering the period from transplantation (January 23rd) to little after harvest (May 23rd) to monitor the crop during Rabi 2022. The derived vegetation indices including normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), and normalized difference red edge (NDRE) were extracted from these pixels. Mean indices showed a bell-shaped curve with peak of NDVI (0.73) and GNDVI (0.71) on April 18th and SAVI (0.39) and NDRE (0.48) on April 13th indicating the general pattern of growth. Range, the difference between minimum and maximum, varied between 0.21 (May 23rd) and 0.42 (April 13th) in NDVI. Such differences in range were noticed in other VIs too. These variations at different times of image acquisition highlight that all the pixels were not uniform in crop growth all the times as indicated by VIs, indicating stress in pixels/patches of field. Correlation of NDVI with GNDVI, SAVI and NDRE; of GNDVI with SAVI and NDRE; and of SAVI with NDRE were positively significant and strong. Although correlated well, each index may indicate a specific response to the given situation necessitating the inclusion in the study. Experience suggested that individual fields and certain trials under All India Coordinated Research Project on Rice (AICRPR) with some minimum pixels could be temporally monitored using satellite data for better management.