Estimation of Leaf Area Index for Wheat Crop Using Sentinel-2 Satellite Data

The knowledge of biophysical parameters is very crucial in making a strategy and managing precision agriculture practices. The leaf area index (LAI) is an essential biophysical parameter that helps in partitioning crop evapotranspiration into evaporation and transpiration and yield modeling and prediction. LAI varies with space and time, and optical remote sensing is an irreplaceable method in estimating spatio-temporal LAI. In addition, the various vegetation indices are helpful in assessing crop health and density, photosynthetic activities, leaf structure, and crop senescence. The present study has utilized the Sentinel-2 data to estimate the LAI through the SNAP software for the farmers' field (for wheat crop) in the Delhi-NCR region of India. The LAI estimated through the remote sensing technique is compared with the LAI measured using SunScan, a device manufactured by Delta-T, and these two matched accurately. Further, the estimated LAI through SNAP was correlated with various vegetation indices like normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE1, NDRE2, and NDRE3), re-normalized vegetation index (RDVI), and chlorophyll red-edge index (CIRed-Edge and CIRed-Edge-1). These vegetation indices were also estimated through remote sensing techniques. The coefficient of determination (R-2) between LAI and NDVI was 0.86. The R-2 between LAI and NDRE1 was 0.92, whereas the R-2 between LAI and NDRE2 was 0.70, and that between LAI and NDRE3 was 0.31. Further, the R-2 between LAI and RDVI was 0.88. The R-2 between LAI and CIRed-Edge was 0.88, whereas the R-2 between LAI and CIRed-Edge-1 was 0.74.