Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data. Issue 1 (1st January 2020) - Padalia, Hitendra; Sinha, Sanjiv K.; Bhave, Vipul; Trivedi, Neeraj K.; Senthil Kumar, A.
Highlights: The sensitivity of Sentinel-2 VIs to tropical mixed forest LAI and Chlorophyll tested. Study was undertaken for the leaf emergence period in the study area. In-situ LAI measurements were corrected for foliage clumpiness effect. NDI and RECI were most sensitive to LAI and Chlorophyll respectively. VIs with red-edge and NIR combinations offered best results. Abstract: With the free and full access to images from Sentinel-2 satellite, the interest to use this data for quantitative retrieval of vegetation parameters is ever-increasing. LAI and chlorophyll are two key variables which are desired for studying productivity, nutrient and stress status of vegetation. Studies carried out on croplands using simulated Sentinel-2 MSI and parametric approach have identified vegetation indices (VIs) with high sensitivity to LAI and chlorophyll. To test how Sentinel-2 red-edge based VIs perform for retrieval of LAI and Chlorophyll of tropical mixed forest canopies, this study has been performed. The field measurements of LAI and chlorophyll content were recorded in a total of 28 ESUs (Elementary Sampling Units) in Bhakra range in the Tarai Central Forest Division, Uttarakhand (India). The in-situ measurements were statistically correlated with Sentinel-2VIs and strength of correlation was validated using Predicted Residual Error Sum of Squares (PRESS) statistic. Field LAI corrected for foliage clumpiness effect improved correlation of VIs with LAI. Among all VIs tested, Normalized Difference Index (NDI) offered highest positive correlation (R 2  = 0.79, p  < 0.05) with LAI while Red-Edge Chlorophyll Index (RECI) (R 2  = 0.83, RMSE = 0.24 g/m 2, p  < 0.05) and Simple Ratio (SR) 740/705 (R 2  = 0.79, RMSE = 0.27 g/m 2, p  < 0.05) were the most closely related to chlorophyll content. VIs with red-edge and NIR combinations offered best results.