Evaluating Sentinel-2 red edge through hyperspectral profiles for monitoring LAI & chlorophyll content of Kinnow Mandarin orchards

Abstract

Leaf area index (LAI) and chlorophyll content are efficient plant health and nutrition indicators. However, retrieving these traits is challenging for the multilayered canopy of evergreen fruit trees like Citrus. The spectral reflectance's Red Edge Position (REP) is a surrogate measure of LAI and chlorophyll content. Sentinel-2 (20 m) estimates REP at a much higher spatial resolution than before. However, retrieval of the traits in the dense canopy structure of orchards requires proper validation of the Sentinel-2 REP (S2REP) through the hyperspectral profiles of plants. The objective here is to evaluate the potential of S2REP to retrieve the LAI and chlorophyll of kinnow mandarin fruit orchards and validate the results with REP extracted from the hyperspectral profiles and ground measurements of these traits. For this, we first tested several algorithms to enumerate the REP range from hyperspectral data that corresponds well to the kinnow mandarin's LAI and Chl content. Next, we used the S2REP to estimate these traits at sampled locations. Hence, we calibrated the regression models of LAI and chlorophyll content with the Sentinel-2 REP as an explanatory variable and traits ground measurements as response variables. The prediction accuracy was tested through an independent validation data set. The results show that the kinnow mandarin trees demonstrated significant spectral response in the 695–725 nm S2REP range of the electromagnetic spectrum similar to the spectral profiles from ground-based hyperspectral data. With this S2REP range, LAI and chlorophyll content models outperformed to predict these traits for the orchards, with adjusted-R2 0.86 and 0.80 and RMSE 3.7 and 53.3%, respectively. We conclude that the S2REP can effectively evaluate kinnow mandarin's LAI and Chlorophyll over large areas. The plants' LAI and chlorophyll content are best represented by the REP derived from the hyperspectral profiles through Polynomial fitting and Linear Extrapolation. Such spectrally-validated Sentinel-2 REP for kinnow orchards can calibrate the LAI and chlorophyll models more accurately over large scales. Our methods can be extrapolated to other fruit traits to enable the real-time monitoring of orchards' health and nutritional status.