PROSAIL and empirical model to evaluate spatio-temporal heterogeneity of canopy chlorophyll content in subtropical forest

Abstract

Canopy Chlorophyll Content (CCC) is the most important biophysical parameter in a forest eco-system, since it determines plant production, stress, and photosynthetic capability. Plant adaptation monitoring in a changing environment necessitates analyzing long-term changes in CCC. Present study computes CCC using the PROSAIL Radiative Transfer Model Inversion (RTM) with Artificial Neural Networking (ANN). The data from the Sentinel 2A satellite was utilized for this purpose. The various vegetation indices (VIs) were derived from Landsat and Sentinel data. The Infrared Percentage vegetation index (IPVI) from Landsat 8 demonstrated a strong connection with CC (R2\u2009=\u20090.8). Green vegetation index (GVI), Normalized difference index (NDI), and Pigment specific simple ratio index (PSSRa) exhibited good correlations with CCC. From 1997 to 2017, the correlation of IPVI with CCC was utilized to model the spatio-temporal variation of CCC. The negative trend and decrease of CCC was detected at a rate of − 1.2 g cm−2 year−1 throughout this 20-year period with 33% fall in chlorophyll concentration, indicating a substantial reduction in forest health. The primary differences observed in dense forest area CCC and change in agricultural CCC were minor. This dramatic drop in chlorophyll concentration creates a variety of photosynthetic vulnerabilities in the forest ecosystem, resulting in forest degradation that may have unintended consequences for humans and wildlife.