Estimating Mangrove Leaf Area Index from Sentinel-2 Imagery Using Inform Radiative Transfer Model

Mangroves play pivotal roles in ecosystem services, but anthropogenic pressures contribute to their alarming degradation. Precise quantification of vital vegetation characteristics, particularly leaf area index (LAI), is crucial for effective monitoring. LAI serves as a key biophysical parameter in assessing vegetation structure, eco-physiological processes, and overall health. This study pioneers the exploration of a hybrid model (combining radiative transfer with machine learning) for LAI estimation in mangroves. Employing (INvertible FOrest Reflectance Model) INFORM and support vector regression in Bhitarkanika Wildlife Sanctuary, India, we utilized digital hemispherical photographs and Sentinel-2 optical data. Results reveal INFORM's superior retrieval capacity (RMSE = 2.56 m
²
.m
⁻²
) over the PROSAIL model (RMSE = 0.73 m
²
.m
⁻²
). The study underscores the efficacy of physical-based models, particularly INFORM, in accurate LAI estimation, particularly in challenging environments like mangrove ecosystems where in-situ data collection is constrained.