Improving Leaf Area Index Estimation With Chlorophyll Insensitive Multispectral Red-Edge Vegetation Indices

As an essential vegetation biophysical trait that determines the plant's structure and photosynthetic capacity, characterizing of leaf area index (LAI) is important for growth health monitoring. The empirical models based on indices (VIs) from remote sensing images effective method deriving LAI. However, due to coupled impacts LAI chlorophyll content (LCC) canopy reflectance saturation effect, most VIs cannot achieve a good accuracy estimation. remotely sensed red-edge can provide valuable information delineate LAI, therefore series insensitive by using Sentinel-2 GF-6 multispectral are developed in this work improve estimation accuracy. potentials reflecting variations sensitivity LCC changes each band comprehensively analyzed PROSAIL model select optimal design. proposed then evaluated multiple ways, including with simulated datasets, ground measured spectra real satellite images. evaluation results field measurements indicate effectively crop best regression coefficient ( R 2 =0.81 =0.65 GF-6) among all comparative VIs. Our showcases incorporating bands suitable formula promising improving VI-based retrieval, they offer practicable solution fast decameter maps or