A novel red‐edge spectral index for retrieving the leaf chlorophyll content

The leaf chlorophyll content (Chl(leaf)) is a crucial vegetation parameter in carbon cycle modelling and agricultural monitoring at local, regional and global scales. The red-edge spectral region is sensitive to variations in Chl(leaf.) An increasing number of sensors are capable of sampling red-edge bands, providing opportunities to estimate Chl(leaf). However, the contributions of canopy/foliar/soil factors are always combined in the reflectance signal, which limits the generalizability of vegetation index (VI)-based Chl(leaf) inversions. This study aims to propose a new red-edge chlorophyll index to decouple the effects of the canopy and soil background from the Chl(leaf) estimation. The chlorophyll sensitive index (CSI) was proposed, and the regression equations between the CSI and Chl(leaf) were acquired using PROSAIL (PROSPECT + SAIL) and the 4-Scale-PROSPECT model. Sensitivity analyses showed that the CSI is resistant to variations in the canopy structure and soil background. Validation results obtained using 308 ground-measured samples over nine sites world-wide revealed that CSI improves the Chl(leaf) retrieval accuracy (root mean square error (RMSE = 9.39 mu g cm(-2)) compared with the existing Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI; RMSE = 13.00 mu g cm(-2)). Moreover, the CSI method steadily achieves a highly accurate inversion under different LAI and Chl(leaf) conditions. Based on the CSI regression method, a Chl(leaf) product with a 30-m/10-day resolution across China was generated. The CSI is sensitive to Chl(leaf) but resistant to canopy structure and soil moisture parameters, and it has the potential to explicitly retrieve leaf-scale biochemistry in ecosystem modelling and ecological applications.