Estimation of vegetation canopy chlorophyll content based on sunlight-induced chlorophyll fluorescence

Remote sensing has proven to be an effective approach for acquiring information on regional and global vegetation canopy chlorophyll content (CCC). Existing remote sensing methods for estimating CCC often rely on vegetation indices constructed from different spectral bands. However, Sun-Induced Chlorophyll Fluorescence (SIF) emitted by chlorophyll is more sensitive to changes in chlorophyll content. This study explores the potential of SIF indices, specifically those obtained from the 771nm, 757nm, and 740nm bands of OCO-2 satellite products, to estimate vegetation canopy chlorophyll content. Correlations between SIF indices at each wavelength and CCC were investigated, and empirical models were established using both simple linear regression and multiple linear regression methods. The results indicate that the SIF indices (SIF₇₄₀, SIF₇₅₇, SIF₇₇₁) have high correlations with CCC values, with determination coefficients for the univariate regression equations being 0.5124, 0.5565, and 0.5684, respectively. The determination coefficient for the multivariate regression model considering all three SIF indices and CCC is 0.6549. Validation analysis using 185 independent samples, which were not part of the training set, confirms the high predictive accuracy of the multivariate diagnostic model based on SIF₇₄₀, SIF₇₅₇, and SIF₇₇₁ for CCC. This suggests that the inversion of vegetation canopy chlorophyll content can be well achieved based on SIF indices.