A chlorophyll-constrained semi-empirical model for estimating leaf area index using a red-edge vegetation index

The parameter of leaf area index (LAI) provides valuable information about plant growth. Most remote sensing methods for estimating LAI are based on the vegetation indices (VIs) in the visible and near-infrared (VNIR) spectral regions. Recently, these VIs calculated with one or more bands in the red-edge region become popular in estimating LAI. Since the reflectance in the red-edge spectral region is sensitive to both LAI and leaf chlorophyll content (LCC), most red-edge-based VIs (such as normalized difference red edge index, NDRE) should be used to estimate the canopy chlorophyll content (the product of LAI and LCC) in theory. When applying NDRE to estimating LAI alone, their relationship is affected by the variation in LCC. To mitigate the LCC effect on LAI estimation using NDRE, this study proposed a chlorophyll-constrained semi-empirical model (CSE). The coefficients of the CSE model were determined from the simulated dataset based on radiative transfer models, and hence were not dependent on measured data. The performance of the CSE model on LAI estimation was evaluated using simulated and measured datasets, including a crop dataset collected in China and a multi-ecosystem dataset obtained from the National Ecological Observatory Network (NEON). The results showed that the root mean square error (RMSE) obtained by the CSE model ranged from 0.58 to 0.72 m2/m2 for the simulated and measured datasets. In particular, the CSE model also exhibited wide generality across multiple vegetation types for the NEON dataset, with the RMSE in the range of 0.33–0.86 m2/m2. In comparison, without considering the LCC effect, the model would lead to clear underestimation or overestimation of LAI (RMSE = 1.26–2.11 m2/m2). These findings improved our understanding of the LCC effects on universal LAI predictive models and would facilitate the accurate mapping of LAI over large regions for monitoring plant growth status.