Estimating Leaf Area Index with Dynamic Leaf Optical Properties

Leaf area index (LAI) plays an important role in models of climate, hydrology, and ecosystem productivity. The physical model-based inversion method is a practical approach for large-scale LAI inversion. However, the ill-posed inversion problem, due to the limited constraint of inaccurate input parameters, is the dominant source of inversion errors. For instance, variables related to leaf optical properties are always set as constants or have large ranges, instead of the actual leaf reflectance of pixel vegetation in the current model-based inversions. This paper proposes to estimate LAI with the actual leaf optical property of pixels, calculated from the leaf chlorophyll content (Chl(leaf)) product, using a three-dimensional stochastic radiative transfer model (3D-RTM)-based, look-up table method. The parameter characterizing leaf optical properties in the 3D-RTM-based LAI inversion algorithm, single scattering albedo (SSA), is calculated with the Chl(leaf) product, instead of setting fixed values across a growing season. An algorithm to invert LAI with the dynamic SSA of the red band (SSA(red)) is proposed. The retrieval index (RI) increases from less than 42% to 100%, and the RMSE decreases to less than 0.28 in the simulations. The validation results show that the RMSE of the dynamic SSA decreases from 1.338 to 0.511, compared with the existing 3D-RTM-based LUT algorithm. The overestimation problem under high LAI conditions is reduced.

Automated summary

The paper proposes a method to estimate LAI using the actual leaf optical property of pixels, calculated from the leaf chlorophyll content product, with a 3D-RTM-based look-up table method. By utilizing dynamic SSA based on the Chl(leaf) product, the inversion algorithm shows improvement in retrieval index and RMSE in simulations, reducing overestimation problems under high LAI conditions.