Abstract
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 (Chlleaf) 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 Chlleaf product, instead of setting fixed values across a growing season. An algorithm to invert LAI with the dynamic SSA of the red band (SSAred) 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.
Highlights
The leaf area index (LAI), defined as the one-sided green leaf area per unit ground area in broadleaf canopies [1,2], is a key parameter in models of climate, meteorology, hydrology, biogeochemistry, and ecosystem productivity to characterize vegetation canopy structure [3,4,5,6]
The SSA of the red band (SSAred) decreased with increasing Chlleaf for all biomes
An LAI inversion algorithm was proposed by considering the leaf optical properties in 3D-radiative transfer models (RTMs)
Summary
The leaf area index (LAI), defined as the one-sided green leaf area per unit ground area in broadleaf canopies [1,2], is a key parameter in models of climate, meteorology, hydrology, biogeochemistry, and ecosystem productivity to characterize vegetation canopy structure [3,4,5,6]. The methods to estimate LAI can be classified into three major types: the empirical transfer method [8], the physical model inversion method [9,10], and the machine learning method [11,12,13]. The physical model inversion method, owing to its accurate physical mechanism and better transferability, is more suitable and stable for larger-scale LAI estimation [10,17]. Machine learning methods generally use physical model simulations or physical model-derived LAI products to train the neural network, and retrieve the LAI [12,13], and it is more of a black-box, tracking down sources of Remote Sens.
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