A simple method for estimation of leaf dry matter content in fresh leaves using leaf scattering albedo

Leaf dry matter content (known as leaf mass per area, LMA) is a key parameter for studies of terrestrial ecosystem monitoring and plant species identification. Remote sensing of LMA has been reported to be difficult because of the predominate absorption of water and uncertainty of scattering modeling in the infrared spectral region, which in turn causes large errors in estimation of LMA. In this study, we introduced a new approach for LMA estimation using leaf scattering albedo, rather than leaf reflectance and/or transmittance as in many other studies. A wavelength-invariant modification factor was added to the output of the PROSPECT-5 model for a better simulation of leaf optics in the strong scattering spectral region and thus decreasing the error in LMA estimation. The new approach is simple-to-use because no modifications are made to the original PROSPECT-5 model. Our results suggest that, the new approach is as accurate as the multistage inversion approach, which was built using the PROSPECT-g model. Compared to the standard approach, the new approach can reduce the errors (in terms of root-mean-square error) in LMA estimation by 66% and 54% when using LOPEX and Angers datasets, respectively. Leaf scattering albedo reconstruction using the new approach was also improved. The new approach is insensitive to the leaf structure parameter and it can account for leaf scattering uncertainties. The new approach is thus helpful for the accurate estimation of leaf dry matter content in fresh leaves. (C) 2020 The Author(s). Published by Elsevier B.V.