LAUSR

Plant leaf chlorophyll content (LCC) plays a key role in the assessment of plant stress and plant functioning. To date, accurate estimation of LCC over a wide range of plant species (trees, bushes, and lianas) under different measurement conditions is still challenging for nondestructive methods. Based on multiangular hyperspectral reflection of 706 leaves (ten plant species), several popular spectral indices were evaluated for a general estimation of LCC. The modified difference ratio index (MDRI) had the strongest linear relationship ( $R^{2}=0.92$ ) to LCC among all the tested spectral indices. The regression algorithm was then used to estimate LCC in other datasets from different regions across the globe. Comparing with the machine learning techniques and PROSPECT model, validation results from 2024 leaves (114 plant species) confirmed that the linear algorithm derived from the MDRI was the most effective for estimating LCC (root-mean-square error (RMSE) $=6.72\,\,\mu \text{g}$ /cm2) across a wide range of plant species under different measurement conditions. The MDRI does not require parameterization for each plant species and has the potential to estimate LCC from a simple handheld laboratory or field instrument at any arbitrary direction. The generality of the approach makes it convenient for botanical and ecological studies under different measurement conditions that need accurate LCC estimates.