Fine-scale leaf chlorophyll distribution across a deciduous forest through two-step model inversion from Sentinel-2 data

Abstract

Leaf chlorophyll content (LCC) is a key physiological trait and is crucial for monitoring plant health and accurately modeling the terrestrial carbon cycle. However, spatially-continuous information on LCC variability at fine time-steps, and at fine spatial resolutions across regional spatial extents, is sparse. In this study, we improved a physically-based, two-step inversion approach by using an advanced canopy-to-leaf reflectance conversion model to estimate LCC at fine spatial resolution (20 m) from Sentinel-2 Multi-Spectral Instrument (MSI) data. The first step is to convert MSI canopy reflectance to leaf reflectance using look-up tables constructed from a geometric optical model (4-Scale). The second step is to estimate LCC from the modeled leaf reflectance using the PROSPECT-5 leaf optical model. Both leaf reflectance and LCC derived from MSI were validated against field measurements at a mixed temperate forest site in Canada to examine the accuracy of leaf area index (LAI) and LCC retrievals. The results demonstrate robust canopy-level inversions with strong relationships between measured and MSI-derived leaf reflectance (R2 = 0.995, p < 0.001, RMSE = 0.0143). The modeled LCC results were also strong when compared to measured LCC samples: R2 = 0.849, p < 0.001, and RMSE = 0.304 μg/cm2, respectively. The most important Sentinel-2 MSI band for LAI and LCC derivation was centered at 705 nm (Band 5). Importantly, this two-step radiative transfer inversion approach substantially improved upon the current LAI and LCC algorithms adopted by the Sentinel-2 Application Platform, which underestimated LAI by 52.93% and overestimated LCC by 44.45%. This work highlights the potential of the physically-based two-step inversion method for deriving leaf and canopy traits from Sentinel-2 at very fine spatial and temporal resolutions, for a wide range of terrestrial ecological applications.