Synergistic retrievals of leaf area index and leaf chlorophyll content in deciduous broadleaf forests from Sentinel-2 and Landsat

Leaf area index (LAI) and leaf chlorophyll content (LCC) are essential for ecological applications. Physically based algorithms enable the synergistic retrieval of LAI and LCC, but their performance is often limited by simplifications in radiative transfer (RT) models, which can induce mutual error compensation between these two parameters. In this study, we systematically evaluate synergistic LAI and LCC retrievals for deciduous broadleaf forests from Sentinel-2 and Landsat-7/8 data by examining canopy structural representation of RT models (1D PROSAIL model and the geometric-optical RT model 5-Scale), parameterization strategies, and sensor spectral characteristics. PROSAIL is enhanced by incorporating the pure vegetation fraction (FV), canopy non-photosynthetic component fraction (FS), and a prior-knowledge (PK) constraint based on a relationship between LAI and a vegetation index; 5-Scale is enhanced with FS and PK. Our results identify limitations in canopy structural representation as a primary driver of LAI-LCC mutual error compensation. The integrated parameterization (FV + FS + PK for PROSAIL; FS + PK for 5-Scale) enables a more balanced and accurate synergistic retrieval, improving LAI and LCC accuracy by 24.0–46.3% and 12.0–45.4%, respectively, across sites and models, whereas simpler strategies tend to improve one parameter at the expense of the other. Both enhanced models show higher retrieval accuracy for LAI and LCC than the operational SL2P, and relative to SL2P-CCRS, they achieve comparable LAI with more accurate LCC. These parameterizations also reduce discrepancies between Sentinel-2 and Landsat-7/8 retrievals. We further demonstrate that an advanced leaf-spectra parameterization—accounting for all canopy materials rather than green leaves alone—is essential for capturing seasonal LAI dynamics, particularly during autumn senescence. This study underscores the advantage of synergistic retrieval for LAI and LCC and highlights the value of incorporating structural and prior-knowledge enhancements into RT models for developing robust algorithms. • Parameterization strategies enhance LAI/LCC retrieval from both PROSAIL and 5-Scale. • Integrated strategies reduce LAI-LCC error compensation in DBFs. • 5-Scale mitigates LAI and LCC signal saturation issues. • Advanced leaf-spectra modeling helps capture seasonal LAI dynamics. • Parameterization reduce discrepancies between Sentinel-2 and Landsat retrievals.