Evidence of a bias-variance trade off when correcting for bias in Sentinel 2 forest LAI retrievals using radiative transfer models

Forest canopies exhibit spatial heterogeneity that impacts the relationship between essential climate variables such as leaf area index (LAI) or the fraction of absorbed photosynthetically active radiation (fAPAR) and bi-directional surface reflectance, and subsequently the estimation of these variables from satellite measurements. The Simplified Level 2 Prototype Processor (SL2P) allows global LAI and fAPAR mapping at 20 m resolution using Sentinel 2 imagery. Previous validation studies over forests indicate SL2P underestimates LAI by up to 50% in comparison to in-situ reference measurements. Our study tests the hypothesis that the SL2P LAI and fAPAR bias can be reduced by replacing the spatially homogenous SAILH canopy radiative transfer model used to calibrate SL2P with the heterogenous 4SAIL2 model, together with a shoot clumping parameterization. We also hypothesized that the additional parameters involved in this new version of SL2P (SL2P-CCRS) would lead to an increase in precision error and subsequently a bias-variance trade off. SL2P-CCRS reduced LAI bias by 65%, in comparison to SL2P, during direct validation with 1107 in-situ measurements. The LAI absolute bias reduced by ∼0.5 at LAI 3 and by ∼1 at LAI 6. SL2P-CCRS reduced fAPAR bias by 31% compared to SL2P but