Evidence of a bias-variance trade off when correcting for bias in Sentinel 2 forest LAI retrievals using radiative transfer models. Fernandes, R., Djamai, N., Harvey, K., Hong, G., MacDougall, C., Shah, H., & Sun, L. Remote Sensing of Environment, 305:114060, May, 2024.
Evidence of a bias-variance trade off when correcting for bias in Sentinel 2 forest LAI retrievals using radiative transfer models [link]Paper  doi  abstract   bibtex   
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 \textless0.05 on an absolute basis. Bias reduction was accompanied by an increase in precision error so that overall uncertainty, quantified by the root mean square difference in comparison to in-situ measurements, reduced by only 6% for LAI and 5% for fAPAR. These findings support the hypothesis that updating SL2P with a spatially heterogeneous RTM can reduce LAI and fAPAR bias over forests. The results also support the hypothesis that there is a bias-variance trade-off for LAI, and to a lesser extent for fAPAR, when increasing the complexity of SL2P by using a radiative transfer model that accounts for spatial heterogeneity. Nevertheless, SL2P-CCRS increased the agreement rate with Global Climate Observing System uncertainty requirements from 52% to 58% for LAI and 32% to 40% for fAPAR, suggesting that the trade-off is worthwhile, and that algorithms such as SL2P-CCRS, that use a spatially heterogenous radiative transfer model, should be applied for mapping fAPAR and LAI from Sentinel-2 measurements.
@article{fernandes_evidence_2024,
	title = {Evidence of a bias-variance trade off when correcting for bias in {Sentinel} 2 forest {LAI} retrievals using radiative transfer models},
	volume = {305},
	issn = {0034-4257},
	url = {https://www.sciencedirect.com/science/article/pii/S0034425724000713},
	doi = {10.1016/j.rse.2024.114060},
	abstract = {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 {\textless}0.05 on an absolute basis. Bias reduction was accompanied by an increase in precision error so that overall uncertainty, quantified by the root mean square difference in comparison to in-situ measurements, reduced by only 6\% for LAI and 5\% for fAPAR. These findings support the hypothesis that updating SL2P with a spatially heterogeneous RTM can reduce LAI and fAPAR bias over forests. The results also support the hypothesis that there is a bias-variance trade-off for LAI, and to a lesser extent for fAPAR, when increasing the complexity of SL2P by using a radiative transfer model that accounts for spatial heterogeneity. Nevertheless, SL2P-CCRS increased the agreement rate with Global Climate Observing System uncertainty requirements from 52\% to 58\% for LAI and 32\% to 40\% for fAPAR, suggesting that the trade-off is worthwhile, and that algorithms such as SL2P-CCRS, that use a spatially heterogenous radiative transfer model, should be applied for mapping fAPAR and LAI from Sentinel-2 measurements.},
	urldate = {2024-08-12},
	journal = {Remote Sensing of Environment},
	author = {Fernandes, Richard and Djamai, Najib and Harvey, Kate and Hong, Gang and MacDougall, Camryn and Shah, Hemit and Sun, Lixin},
	month = may,
	year = {2024},
	keywords = {North American Forests},
	pages = {114060},
}
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