Comparing leaf area index estimates in a Mediterranean forest using field measurements, Landsat 8, and Sentinel-2 data

Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in ecosystem services’modeling,which is emerging as a critical tool for steering upcoming urban reforestation strategies.However,LAI field measures are extremely time-consuming and require remarkable economic and human resources.In this context,spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8,may represent a feasible and economic solution for estimating the LAI at the city scale.Nonetheless,as far as we know,only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems.To fill such a gap,we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI,using field measurements collected with the LI-COR LAI 2200c as a reference.We hypothesized that Sentinel-2 data,owing to their finer spatial and spectral resolution,perform better in estimating vegetation’s structural parameters compared to Landsat 8.Results We found that Landsat 8-derived models have,on average,a slightly better performance,with the best model(the one based on NDVI)showing an R^(2) of 0.55 and NRMSE of 14.74%,compared to R^(2) of 0.52 and NRMSE of 15.15%showed by the best Sentinel-2 model,which is based on the NBR.All models were affected by spectrum saturation for high LAI values(e.g.,above 5).Conclusion In Mediterranean ecosystems,Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season.Therefore,the uncertainty introduced using satellite-derived LAI in ecosystem services’assessments should be systematically accounted for.