Validation of coarse spatial resolution LAI and FAPAR time series over cropland in southwest France

Abstract

This study aims at validating Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) products derived from MODIS surface reflectance (MOD09CMG) at coarse resolution (0.05°) over crops. These Essential Climate Variables (ECVs) are estimated by using the inversion of the PROSAIL radiative transfer (BV-NNET tool) applied on MODIS BRDF (Bidirectional Reflectance Distribution Function) corrected surface reflectances and non-corrected. ECV estimates and the corresponding MCD15A3 Collection 5 and GEOLAND-2 (GEOv1) products are compared with ECV reference maps derived from BV-NNET applied on 105 high spatial resolution images (Formosat-2, 8m) which were acquired from 2006 to 2010 in Southwest France. These latter are compared with local scale in situ measurements. The validation shows an uncertainty of 0.35 and 0.07 for LAI and FAPAR, respectively.The comparison shows that the ECV estimates from the three products properly capture the crops phenology in agreement with reference maps. Results indicate that MCD15A3 uncertainties (0.23 and 0.07 for LAI and FAPAR, respectively) are similar to previous intercomparison studies. GEOv1 shows a systemic positive bias for both LAI and FAPAR. The best agreement with the reference maps is found for MODIS BV-NNET products with r2 higher than 0.9 and relative uncertainties lower than 17%. The use of BRDF-corrected surface reflectances as input of BV-NNET tool improves the uncertainty of LAI estimates (0.11, compared to 0.17 when directional surface reflectances are used as input) but not the uncertainty of FAPAR estimates. The deviation between FAPAR products which mostly affects low winter FAPAR, is related to the discrepancy of the soil directional assumption in PROSAIL model and BRDF correction method. The temporal stability of the daily MODIS BV-NNET products is better than the 4-day composite MCD15A3 products. Finally, BV-NNET tool applied at finer resolutions demonstrates that the increase of the resolution results in a decrease of the LAI and FAPAR uncertainties and a conservation of the biases.