Assessing vegetation traits estimates accuracies from the future SBG and biodiversity hyperspectral missions over two Mediterranean Forests

The estimation and mapping of vegetation traits from satellite hyperspectral imagery is entering a new era, as multiple missions have recently started and more are currently in preparatory phase. With expected ground sampling distances (GSD) ranging from 8 to 30 m, these missions could complement each other, especially over spatially heterogeneous environments where the canopy cover (CC) is low. This study focused on the retrieval of five vegetation traits (gap fraction, leaf chlorophylls (C-ab) and carotenoids (Car) contents, equivalent water thickness, and leaf mass per area) of two Mediterranean-climate forests from AVIRIS-Classic (AVIRIS-C), synthetic Biodiversity, and synthetic Surface Biology and Geology (SBG) missions with 18 m, 8 m, and 30 m GSD, respectively, using a hybrid method. The synthetic SBG images were provided by NASA, while the Biodiversity images were generated from airborne AVIRIS-Next Generation hyperspectral imagery. Partial least-square regressors were trained over the outputs of the DART model to estimates vegetation traits. Estimated accuracies were assessed, when possible, by comparison with in situ measurements. We showed that estimated accuracy of gap fraction was similar between AVIRIS-C and SBG (RMSE of 0.09, R-2 of 0.8 and RMSE of 0.07, R-2 of 0.59, respectively). Leaf traits estimated accuracies were also similar between these two sensors, but only acceptable for C-ab and Car (similar to 7.5 mu g.cm(-2) RMSE for C-ab , similar to 1.65 mu g.cm(-2) RMSE for Car), especially over the densest parts of the canopy. When comparing estimates obtained from Biodiversity and SBG imagery, it appeared that the denser the canopy, the more estimates from both sensors were in agreement for all leaf traits (for instance, C-ab , R-2 was 0.2 for 30% <= CC <= 50% and 0.48 for CC >= 80%). The results show that (i) SBG imagery should lead to estimated accuracies similar to AVIRIS-C, with acceptable performances over dense canopies, and that (ii) Biodiversity imagery has a high potential to map vegetation traits over any canopy no matter its sparsity, as individual tree crowns are mostly resolved at an 8 m GSD.