GEOV2: Improved smoothed and gap filled time series of LAI, FAPAR and FCover 1 km Copernicus Global Land products
Verger, Aleixandre | Sánchez-Zapero, Jorge | Weiss, Marie | Descals, Adrià | Camacho, Fernando | Lacaze, Roselyne | Baret, Frédéric | Centre for Ecological Research and Applied Forestries = Centre de Recerca Ecologica i Aplicacions Forestals (CREAF) ; Consejo Superior de Investigaciones Cientificas [España] = Spanish National Research Council [Spain] (CSIC) | Consejo Superior de Investigaciones Cientificas [España] = Spanish National Research Council [Spain] (CSIC) | Universitat de València = University of Valencia (UV) | EOLAB, Parc Cientific ; Universitat de València = University of Valencia (UV) | Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH) ; Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | EOLAB | HYGEOS (SARL) | European Project: 218795,EC:FP7:SPA,FP7-SPACE-2007-1,GEOLAND2(2008)
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Показать больше [+] Меньше [-]Английский. Essential vegetation variables including leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and fraction of green vegetation cover (FCover) are produced and distributed in the Copernicus Global Land Service. We describe here the algorithmic principles, consistency and improvements of GEOV2, Version 2 of LAI, FAPAR and FCover products derived from SPOT/VGT (1999–2013) and PROBA-V data (2014–2020) at 1 km resolution, as compared to the earlier version GEOV1. GEOV2 is based on neural networks first trained with CYCLOPES and MODIS products to estimate LAI, FAPAR and FCover from daily top of canopy reflectance. Temporal techniques are then applied to filter, smooth, fill gaps and get a composited value every 10 days. Results show that GEOV2 products keep a high consistency with GEOV1 (90% of residuals within ± max(0.5, 20%) LAI, and 80% within ± max(0.05, 10%) FAPAR / FCover) and improves in terms of product completeness (<1% of missing data), temporal consistency, consistency across variables and accuracy.
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