GEOV2: Improved smoothed and gap filled time series of LAI, FAPAR and FCover 1 km Copernicus Global Land products
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 an...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/350663 |
| Acceso en línea: | http://hdl.handle.net/10261/350663 |
| Access Level: | acceso abierto |
| Palabra clave: | Global vegetation monitoring Leaf area index Fraction of absorbed PAR Green vegetation cover SPOT/VGT PROBA-V |
| Sumario: | 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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