6533b829fe1ef96bd128a2c9
RESEARCH PRODUCT
A global Canopy Water Content product from AVHRR/Metop
Gustau Camps-vallsAlvaro MorenoAlvaro MorenoMarta YebraMarta YebraHåkan Torbern TagessonBeatriz MartínezSergio SánchezMaría Amparo GilabertFrancisco Javier García-haroFernando CamachoMaria PilesManuel Campos-tabernersubject
Canopy010504 meteorology & atmospheric sciencesMean squared errorAdvanced very-high-resolution radiometerCanopy Water Content (CWC)0211 other engineering and technologiesGaussian Process Regression (GPR)FOS: Physical sciencesContext (language use)02 engineering and technologyAVHRR/MetOp01 natural sciencesComputers in Earth SciencesEngineering (miscellaneous)Water content021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingVegetation15. Life on landAtomic and Molecular Physics and OpticsComputer Science ApplicationsPhysics - Atmospheric and Oceanic PhysicsMODIS13. Climate actionEUMETSAT Polar System (EPS)Atmospheric and Oceanic Physics (physics.ao-ph)Spatial ecologyEnvironmental scienceSatelliteSentinel-2description
Abstract Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climate interactions. Despite many efforts there is currently no operational CWC product available to users. In the context of the Satellite Application Facility for Land Surface Analysis (LSA-SAF), we have developed an algorithm to produce a global dataset of CWC based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board Meteorological–Operational (MetOp) satellites forming the EUMETSAT Polar System (EPS). CWC reflects the water conditions at the leaf level and information related to canopy structure. An accuracy assessment of the EPS/AVHRR CWC indicated a close agreement with multi-temporal ground data from SMAPVEX16 in Canada and Dahra in Senegal, with RMSE of 0.19 kg m−2 and 0.078 kg m−2 respectively. Particularly, when the Normalized Difference Infrared Index (NDII) was included the algorithm was better constrained in semi-arid regions and saturation effects were mitigated in dense canopies. An analysis of spatial scale effects shows the mean bias error in CWC retrievals remains below 0.001 kg m−2 when spatial resolutions ranging from 20 m to 1 km are considered. The present study further evaluates the consistency of the LSA-SAF product with respect to the Simplified Level 2 Product Prototype Processor (SL2P) product, and demonstrates its applicability at different spatio-temporal resolutions using optical data from MSI/Sentinel-2 and MODIS/Terra & Aqua. Results suggest that the LSA-SAF EPS/AVHRR algorithm is robust, agrees with the CWC dynamics observed in available ground data, and is also applicable to data from other sensors. We conclude that the EPS/AVHRR CWC product is a promising tool for monitoring vegetation water status at regional and global scales.
year | journal | country | edition | language |
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2020-01-01 |