Abstract
Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetationstatus, and constitute essential information for studying ecosystem-climate interactions. Despite many effortsthere is currently no operational CWC product available to users. In the context of the Satellite ApplicationFacility for Land Surface Analysis (LSA-SAF), we have developed an algorithm to produce a global dataset ofCWC based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on boardMeteorological–Operational (MetOp) satellites forming the EUMETSAT Polar System (EPS). CWC reflects thewater conditions at the leaf level and information related to canopy structure. An accuracy assessment of theEPS/AVHRR CWC indicated a close agreement with multi-temporal ground data from SMAPVEX16 in Canadaand Dahra in Senegal, with RMSE of 0.19 kg m−2 and 0.078 kg m−2 respectively. Particularly, when theNormalized Difference Infrared Index (NDII) was included the algorithm was better constrained in semi-aridregions and saturation effects were mitigated in dense canopies. An analysis of spatial scale effects shows themean bias error in CWC retrievals remains below 0.001 kg m−2 when spatial resolutions ranging from 20 m to1 km are considered. The present study further evaluates the consistency of the LSA-SAF product with respect tothe Simplified Level 2 Product Prototype Processor (SL2P) product, and demonstrates its applicability at differentspatio-temporal resolutions using optical data from MSI/Sentinel-2 and MODIS/Terra & Aqua. Resultssuggest that the LSA-SAF EPS/AVHRR algorithm is robust, agrees with the CWC dynamics observed in availableground data, and is also applicable to data from other sensors. We conclude that the EPS/AVHRR CWC product isa promising tool for monitoring vegetation water status at regional and global scales.
Original language | English |
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Article number | 10.1016/j.isprsjprs.2020.02.007 |
Pages (from-to) | 77–93 |
Number of pages | 17 |
Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
Volume | 162 |
DOIs | |
Publication status | Published - 2020 Feb 12 |
Externally published | Yes |
Subject classification (UKÄ)
- Physical Geography
- Remote Sensing
Free keywords
- EUMETSAT Polar System (EPS)
- AVHRR/MetOp
- Canopy Water Content (CWC)
- Gaussian Process Regression (GPR)
- Sentinel-2