A global canopy water content product from AVHRR/Metop

• Published in: ISPRS journal of photogrammetry and remote sensing 2020-04, Vol.162, p.77-93
• Main Authors: García-Haro, Francisco Javier, Campos-Taberner, Manuel, Moreno, Álvaro, Tagesson, Håkan Torbern, Camacho, Fernando, Martínez, Beatriz, Sánchez, Sergio, Piles, María, Camps-Valls, Gustau, Yebra, Marta, Gilabert, María Amparo
• Format: Article
• Language: English
• Subjects: AVHRR/MetOp; Canopy Water Content (CWC); EUMETSAT Polar System (EPS); Gaussian Process Regression (GPR); MODIS; Sentinel-2
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2020.02.007

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.