Torbern Tagesson
Forskare
A global Canopy Water Content product from AVHRR/Metop
Författare
Summary, in Swedish
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.
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.
Publiceringsår
2020-02-12
Språk
Engelska
Sidor
77-93
Publikation/Tidskrift/Serie
ISPRS Journal of Photogrammetry and Remote Sensing
Volym
162
Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
Elsevier
Ämne
- Physical Geography
- Remote Sensing
Nyckelord
- EUMETSAT Polar System (EPS)
- AVHRR/MetOp
- Canopy Water Content (CWC)
- Gaussian Process Regression (GPR)
- Sentinel-2
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 0924-2716