@article {bnh-7479, title = {A global canopy water content product from AVHRR/Metop}, journal = {Remote Sensing}, volume = {162}, year = {2020}, month = {04/2020}, pages = {77-93}, 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{\textendash}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.

}, keywords = {AVHRR/MetOp, Canopy Water Content (CWC), EUMETSAT, Gaussian Process Regression (GPR), MODIS, Polar System (EPS), Sentinel-2}, doi = {https://doi.org/10.1016/j.isprsjprs.2020.02.007}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0924271620300411}, author = {Francis Javier Garcia-Haro and Manuel Campos-Taberner and Alaro Moreno and Hakan Torbern Tagesson and Fernando Camacho and Beatriz Martinez and Sergio Sanchez and Maria Piles and Gustau Campas-Valls and Marta Yebra and Maria Amparo Gilabert} } @article {bnh-4586, title = {A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing}, journal = {Remote Sensing of Environment}, volume = {212}, year = {2018}, month = {06/2018}, pages = {12}, chapter = {260}, abstract = {

Fuel Moisture Content (FMC) is one of the primary drivers affecting fuel flammability that lead to fires. Satellite observations well-grounded with field data over the highly climatologically and ecologically diverse Australian region served to estimate FMC and flammability for the first time at a continental-scale. The methodology includes a physically-based retrieval model to estimate FMC from MODIS (Moderate Resolution Imaging Spectrometer) reflectance data using radiative transfer model inversion. The algorithm was evaluated using 360 observations at 32 locations around Australia with mean accuracy for the studied land cover classes (grassland, shrubland, and forest) close to those obtained elsewhere (r2 = 0.58, RMSE = 40\%) but without site-specific calibration. Logistic regression models were developed to generate a flammability index, trained on fire events mapped in the MODIS burned area product and four predictor variables calculated from the FMC estimates. The selected predictor variables were actual FMC corresponding to the 8-day and 16-day period before burning; the same but expressed as an anomaly from the long-term mean for that date; and the FMC change between the two successive 8-day periods before burning. Separate logistic regression models were developed for grassland, shrubland and forest. The models obtained an {\textquotedblleft}Area Under the Curve{\textquotedblright} calculated from the Receiver Operating Characteristic plot method of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction.

}, keywords = {Fire occurrence, Fire risk, Forests., GEOPROSAIL inversion, Grasslands, MODIS, PROSAIL inversion, Shrubs}, doi = {10.1016}, url = {https://www.sciencedirect.com/science/article/pii/S0034425718302116$\#$.WvZrg57h8og.twitter}, author = {Marta Yebra and Xingwen Quan and David Ria{\~n}o and Pablo Rozas Larraondo and Albert van Dijk and Geoffrey J. Cary} }