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.

时空上的冠层含水量（CWC）数据对于监测植被状况非常重要，并且构成研究生态系统与气候之间相互作用的重要信息。尽管付出了许多努力，但目前尚无用户可使用的CWC产品。在用于陆地表面分析的卫星应用设施（LSA-SAF）的背景下，我们已经开发了一种算法，可基于气象-操作（ MetOp）卫星组成EUMETSAT极地系统（EPS）。CWC反映了叶片水平的水分状况以及与冠层结构有关的信息。^-2和0.078 kg m ^-2。特别是，当包括归一化差异红外指数（NDII）时，该算法在半干旱地区受到更好的约束，而在茂密的树冠中饱和效果得到缓解。对空间尺度效应的分析表明，CWC取回中的平均偏差误差保持在0.001 kg m ^-2以下考虑的空间分辨率范围为20 m至1 km。本研究进一步评估了LSA-SAF产品相对于简化的2级产品原型处理器（SL2P）产品的一致性，并使用MSI / Sentinel-2和MODIS /的光学数据证明了其在不同时空分辨率下的适用性。 Terra和Aqua。结果表明，LSA-SAF EPS / AVHRR算法具有鲁棒性，与在可用地面数据中观察到的CWC动态一致，并且也适用于其他传感器的数据。我们得出结论，EPS / AVHRR CWC产品是用于在区域和全球范围内监测植被水状况的有前途的工具。