Drylands are critical in regulating global carbon sequestration, but the resiliency of these semi-arid shrub, grassland and forest systems is under threat from global warming and intensifying water stress. We used synergistic satellite optical-Infrared (IR) and microwave remote sensing observations to quantify plant-to-stand level vegetation water potentials and seasonal changes in dryland water stress in the southwestern U.S. Machine-learning was employed to re-construct global satellite microwave vegetation optical depth (VOD) retrievals to 500-m resolution. The re-constructed results were able to delineate diverse vegetation conditions undetectable from the original 25-km VOD record, and showed overall favorable correspondence with in situ plant water potential measurements (R from 0.60 to 0.78). The VOD water potential estimates effectively tracked plant water storage changes from hydro-climate variability over diverse sub-regions. The re-constructed VOD record improves satellite capabilities for monitoring the storage and movement of water across the soil-vegetation-atmosphere continuum in heterogeneous drylands.

中文翻译：

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通过多源传感器融合增强对旱地植被水势的卫星监测


缺水地区在调节全球碳封存方面至关重要，但这些半干旱灌木、草原和森林系统的复原力正受到全球变暖和日益加剧的水资源压力的威胁。我们使用协同卫星光学红外 （IR） 和微波遥感观测来量化美国西南部植物到林分水平植被的水势和旱地水分胁迫的季节性变化。机器学习用于将全球卫星微波植被光学深度 （VOD） 检索重建为 500 m 分辨率。重建的结果能够描绘出原始 25 km VOD 记录中无法检测到的各种植被条件，并显示出与原位植物水势测量值的总体良好对应（R 从 0.60 到 0.78）。VOD 水势估计有效地跟踪了不同子区域水文气候变化引起的植物储水量变化。重建的 VOD 记录提高了卫星监测异质干旱地区土壤-植被-大气连续体中水的储存和移动的能力。