Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field scale using a data assimilation strategy.

中文翻译：

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用于滴灌葡萄园土壤湿度监测的高分辨率热和雷达遥感检索的数据同化


有效的用水评估和灌溉管理对于灌溉农业的可持续性至关重要，特别是在气候条件变化的情况下。由于在广泛的地理区域维护地面仪器是不切实际的，遥感和基于数值模型的土壤水状况精细绘图已应用于一系列空间尺度的水资源应用。在这里，我们提出了一个原型框架，用于将高分辨率热红外（TIR）和合成孔径雷达（SAR）遥感数据集成到土壤植被大气转移（SVAT）模型中，目的是提供改进的地表估计和根区土壤湿度，可以支持优化的灌溉管理策略。具体来说，对美国加利福尼亚州中央山谷葡萄园地点的水分胁迫遥感估计（来自 TIR）和表层土壤湿度反演（来自 SAR）被同化为 30 米分辨率的 SVAT 模型。通过合成和真实数据实验对算法进行了研究。结果表明，在处理模型状态和观测值之间的非线性关系方面，粒子滤波方法优于整体卡尔曼滤波器。此外，生物物理条件（例如叶面积指数）会影响观测值和状态之间的关系，因此必须在同化模型中准确表示。总体而言，通过 SVAT 模型预测的表面和根区土壤湿度都通过热和雷达检索的同化得到增强，这表明使用数据同化策略改善农业子田规模灌溉管理的潜力。