The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R) and mean absolute error values of 0.73- and 0.95-mm at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

中文翻译：

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OpenET 评估加州杏仁园蒸散量的比较分析


加利福尼亚州的杏仁产业面临着水资源管理的挑战，而干旱、气候变化和地下水可持续性立法加剧了这些挑战。树木作物遥感蒸散实验（T-REX）旨在探索改善多年生木本作物系统精准灌溉管理的机会。加州中央山谷的杏仁园配备了涡流协方差通量测量仪，以评估基于卫星遥感的蒸散量 (RSET) 模型。 OpenET 提供高分辨率（30 米空间和每日时间）RSET 数据，综合了数十年的实际水管理研究。本研究对六个杏仁地点的 OpenET 性能进行了评估，涵盖了大范围的土壤、树龄和品种。它还将 OpenET 集合蒸散量 (ET) 数据与位于加利福尼亚州中央山谷的另外 148 个杏仁园的灌溉和降水记录进行了比较。结果显示，包括集合 ET 值在内的 OpenET 模型产生了合理且可操作的 ET 值，每日时间步长的总体决定系数 (R) 和平均绝对误差值分别为 0.73 毫米和 0.95 毫米。然而，考虑到 Landsat 的时间采样（8 天重访）和所使用的插值方法，评估的 ET 模型难以捕捉杏仁 ET 的短期变化；例如，由于杏仁收获前和收获期间缺乏灌溉，观测到的蒸散量迅速下降。该研究还提请注意灌溉果园被炎热/干燥地区包围的情况下的空间复杂性，导致测量的蒸散值与模拟的蒸散值之间存在差异。 与灌溉记录相比，在评估每月数据时，OpenET ensemble ET 能够将杏仁园的水输入（灌溉 + 降水）量化到 13% 以内。这里提出的初步结果强化了这样一个观点：RSET 模型（例如 OpenET 中的模型）是强大的工具，但其应用需要细致入微的理解并仔细考虑当地条件。