While ensuring food security worldwide, irrigation is altering the water cycle and generating numerous environmental side effects. As detailed knowledge about the timing and the amounts of water used for irrigation over large areas is still lacking, remotely sensed soil moisture has proved potential to fill this gap. However, the spatial resolution and revisit time of current satellite products represent a major limitation to accurately estimating irrigation. This work aims to systematically quantify their impact on the retrieved irrigation information, hence assessing the value of satellite soil moisture for estimating irrigation timing and water amounts.

In a real-world experiment, we modeled soil moisture using actual irrigation and meteorological data, obtained from farmers and weather stations, respectively. Modeled soil moisture was compared against various remotely sensed products differing in terms of spatio-temporal resolution to test the hypothesis that high-resolution observations can disclose the irrigation signal from individual fields while coarse-scale satellite products cannot. Then, in a synthetic experiment, we systematically investigated the effect of soil moisture spatial and temporal resolution on the accuracy of irrigation estimates. The analysis was further elaborated by considering different irrigation scenarios and by adding realistic amounts of random errors in the soil moisture time series.

We show that coarse-scale remotely sensed soil moisture products achieve higher correlations with rainfed simulations, while high-resolution satellite observations agree significantly better with irrigated simulations, suggesting that high-resolution satellite soil moisture can inform on field-scale (∼40 ha) irrigation. A thorough analysis of the synthetic dataset showed that satisfactory results, both in terms of detection (F-score > 0.8) and quantification (Pearson’s correlation > 0.8), are found for noise-free soil moisture observations either with a temporal sampling up to 3 days or if at least one-third of the pixel covers the irrigated field(s). However, irrigation water amounts are systematically underestimated for temporal samplings of more than one day, and decrease proportionally to the spatial resolution, i.e., coarsening the pixel size leads to larger irrigation underestimations. Although lower spatial and temporal resolutions decrease the detection and quantification accuracies (e.g., R between 0.6 and 1 depending on the irrigation rate and spatio-temporal resolution), random errors in the soil moisture time series have a stronger negative impact (Pearson R always smaller than 0.85). As expected, better performances are found for higher irrigation rates, i.e. when more water is supplied during an irrigation event. Despite the potentially large underestimations, our results suggest that high-resolution satellite soil moisture has the potential to track and quantify irrigation, especially over regions where large volumes of irrigation water are applied to the fields, and given that low errors affect the soil moisture observations.

在确保全球粮食安全的同时，灌溉正在改变水循环并产生许多环境副作用。由于仍然缺乏对大面积灌溉用水的时间和水量的详细了解，遥感土壤湿度已被证明有可能填补这一空白。然而，当前卫星产品的空间分辨率和重访时间是准确估算灌溉的主要限制。这项工作旨在系统地量化它们对检索到的灌溉信息的影响，从而评估卫星土壤水分的价值，以估计灌溉时间和水量。

在真实世界的实验中，我们分别使用从农民和气象站获得的实际灌溉和气象数据模拟土壤湿度。将模拟的土壤水分与时空分辨率不同的各种遥感产品进行比较，以检验高分辨率观测可以揭示单个田地的灌溉信号而粗尺度卫星产品不能揭示这一假设。然后，在一个综合实验中，我们系统地研究了土壤水分时空分辨率对灌溉估计准确性的影响。通过考虑不同的灌溉情景并在土壤水分时间序列中添加实际数量的随机误差，进一步阐述了该分析。

我们表明，粗尺度遥感土壤水分产品与雨养模拟具有更高的相关性，而高分辨率卫星观测与灌溉模拟的一致性明显更好，这表明高分辨率卫星土壤水分可以在田间尺度（~40 公顷）上提供信息灌溉。对合成数据集的彻底分析表明，无论是在时间采样高达 3天或至少三分之一的像素覆盖灌溉田地。然而，对于超过一天的时间采样，灌溉水量被系统地低估了，并且与空间分辨率成比例地减少，即 粗化像素大小会导致更大的灌溉低估。尽管较低的空间和时间分辨率会降低检测和量化精度（例如，R 在 0.6 和 1 之间，取决于灌溉率和时空分辨率），但土壤水分时间序列中的随机误差具有更强的负面影响（Pearson R 总是较小大于 0.85）。正如预期的那样，更高的灌溉率（即在灌溉事件期间供应更多的水）具有更好的性能。尽管可能存在较大的低估，但我们的结果表明，高分辨率卫星土壤水分具有跟踪和量化灌溉的潜力，特别是在向田间施用大量灌溉水的地区，并且考虑到低误差会影响土壤水分观测.