ABSTRACT The study of the spatio-temporal dynamics of surface soil moisture (SSM) drydowns integrates the soil response to climatic conditions, drainage and land cover and is key to advances in our knowledge of the soil–atmosphere water exchange. SSM drydowns have also been employed to compare soil moisture spatio-temporal behaviour between different data sources such as satellite-derived data and land–surface models, difficult to compare with standard methodologies. However, the errors introduced by satellite effective sampling frequencies (SF) and by different methodologies employed to define a drydown period have until now not been properly addressed in the literature. Here, SSM from microwave remote sensing products operating at L, C and X frequency bands are analysed together with SSM from a land–surface model in southeastern South America during 2010–2014, at seasonal and annual scales. We use an SSM-based drydown detection methodology and an exponential model to estimate the drydown time scale. The errors generated by the SF and by using SSM instead of precipitation to define the start of the drydown period are examined using a synthetic soil moisture model. Most of the products can detect the negative correlation between aridity conditions and drydown time scales (faster soil drying in the semiarid west and slower – and noisier – towards the wetter east). The Soil Moisture Ocean Salinity (SMOS) L-band product reproduces the smoothest drydown time scale spatial patterns at the annual and seasonal scales and displays large seasonal contrasts, although its error due to SF is the highest among the three products. The Organizing Carbon Hydrology In Dynamic Ecosystems (ORCHIDEE) land–surface model resampled by the SF of each product shows better agreement with SMOS, followed by the X-band product. The agreement is higher over the southern Pampas Plains, a region with high coverage of satellite-derived data and flat topography. SSM observational errors generate higher relative uncertainties for drydown time scales longer than 8 days, while the SF is more relevant for shorter drydowns. Also, the SF has a larger impact than soil depth, particularly in the dry season, when sparse temporal coverage misses short drydowns. Soil texture influence is captured by SMOS and ORCHIDEE, revealing slower drydowns for finer textures at the annual scale. Our results show that the soil drying behaviour is comparable between microwave remote sensing products and a land–surface model and that the observational errors and the SF are important sources of uncertainty to consider when interpreting drydown results.

中文翻译：

---

南美洲东南部的时空土壤干燥：有效采样频率和观测误差对干旱时间尺度估计的重要性

摘要 表层土壤水分 (SSM) 干涸的时空动态研究整合了土壤对气候条件、排水和土地覆盖的响应，是推进我们对土壤-大气水交换知识的关键。SSM 干涸也被用来比较不同数据源之间的土壤水分时空行为，如卫星数据和地表模型，难以与标准方法进行比较。然而，由卫星有效采样频率 (SF) 和用于定义干涸期的不同方法引入的误差，直到现在文献中还没有得到适当的解决。这里，来自在 L 运行的微波遥感产品的 SSM，C 和 X 频段与来自 2010-2014 年南美洲东南部地表模型的 SSM 一起在季节和年度尺度上进行分析。我们使用基于 SSM 的干涸检测方法和指数模型来估计干涸时间尺度。SF 产生的误差以及使用 SSM 代替降水来定义干涸期的开始，使用合成土壤水分模型进行检查。大多数产品都可以检测到干旱条件和干旱时间尺度之间的负相关（半干旱西部土壤干燥速度更快，而东部更湿的土壤干燥速度更慢，噪音更大）。土壤水分海洋盐度 (SMOS) L 波段产品再现了年和季节尺度上最平滑的干涸时间尺度空间格局，并显示出较大的季节对比，虽然由于SF引起的误差是三款产品中最高的。由每个产品的 SF 重新采样的动态生态系统中的组织碳水文学 (ORCHIDEE) 地表模型与 SMOS 显示出更好的一致性，其次是 X 波段产品。南潘帕斯平原的一致性更高，该地区卫星数据覆盖率高且地形平坦。对于超过 8 天的干涸时间尺度，SSM 观测误差会产生更高的相对不确定性，而 SF 与较短的干涸时间更相关。此外，SF 的影响大于土壤深度，尤其是在旱季，稀疏的时间覆盖会错过短暂的旱季。SMOS 和 ORCHIDEE 捕获了土壤质地的影响，揭示了在年度尺度上更细质地的较慢干涸。