Characterizing Precipitation and Soil Moisture Drydowns in Finland Using SMAP Satellite Data

Precipitation (P) and soil moisture (SM) are critical components of the global water, energy, and biogeochemical cycles, yet their patterns and interrelations in the Arctic are poorly understood. Due to the sparse in situ measurement network, satellites are the only way to observe P and SM in high-latitude regions. This study uses NASA’s SMAP satellite to analyze the relationship between SM and P, assess the feasibility of estimating P from SM, and examine SM drydown patterns in Finland from April to September over 2018-2019. The analysis reveals a notable spatial and temporal variability in SM, with a weaker correlation between P and SM in spring due to snowmelt and a stronger relationship in summer and fall. Water bodies complicate the SM retrieval causing the SM retrievals to saturate. Using the SM2RAIN algorithm, we estimated P from SM data. The algorithm shows promising results, detecting the area of rainfall accurately in most cases but estimating the intensity of the rainfall is more challenging, particularly for light and very heavy rain. We analyzed SM drydown patterns by fitting an exponential model to each SM drydown period, from which we estimated the exponential decay time scale (τ) and the lower bound of SM (SMmin). τ does not show much spatial or temporal variability. The distribution of τ is positively skewed, with a mode of 1.6 days and a median of 4.0 days, consistent with other studies. The distribution of SMmin is also positively skewed, with a mode of 0.14 m3 m-3 and a median of 0.17 m3 m-3. SMmin exhibits another lower peak at 0.02 m3 m-3, the lower limit of SMAP SM retrievals, possibly causing an artifact in the results. SMmin shows spatial variability, with the lower bound being slightly higher near water bodies but also showing a more prominent peak at 0.02 m3 m-3. Grid cells with dense vegetation and low vegetation agree better with each other, indicating that water bodies particularly affect and complicate SM retrieval. The promising results suggest that the method could be applied across the entire Arctic region.

降水（Precipitation，P）和土壤湿度（Soil Moisture，SM）是全球水、能量及生物地球化学循环（biogeochemical cycles）的关键组成部分，但二者在北极地区的分布模式及相互关系仍知之甚少。由于原位观测（in situ measurement）网络稀疏，卫星是高纬度地区观测P和SM的唯一途径。本研究利用美国国家航空航天局（NASA）的SMAP卫星，分析2018-2019年4月至9月期间芬兰地区SM与P的关系，评估基于SM估算P的可行性，并探究SM的干旱消退模式。分析结果显示，SM存在显著的时空变异性；春季因融雪，P与SM的相关性较弱，而夏季和秋季的相关性较强。水体使SM反演（SM retrieval）过程复杂化，导致反演结果饱和。利用SM2RAIN算法，我们基于SM数据估算了P。该算法表现出良好前景：多数情况下能准确识别降雨区域，但估算降雨强度更具挑战性，尤其是对小雨和暴雨。我们通过对每个SM干旱消退期拟合指数模型来分析其模式，由此估算出指数衰减时间尺度（exponential decay time scale，τ）和SM的下限值（SMmin）。τ未表现出明显的时空变异性，其分布呈正偏态，众数为1.6天，中位数为4.0天，与其他研究结果一致。SMmin的分布同样呈正偏态，众数为0.14 m³/m⁻³，中位数为0.17 m³/m⁻³；此外，SMmin在0.02 m³/m⁻³处存在另一个较低峰值，这是SMAP卫星SM反演的下限值，可能导致结果中出现伪影（artifact）。SMmin存在空间变异性：水体附近的下限值略高，但在0.02 m³/m⁻³处也呈现更显著的峰值；植被茂密与植被稀疏的网格单元结果更一致，表明水体对SM反演的影响尤为显著且使其复杂化。这些良好的结果表明，该方法可推广至整个北极地区。