Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land