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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.

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.