Soil moisture is a notably important component in various studies in water sciences, including hydrology, agriculture, and water management. To achieve extensive or global spatial coverage, satellites focusing on soil moisture observation have been launched, and many satellite products, such as SMAP and SMOS soil moisture products, have been provided. Most of these satellite observations are based on the dielectric properties of wet soil, and most soil moisture retrieval algorithms are calibrated or evaluated using in situ soil moisture. While the in situ data observed by dielectric sensors, which are the most widely used, are reported to include errors caused by the so-called “temperature effects” of these sensors, the temperature dependency of bulk soil dielectric constant has rarely been discussed on satellite data. Since both in situ dielectric measurements and satellite observations are based on the same physical variable, the dielectric constant and the dielectrically measured in situ soil moisture data are also used as ground truth, it is necessary to assess the impact of temperature effects on satellite products. In this work, we attempted to identify the existence of the temperature effects and evaluate the consequences of removing these effects on in situ and satellite soil moisture and the relationships between the brightness temperature at the soil surface and the brightness temperature provided by satellite observation. To achieve the goals of this study, we analyzed the temperature effects on surface soil moisture data provided by a SMAP mission in Oklahoma, the United States. The results show that temperature effects exist in SMAP soil moisture products in Oklahoma, and the removal of these effects will potentially improve the accuracy of these products.

Rainfall data is the most basic and essential input for many studies in hydrology, climatology, or water management. However, rainfall data is quite limited in poorly equipped regions such as high mountain regions, developing countries, and savannahs. In recent years, a new approach named SM2RAIN that has been proposed to assess rainfall amount on a daily scale. This method uses the relative saturation of soil (Se) as the input to infer rainfall. Some of these studies showed promising results in evaluating rainfall by using SM2RAIN. Over past decades, however, some other studies have also found that the soil water content (SWC), which is used to calculate the Se, has errors associated with diurnal temperature fluctuation. As soil water content is the primary input variable of SM2RAIN, this error must have impacts on the performance of SM2RAIN. In this study, the authors have assessed and cleared the impact of temperature on SM2RAIN performance. Thus, an effective temperature correction method has been applied to remove the impact of temperature on soil water content at 19 sites over the United States, Africa (Senegal) and Europe (Romania) in varies climatic conditions to assess the impact of temperature effect removal on results of SM2RAIN algorithm. The criteria to evaluate the program such as Nash-Sutcliff has increased up to 53% depending on the initial results and climatic condition. The correlation coefficient has positive change as well. As a result, this removing makes the performance of the algorithm better.

Soil moisture is among the most essential variables in hydrology and earth science. Many satellite missions, such as AMSR-E/2, have been launched to observe it in broader spatial coverage to overcome the shortage of in situ observations. However, the satellite soil moisture products have been reported to comprise errors caused by the so-called “temperature effects” widely observed in dielectrically measured in situ volumetric soil water content (SWC). In this work, we confirmed the existence of these errors in AMSR2 soil moisture products. A new algorithm was developed to remove these errors using satellite data at ascending and descending overpasses. The application of this algorithm to both satellite and in situ data of SWC and soil temperature at the Mongolia site shows that the difference between SWC values at ascending and descending overpasses caused by temperature effects is effectively removed. We assess the impact of this removal method on satellite data by comparing it with in situ data, utilizing metrics such as the correlation coefficient and other widely adopted evaluation methods. It is shown that the difference between the original and corrected in situ SWC is much smaller than that between AMSR2 and in situ SWC, either corrected or not. The results indicate that the metric values between the corrected AMSR2 and in situ SWC, after removing apparent differences caused by temperature effects, slightly improved compared to those between the original AMSR2 and in situ SWC. Though these findings imply that the removed errors may not be the most dominant, considering the current significant difference between AMSR2 and in situ SWC, the removal makes the ascending and descending data have close characteristics. It may allow using data at both ascending and descending overpasses and double the temporal resolution of AMSR2 SWC data.