## 1 Introduction  
Soil moisture is an essential parameter of the global surface water cycle and is also a physical surface quantity that has long been studied with interest. Monitoring soil moisture on a large scale is significant for agriculture, hydrology, and the geographic environment [1]. It also plays a vital role in the climate system and extreme weather such as droughts, floods, and inundation. The persistence of extreme weather is relatively short-lived, and soil moisture has a high memory compared to the atmosphere. In seasonal time scales, soil moisture is of great use [2]. For this reason, it is of more practical importance to achieve soil moisture inversion at a large scale, with high accuracy, low cost, and high spatial and temporal resolution. The advent of remote sensing technology has made it possible to estimate and monitor surface parameters significantly. Remote sensing satellites can measure soil moisture with uniform accuracy over large spatial scales by constant revisit intervals. Commonly used remote sensing techniques include optical remote sensing and microwave remote sensing. Among the remote sensing techniques capable of measuring soil moisture, microwave remote sensing is the most promising technique for measuring soil moisture with shorter revisit times by sensing the dielectric properties of soil moisture [3; 4]. Soil Moisture and Ocean Salinity (SMOS), launched by the European Space Agency, and Soil Moisture Active and Passive (SMAP), launched by NASA in 2015, are the predominant soil moisture missions today [5; 6]. Both enable soil moisture monitoring globally by carrying L-band instruments to detect soil moisture in the top 5 cm. It provides soil moisture inversion with a spatial resolution of about 40 km and a revisit time of 2-3 days, with an accuracy requirement of 0.04 cm\({}^{3}\)cm\({}^{-3}\)[7]. Meanwhile, the SMAP mission provides a high spatial and temporal resolution product with 3 km spatial resolution and 2-3 days revisit time with the help of rotating antennas until the hardware fails in mid-2015 [8; 9]. However, microwave wavelengths are hundreds to millions of times longer than visible and infrared light, resulting in a low spatial resolution of microwave soil moisture products, which cannot represent localized soil moisture variations in detail. For this reason, a large number of downscaling studies based on the SMOS and SMAP missions have been carried out. It has become an ongoing research hotspot to improve the spatial and temporal resolution of soil moisture products through algorithms. [PERSON] et al. used a combination of a relatively noisy 3 km radar backscatter coefficient and a more accurate 36 km radiometer based on the SMAP task, generating an optimal 10 km soil moisture product with better performance than the reflectance radiometer alon0065 [10]. On this basis, [PERSON] combined the accuracy of SMOS observations with the high spatial resolution of visible/infrared satellite data, effectively capturing soil moisture variability at spatial scales of 10 and 1 km without a significant reduction in root mean square error [11]. With the SMAP mission, [PERSON] et al. combined coarse-scale radiometry with radar observations detectable at fine-scale spatial heterogeneity; to produce a high-resolution best soil moisture estimate at 9 km, further improving the spatial resolution and accuracy of soil moisture [12]. [PERSON] et al. even combined SMOS and SMAP missions with information from a high spatial resolution imaging spectrometer to obtain higher resolution (1 km) soil moisture estimates [13]. However, microwave reflections from the soil surface are affected by the state of soil moisture and by environmental factors such as surface roughness, vegetation elements, and interactions with the atmosphere. For this reason, soil moisture inversion using microwave remote sensing is susceptible to non-soil moisture factors and thus error, which may lead to inaccurate soil moisture inversion [14; 15].