# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data  
[PERSON]  
1 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (H.X. ); [EMAIL_ADDRESS] (Q.Y. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (R.J.)  
[PERSON]  
1 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (H.X. ); [EMAIL_ADDRESS] (Q.Y. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (R.J.)  
[PERSON]  
1 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (H.X. ); [EMAIL_ADDRESS] (Q.Y. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (R.J.)  
[PERSON]  
1 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (H.X. ); [EMAIL_ADDRESS] (Q.Y. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (R.J.)  
[PERSON]  
1 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China; [EMAIL_ADDRESS] (H.X. ); [EMAIL_ADDRESS] (Q.Y. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (R.J.)  
Runsheng Jing  
1 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (H.X. ); [EMAIL_ADDRESS] (Q.Y. ); [EMAIL_ADDRESS] (R.Z. ); [EMAIL_ADDRESS] (R.J.)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: email: {[[https://github.com/jps](https://github.com/jps)]([https://github.com/jps](https://github.com/jps))  
Remote sensing provides a new means for obtaining soil moisture information. Microwave remote sensing is an effective way to achieve regional and even global soil moisture data, which are not affected by weather and are sensitive to the dielectric properties of the soil moisture [7]. Currently, a variety of soil moisture products, such as Soil Moisture Active Passive (SMAP, 9 km, 36 km), Advanced Microwave Scanning Radiometer (AMSR-E, 25 km), and Soil Moisture and Ocean Salinity (SMOS, 25 km), have been released worldwide [8; 9; 10]. These remote sensing soil moisture products have high temporal resolution but coarse spatial resolution, making them difficult to satisfy the needs of research on regional agriculture, environmental protection, hydrology, and water resource management. In order to obtain high-resolution soil moisture data, scholars have proposed a variety of downscaling methods to improve the resolution of soil moisture products, including semi-physical models and statistical models [11; 12; 13; 14]. Semi-physical models are simplified models that combine physical principles and mathematical methods and use empirical and experimental data. Disaggregation based on Physical and Theoretical scale Change (DISPATCH) is the most common method in previous studies. It uses physical processes (water movement, evaporation, etc.) to describe the changes in target variables (soil moisture). Based on statistical and mathematical theories, the relationship between variables is established, and inferences are made using existing high-resolution soil moisture data. Based on this method, soil moisture downscaling has also been carried out and reasonably verified [11; 12; 15]. The statistical model performs downscaling by establishing a functional relationship between auxiliary data and soil moisture, which has higher operating efficiency and accuracy [16; 17; 18]. Machine learning, or deep learning, is usually better than physical and traditional statistical models in explaining the relationships between different data [19; 20].