# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
###### Abstract  
Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details. downscaling; soil moisture; remote sensing; complex terrains; machine learning; data fusion 2023  
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of SM mainly includes active methods based on radar or microwave scatterometers, and passive methods based on microwave radiometers [8]. Active microwave sensors have a resolution at the meter scale, but their data quantity is large and expensive with complex processing, which makes it difficult to apply. Their precision is also affected by the radar incident angle, wavelength, and polarization [9]. A passive microwave system is the main method to continuously monitor SM over extensive areas at present. Among them, the L-band is independent of solar illumination and can penetrate the atmosphere and sparse-to-medium vegetation layers to detect deeper soil signals from approximately the top 5 cm of soil layer [10]. However, passive microwave data have low spatial resolution of approximately 25-40 km due to the weak incoming energy [11]. Obtaining large-area, high-resolution, high-precision SM data by downscaling the passive microwave measurements has become a hot topic [12; 13]. The downscaled results are usually interdependent in terms of the spatial distribution, detailed information, and data accuracy. According to the process mechanism and participating factors, the existing methods of passive microwave SM spatial downscaling can be divided into three different categories: the theoretical method, the spatial interpolation method, and the information fusion method. The theoretical method models the internal mechanism among physical quantities, including hydrological models [14], surface models [15] and spatial fractal models [16]. Restricted by the initial field, boundary conditions, and the complicated physical processes, it is mainly applied in small local areas. The spatial interpolation method aims to mine the spatial information connotation of coarse-scale homologous data to achieve fine-scale SM data. It performs well on simple terrains [17]. There are several factors that impact SM, and their influences are complex and nonlinear. Topography, for example, can affect the physical and chemical properties of soil, as well as the distribution of vegetation patterns on the slope [18], which leads to a spatial variation of SM. SM is negatively correlated with elevation and decreases with an increasing slope. Moreover, SM has different slopes with varying energy and duration of the receiving solar radiation. Therefore, slope and aspect directly affect material and energy distribution, soil development, vegetation types of the surface, and land-use mode, which significantly impact SM [19; 20; 21].