# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
###### Abstract
The changing topography and climate over complex terrains causes SM to shift with the evolution of the day. Furthermore, the temporal and spatial distributions of SM are influenced by various land surface environmental variables. Evapor transpiration is the main factor that affects deep soil [22]. Land surface temperature has an impact on soil evaporation, crop transpiration, and SM dynamics [23]. Vegetation canopy has the ability to intercept precipitation and decrease evapotranspiration. The understory layer can capture surface runoff and enhance surface infiltration. Vegetation roots can also form infiltration and drainage, thereby affecting water transport and SM [24]. The SM also varies with different types of land cover [25]. The information fusion method emerged as a response to these influencing variables. It is the process of combining knowledge extracted from different sensors/modalities, in order to obtain more useful or discriminant information for the purpose of regression or classification [26]. The modality is a source or form of information [27]. At present, passive microwave SM downscaling mainly fuses optical remote sensing information [28]. There are mature technologies that are easy to use, and many satellites with a high spatial resolution are available. Some researchers have used empirical polynomials to fit the downscaling relationships [29; 30; 31]. Some researchers have used ML methods to fuse multivariate information. Random forests [32; 33; 34], neural networks [13; 35; 36; 37; 38], and support vector machines [39; 40] are the top three ML algorithms that have been frequently applied together with in situ SM measurements [41]. ML-based methods do not require continuous data and concurrent overpass by other satellites, but they are computationally intensive and require parameter optimization [42]. An intercomparison of different downscaling methods based on constructed synthetic datasets with a rich variability in temporal and spatial heterogeneities and patterns of SM will help to determine the applicability of each method for certain conditions [43]. There are still certain challenges regarding SM downscaling in complex terrains. The spatial and temporal heterogeneities of SM are significant due to high elevation differences, an undulating topography, messy aspects, and changing underlying surface conditions. These affect the accuracies of theoretical models [44], spatial interpolation methods [45], and empirical polynomial fittings [30]. According to the no-free-lunch theorem [46; 47], different ML algorithms have different inductive preferences, which raises the question of which ML algorithm is superior for SM downscaling in complex terrains. ML models driven by data tend to move towards the sample average, weakening the extreme cases. To address these challenges, spatiotemporal information, terrain elements, land-surface environmental variables, and in situ measurements are fused to downscale SM through ML algorithms in complex terrains in this study. The genetic-algorithm-optimized backpropagation (GABP) neural network, convolutional neural network (CNN), and random forest (RF) algorithms are compared for SM downscaling in complex terrains. An in situ information fusion method based on GDA_Kriging is proposed to optimize the downscaled SM.