In addition, a large number of empirical or semi-physical relationships have been developed to link coarse-resolution, satellite-based SM retrievals with other land-surface parameters to obtain finer-resolution and more-continuous SM data products. These fitted models take a wide variety of forms, including optical and thermal temperature/vegetation feature space regression [18; 19; 20], active and passive microwave data fusion [16; 21; 22; 23], machine learning [24; 25; 26], deep learning [27; 28], geostatistical methods [29; 30; 31], and data assimilation methods [32; 33; 34]. [PERSON] et al. investigated the applicability of a convolutional long short-term memory network (ConvLSTM) algorithm for predicting SM and filling observational gaps in south Louisiana in the United States [28]. [PERSON] et al. designed a new multivariate sequential predictive model that utilizes the ensemble empirical mode decomposition (EEMD) algorithm hybridized with extreme learning machines (ELMs) to forecast soil moisture (SM) over weekly horizons [35]. [PERSON] et al. developed a geographically weighted area-to-area regression kriging (GWATARK) model for the upstream region of the Heihe River Basin [36]. [PERSON] et al. used gradient boosting decision tree regression (GBDT) with SMAP L3_SM_P to produce a 1 km SM product over the Qinghai-Tibet Plateau (QTP) [37]. Moreover, [PERSON] et al. compared six downscaling strategies, including artificial neural networks, Bayesian estimation, decision trees (CART), nearest-neighbor algorithms, random forests, and support-vector machines [38]. [PERSON] et al. compared the performances of five SM fitting models (i.e., multiple statistical regression, DisPATCh, random forest, Gaussian process regression, and area-to-area regression Kriging) for SM downscaling [39]. Generally, these methods are characterized by inherent advantages, disadvantages, background theories, suitable operating environments, and other specific assumptions, which have been thoroughly analyzed in [40] and [41; 42]. As discussed above, current SM analyses (e.g., gap-filling, downscaling, and forecasting) generally rely on existing SM fitting methods, with some based on comparisons between multiple methods. However, few studies have focused on general method optimization, which is directly related to the subsequent accuracy of the resulting SM products (i.e., products after gap-filling, downscaling, and/or forecasting). A possible issue in existing SM fitting methods is that it is often difficult to capture data characteristics in feature space. For example, SM products generally contain unique image signal characteristics at different image frequencies in the time and space domains. In SM analysis applications, image signal characteristics at different scales--caused by scale-invariance of spatial heterogeneity--also cannot be ignored [43; 44]. Therefore, characteristics at a specific spatial resolution cannot be similarly extrapolated to other SM resolutions. In response to these general challenges, we propose a plausible strategy (i.e., optimized wavelet-coupled fitting method, or OWCM) to optimize the general SM fitting methods (GM) via the application of the wavelet transform (WT) technique. WT is a powerful technique for separating the high- and low-frequency feature information of an image when characterizing spatial features across multiple scales [44]. It has been applied for remote sensing data fusion [45; 46]. Detailed information obtained by WT decomposition can potentially improve upon regression fitting accuracy. In this work, eight fine-resolution SM datasets are generated using four separate GMs--elastic network regression, area-to-area regression Kriging, random forest regression, and neural network regression--each fitted both with and without application of the WT technique.