###### Abstract
By spatially interpolating SM values of stations to form 0.25\({}^{\circ}\) grid cells, the trained model can predict SM in a quasi-global way. Although the sensor measurements provide more reliable estimations of SM values, the dependency of the model on SM sensors limits the use of the model within specific regions where in situ measurements exist. The lack of measurements in high latitudes resulted in poorer forecasts of SM values, specifically in arid regions. Even though in situ measurements play a crucial role in understanding SM, their spatial coverage and network-related problems make them limited in global studies. Recent developments in satellite-based remote sensing allowed continuous monitoring of the Earth's surface. In order to overcome the problems encountered in SM predictions when using in situ measurements, satellite data from microwave remote sensing has been used excessively [22; 23]. In this context, satellite images are the key to breaking free from the dependency of SM prediction from in situ sensors. The data from the NASA soil moisture active passive (SMAP) [24] and ESA soil moisture and ocean salinity (SMOS) [25] missions are a valuable asset for the global SM monitoring with their 2-3 days temporal resolution. In 2020, ref. [26] expanded the near real-time SM predictions by integrating time series data from SMAP and SMOS missions by using a statistical approach to overcome the inconsistencies between the different SM retrieval algorithms. Although SMAP and SMOS SM products enable the monitoring of Earth's surface moisture in high temporal resolution, their applications are constrained due to their coarse spatial resolution. To overcome this limitation, researchers [27; 28] used downscaling methods by merging higher-resolution satellite images with lower-resolution SMAP/SMOS data to achieve improved spatial resolution SM predictions. Even though these downscaling efforts are applicable in predicting SM, the generated maps still have an insufficient spatial resolution (\(\sim\)5.6 km) for applications such as agricultural monitoring. In this regard, the launch of the Sentinel SAR satellites by ESA under the Copernicus Programme paved the way for accurate SM retrieval in smaller scale by acquiring higher spatial resolution microwave remote sensing images [29; 30; 31; 32; 33]. SM retrieval from remote sensing images has been improved by the state-of-art machine learning-based regression techniques owing to their ability to learn the relationship between predictors and SM from data [34; 35; 36; 37]. An extensive review on the use of machine learning algorithms for predicting SM can be found in [38]. As computers have improved in performance, deep learning (DL) algorithms have become increasingly popular, as they can handle nonlinear and complex relationships between input and output [39].The SM forecasting studies that use remote sensing images exploited the ability of DL models to capture the spatial and temporal dynamics of SM at the expense of large datasets and high computational costs [40; 41; 42; 43; 44; 45; 4]. Among the different DL methods, artificial neural networks (ANNs) have been proposed to estimate SM from microwave remote sensing images integrated with some auxiliary data [46]. For example, although [47] coupled S1 images with soil texture information, ref. [44] used soil texture and soil temperature data to improve the prediction accuracy of SM retrieval. As an alternative to soil texture data, ref. [48] include climate and topography data to the ANN model. Furthermore, in [42], the combination of soil texture, topography, and climate data was utilized to improve the artificial neural network (ANN) model's performance. The recurrent neural network (RNN) is a DL technique that considers the sequential relationships between input data and their effects on the output data. Therefore, such DL models are more appropriate when the sequence modeling tasks are needed, such as SM prediction.