## 6 Conclusions  
In this study, we investigated the short-term SM prediction based on satellite-derived data with LSTM. For this purpose, the static and dynamic features were combined to create sequential input data and used in situ SM measurements of 103 stations from ISMN as an output to train an LSTM model. Our approach uses soil texture and topographical data as static features and satellite (S1 and SMAP) and climate data as dynamic features. As SM monitoring is crucial for water resource management, we employed the SAR data due to its lower sensitivity to atmospheric conditions than optical data. To optimize the LSTM models' hyperparameters, we used the gridSearchCV algorithm. After the optimization, the overall testing accuracy of the model was calculated as \(R^{2}=0.87\), \(RMSE=0.046\), and \(MAE=0.033\). The values obtained from different stations are summarized in Appendix A, including the station ID, network and station name, soil texture, NDVI mean and max values, climate, land cover classes, and the corresponding MAE values. During our investigations, it was observed that the model's prediction performance is affected by the soil texture, vegetation status, and climate conditions. Variations in soil texture change the soil water-holding capacity. In the case in which the amount of sand was dominant, the SM values were easier to model than in the case of silt and clay dominance due to the low SM values and fewer fluctuations in sandy soils. We also observed that vegetation affects the interaction between the SAR signal and the soil. Thus, the model's prediction ability was lowered in vegetated areas with high NDVI values. Moreover, the model can predict better under dry climate conditions, such as arid and semi-arid climates in relatively low precipitation. This study used satellite-based products to create a model to forecast SM values. For operational purposes, we know that obtaining soil texture data on the pixel level is challenging. However, we can overcome this by conducting an intensive sampling campaign for soil texture, or existing models can be used [75], which employs S1 and S2 multi-temporal data. In the future, we plan to combine the LSTM model with the attention mechanism to study the contribution of each variable to SM prediction. The LSTM model combined with the attention mechanism can determine the importance of each feature and its temporal relationship with SM phenomena. Thus, we can increase the accuracy of the model predictions and explain the physical behavior of the black-box model. Figure 11: Overall mean absolute error for first-order (**a**) and second-order (**b**) Köppen–Geiger climate classes [59]. **Author Contributions:** Conceptualization, M.F.C., M.S.I, O.Y., N.F. and E.E. ; methodology, M.F.C., M.S.I., O.Y. and N.F. ; validation, M.F.C. and M.S.I. ; formal analysis, M.F.C. and M.S.I. ; investigation, M.F.C., O.Y. and M.S.I. ; resources, O.Y. and N.F. ; data curation, M.F.C., M.S.I., O.Y. and N.F. ; writing--original draft preparation, M.F.C. ; writing--review and editing, M.F.C., M.S.I., O.Y., N.F. and E.E. ; visualization, M.F.C., M.S.I. and O.Y. ; supervision, E.E. All authors have read and agreed to the published version of the manuscript. **Funding:** The corresponding author carried out this study as a result of his research at the University of Valencia during the period of support by the Scientific and Technological Research Council of Turkishye (TUBITAK) 2214/A fellowship program numbered 1059B141900633. **Data Availability Statement:** Data that are used in this research can be obtained from the following sources.