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• Surface characteristics captured soil moisture variations more accurately than regional scales. • Surface heterogeneities have important & complex effect on soil moisture spatial distribution. • Land surface temperature has a crucial role in soil moisture modeling. • Combined Bootstrap & Triangle method showed promise in reducing estimation uncertainty. Modeling soil moisture (SM) spatial distribution is important for hydrological forecasting, runoff estimation and catchment management and other applications. The objective of this study was to quantify the effect of surface heterogeneity including vegetation, soil, topography etc. on SM at the regional and surface characteristics classes (SCC) scales. Landsat 8 images, ASTER digital elevation model, land cover map, climatic data, and SM measured at 148 locations in the Balikhli-Chay catchment of Iran were used. Greenness, brightness, wetness, and land surface temperature are the surface biophysical characteristics and the topographical characteristics including elevation, slope, aspect, Topographic Wetness Index (TWI) and solar local incident angle calculated using Tasseled Cap Transformation, Single Channel algorithm used in this study. The Triangular method and the bootstrapping model were used to model SM and mitigate prediction uncertainty. Correlation coefficient (r) and Root Mean Square Error (RMSE) between the modeled and measured SM values were used to evaluate the modeled SM. The mean r and RMSE between the modeled and measured SM for different months were 0.83 and 1.84 (volumetric percentage), respectively. The degree of heterogeneity of the spatial distribution of SM in different classes of surface biophysical and topographical characteristics was different. The impact of surface heterogeneity on SM varied across regional and SCC scales. The mean r values between SM and surface biophysical and topographical characteristics in the regional and SCC strategies for different months were 0.41 and 0.54, respectively. Using the SCC scale instead of the regional scale heterogeneity information can increase the accuracy of SM modeling. [ABSTRACT FROM AUTHOR]

• Multitemporal RS images & digital soil mapping were used to predict soil moisture. • Concept of dynamic and static environmental covariates (ECs) were introduced & used. • Dynamic EC helped capture spatiotemporal variation in soil moisture with confidence. • A general model in predicting soil moisture was proposed using multitemporal images. Soil moisture (SM), a critical component of the global hydrological cycle, is affected by individual or combinations of multiple factors including soil properties, climate, and topography. Despite its importance to many disciplines, predicting SM continuously, accurately, and inexpensively over a large area is a great challenge due to its dynamic nature controlled mostly by the spatial and temporal variability of these factors. Static environmental covariates, such as those derived from a digital elevation model, are commonly used in digital soil mapping (DSM); these are typically less suitable for predicting dynamic properties. Easily available multi-temporal satellite images show strong promise to capture this variability. The objective of this study was to predict SM from multi-temporal satellite images using a DSM approach. Specifically, we examined the feasibility of using dynamic, static, and combinations of environmental covariates (ECs) to predict SM in the Balikhli_Chay watershed in Iran on four separate dates in June, July, August, and September 2018 coincident with satellite overpass. Cubist and random forest (RF) machine learning algorithms (MLAs) were trained for making SM predictions for individual dates, and the data was then compiled without considering the date to create generalized models. The baseline for comparisons were the models developed using only static ECs. For June, July, August, and September, Cubist R2 improvements were 96%, 78%, 185% and 120%, respectively. Using the generalized models, R2 improved by as much as 223% and RMSE decreased by as much as 47% when comparing the best SM prediction model in each month to models developed using only static ECs for that same month using the Cubist model. Similar model improvements were seen for the RF model. The generalized Cubist and RF MLAs performed equally well with concordance of 0.91 and 0.90 for Cubist and RF respectively, and low RMSE of 3.04 and 2.98. The best Cubist and RF MLAs by month were always those developed with dynamic, or satellite-derived, ECs. Based on the variable importance statistics, land surface temperature (LST) was the most important EC. This study showed the strong predictions, and the practical feasibility of using multi-temporal satellite data as a dynamic EC that could help to capture the spatial and temporal variations of soil moisture. This approach could likely be extended to other dynamic soil property (e.g., soil temperature). [ABSTRACT FROM AUTHOR]

Accurate information on soil moisture (SM) is critical in various applications including agriculture, climate, hydrology, soil and drought. In this paper, various predictive relationships including regression (Multiple Linear Regression, MLR), machine learning (Random Forest, RF; Triangular regression, Tr) and spatial modeling (Inverse Distance Weighing, IDW and Ordinary kriging, OK) approaches were compared to estimate SM in a semi-arid mountainous watershed. In developing predictive relationship, Remote Sensing datasets including Landsat 8 satellite imagery derived surface biophysical characteristic, ASTER digital elevation model (DEM) derived surface topographical characteristic, climatic data recorded at the synoptic station and in situ SM data measured at Landsat 8 overpass time were utilized, while in spatial modeling, point-based SM measurements were interpolated. While 70%(calibration set) of the measured SM data were used for modeling, 30%(validation set) were used to evaluate modeling accuracy. Finally, the SM uncertainty maps were created for different models based on a bootstrapping approach. Among the environmental parameter sets, land surface temperature (LST) showed the highest impact on the spatial distribution of SM in the region at all dates. Mean R2(RMSE) between measured and modeled SM on three dates obtained from the MLR, RF, IDW, OK, and Tr models were 0.70(1.97%), 0.72(1.92%), 0.59(2.38%), 0.59(2.27%) and 0.71(1.99%), respectively. The results showed that RF and IDW produced the highest and lowest performance in SM modeling, respectively. Generally, the performance of RS-based models was higher than interpolation models for estimating SM due to the influence from combination of topographic parameters and surface biophysical characteristics. Modeled SM uncertainty with different models varies in the study area. The highest uncertainty in SM modeling was observed at the north part of the study area where the surface heterogeneity is high. Using RS data increased the accuracy of SM modeling because they can capture the surface biophysical characteristics and topographical properties heterogeneity. Unlabelled Image • Spectral and spatial based approaches were used to map soil moisture. • Spectral models were superior than interpolation models to estimate soil moisture. • Combined topographic & surface biophysical characters supported higher performance. • Higher surface heterogeneity made soil moisture estimation more uncertain. • Land surface temperature showed the highest impact on soil moisture distribution. [ABSTRACT FROM AUTHOR]