This study estimates soil moisture content (SMC) using Sentinel-1A/B C-band synthetic aperture radar (SAR) images and an artificial neural network (ANN) over a 40 × 50-km² area located in the Geum River basin in South Korea. The hydrological components characterized by the antecedent precipitation index (API) and dry days were used as input data as well as SAR (cross-polarization (VH) and copolarization (VV) backscattering coefficients and local incidence angle), topographic (elevation and slope), and soil (percentage of clay and sand)-related data in the ANN simulations. A simple logarithmic transformation was useful in establishing the linear relationship between the observed SMC and the API. In the dry period without rainfall, API did not decrease below 0, thus the Dry days were applied to express the decreasing SMC. The optimal ANN architecture was constructed in terms of the number of hidden layers, hidden neurons, and activation function. The comparison of the estimated SMC with the observed SMC showed that the Pearson’s correlation coefficient (R) and the root mean square error (RMSE) were 0.85 and 4.59%, respectively.

This study attempts to estimate spatial soil moisture in South Korea (99,000 km²) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R²) of the original LST and observed LST was 0.71, and the R² of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R² values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R² values were between 0.28 and 0.67. The reason for R² values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R² and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the variability of soil moisture.

This study was to evaluate the groundwater-level behavior in Geum River Basin (9645.5 km²) of South Korea with HadGEM3-RA RCP 4.5 and 8.5 climate change scenarios and future groundwater use data using the soil and water assessment tool (SWAT). Before evaluating future groundwater behavior, the SWAT model was calibrated and validated using the daily inflows and storage of two dams (DCD and YDD) in the basin for 11 years (2005–2015), the daily groundwater-level observation data at five locations (JSJS, OCCS, BEMR, CASS, and BYBY), and the daily inflow and storage of three weir locations (SJW, GJW, and BJW) for three years and five months (August 2012 to December 2015). The Nash–Sutcliffe efficiency (NSE) and the coefficient of determination (R²) of two dam inflows was 0.55–0.70 and 0.67–0.75. For the inflows of the three weirs, NSE was 0.57–0.77 and R² was 0.62–0.81. The average R² value for the groundwater levels of the five locations ranged from 0.53 to 0.61. After verifying the SWAT for hydrologic components, we evaluated the behavior of future groundwater levels by future climate change scenarios and estimated future ground water use by Korean water vision 2020 based on ground water use monitoring data. The future groundwater-level decreased by −13.0, −5.0, and −9.0 cm at three upstream locations (JSJS, OCCS, and BEMR) among the five groundwater-level observation locations and increased by +3.0 and +1.0 cm at two downstream locations (CASS and BYBY). The future groundwater level was directly affected by the groundwater recharge, which was dependent on the seasonal and spatial precipitations in the basin.

In this study, we evaluated the aquatic ecosystem health (AEH) with five grades (A; very good to E; very poor) of FAI (Fish Assessment Index), TDI (Trophic Diatom Index), and BMI (Benthic Macroinvertebrate Index) using the results of SWAT (Soil and Water Assessment Tool) stream water temperature (WT) and quality (T-N, T-P, NH₄, NO₃, and PO₄). By applying Random Forest, one of the machine learning algorithms for classification analysis, each AEH index was trained and graded from the SWAT results. For Han river watershed (34,418 km²) in South Korea, the 8 years (2008~2015) observed AEH data of Spring and Fall periods at 86 locations from NAEMP (National Aquatic Ecological Monitoring Program) were used. The AEH was separately trained for Spring (FAI_s, TDI_s, and BMI_s) and Fall (FAI_a, TDI_a, and BMI_a), and the AEH results of Random Forest with SWAT (WT, T-N, T-P, NH₄, NO₃, and PO₄) as input variables showed the accuracy of 0.42, 0.48, 0.62, 0.45, 0.4, and 0.58, respectively. The reason for low accuracy was from the weak strength of the individual trees and high correlation between the trees composing the Random Forest due to the data imbalance. The AEH distribution results showed that the number of Grade A of total FAI, TDI, and BMI were 84, 0, and 158 respectively and they were mostly located at the upstream watersheds. The number of Grade E of total FAI, TDI, and BMI were 4, 50, and 13 and they were shown at downstream watersheds.