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.

The purpose of this study is to analyze the correlation between surface air temperature (SAT) and land surface temperature (LST) based on land use when heat and cold waves occur and to predict the distribution of SAT using the long short-term memory (LSTM) of TensorFlow. For the correlation analysis, the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime LST and maximum, minimum, and mean SAT were measured at 79 weather stations of the Korea Meteorological Administration (KMA) from 2008 to 2018. As a result of the correlation analysis between SAT and LST, the maximum SAT (T_MX) had a good correlation with the daytime LST of Terra MODIS, with a Pearson’s correlation coefficient (R) of 0.92 and root mean square error (RMSE) of 4.8 °C, and the minimum SAT (T_MN) showed a good correlation with the nighttime LST of Terra MODIS, with an R of 0.93 and RMSE of 4.2 °C. When analyzing temperature characteristics by land use (urban, paddy, upland crop, forest, grass, wetland, bare field, and water), it was confirmed that the climate mitigation effect of the wetland and vegetation area appeared in the LSTs and the observed SAT. In the cold wave period, the average temperatures for urban and wetland areas was the highest, and the average temperature for wetland and forest was not higher than that of other land use classes. As the SAT results predicted through the LSTM model, the accuracy of the T_MN during the cold wave period was 0.59 for the coefficient of determination (R²), 3.1 °C for RMSE, and 0.76 for the index of agreement (IoA), while the accuracy of the T_MX for the heat wave period was 0.24 for R², 2.23 °C for RMSE, and 0.63 for IoA.

This study evaluated watershed health (WH) change using reference values for environmental changes at various times. Land use in 1985 was defined as the reference value under the most natural conditions, and the WH for the years 1995 to 2019 was calculated in comparison to 1985. The proposed method was used to assess the WH of 78 standard subbasins in South Korea’s Geum River Basin (GRB), where complex land-use change has occurred since 1995. For evaluating hydrology and water quality (WQ) health index, Soil and Water Assessment Tool (SWAT) and four land-use maps (1985, 1995, 2008, and 2019) were used to simulate the hydrology and WQ. A multivariate normal distribution (MND) from poor (0) to good (1) was used to assess WH based on SWAT modeling results. Based on the reference value, the WQ health from 1995 to 2019 changed to within 0.1, while the range of changes in the hydrology index was analyzed over 0.18. As a result of WH changes from 1985 to 2019, hydrological health deteriorated in high-density urbanized subbasins, while WQ health deteriorated in upland-cultivation-increased subbasins. This study provides useful information for recognizing potential WH issues related to long-term environmental changes.

In this study, we applied machine learning to estimate soil moisture levels in South Korea by harnessing data from the Sentinel-1 C-band synthetic aperture radar (SAR). Our approach incorporated not only the relationship between backscattering coefficients and soil moisture but also diverse physical characteristics. This encompassed topographic information, soil physics data, and antecedent precipitation which is a hydrological factor influencing the initial condition of soil moisture. We applied a variety of machine-learning techniques and conducted a comprehensive analysis to compare the performance of each model.