Overview of the isolation forest method. Light green circles represent common normal samples, dark green circles represent uncommon normal samples, and red circles represent outliers.

Overview of the isolation forest method. Light green circles represent common normal samples, dark green circles represent uncommon normal samples, and red circles represent outliers.

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The spatial distribution of soil moisture (SM) was estimated by a multiple quantile regression (MQR) model with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and filtered SM data from 2013 to 2015 in South Korea. For input data, observed precipitation and SM data were collected from the Korea Meteorological Administration and various...

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... training step structures basic isolation trees that build various subsamples using random sampling from the training set. The testing step calculates the length of the path by passing samples to obtain an anomaly score through isolation trees ( Figure 3). To isolate every subsample and stage, a tree structure can be used effectively because there are few anomalies far from normal points. ...

Citations

... The main differences between these two methods are the electromagnetic energy source, the wavelength region of the electromagnetic spectrum used, the response measured by the sensor and so on [8]. The former estimates SMC by analyzing correlations between SMC and various outputs from optical satellites, such as surface temperature and vegetation-related indices, and uses various statistical, empirical, or machine learning techniques [9][10][11]. The latter method directly estimates SMC using surface backscatter difference water index (NDWI) [42], estimated by optical satellites or ground vegetation measurements [40]. ...
... As an alternative to vegetation parameters, precipitation data were applied by borrowing the concept of antecedent precipitation from the Soil Conservation Service-Curve Number (SCS-CN) method in some studies [9][10][11][44][45][46]. The SCS-CN method was developed by the U.S. Soil Conservation Service (SCS) to create the synthetic unit hydrograph [47]. ...
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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-km 2 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), topo-graphic (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.
Article
This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R²) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R² was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R² was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R² of 0.69 and RMSE of 2.88%.