• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.599-608
• /
• 2023

The increasing interest in soil moisture data using satellite data for applications of hydrology, meteorology, and agriculture has led to the development of methods for generating soil moisture maps of variable resolution. This study demonstrated the capability of generating soil moisture maps using Sentinel-1 and Sentinel-2 data provided by Google Earth Engine (GEE). The soil moisture map was derived using synthetic aperture radar (SAR) image and optical image. SAR data provided by the Sentinel-1 analysis ready data in GEE was applied with normalized difference vegetation index (NDVI) based on Sentinel-2 and Environmental Systems Research Institute (ESRI)-based Land Cover map. This study produced a soil moisture map in the research area of Victoria, Australia and compared it with field measurements obtained from a previous study. As for the validation of the applied method's result accuracy, the comparative experimental results showed a meaningful range of consistency as 4-10%p between the values obtained using the algorithm applied in this study and the field-based ones, and they also showed very high consistency with satellite-based soil moisture data as 0.5-2%p. Therefore, public open data provided by GEE and the algorithm applied in this study can be used for high-resolution soil moisture mapping to represent regional land surface characteristics.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_4
• /
• pp.1935-1943
• /
• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Spatial Information Research
• /
• v.14 no.4 s.39
• /
• pp.363-377
• /
• 2006

Mean annual soil loss was calculated and critical soil erosion areas were identified for the Congaree River Basin in South Carolina, USA using the Revised Universal Soil Loss Equation (RUSLE) model. In the RUSLE model, the mean annual soil loss (A) can be calculated by multiplying rainfall-runoff erosivity (R), soil erodibility (K), slope length and steepness (LS), crop-management (C), and support practice (P) factors. The critical soil erosion areas can be identified as the areas with soil loss amounts (A) greater than the soil loss tolerance (T) factor More than 10% of the total area was identified as a critical soil erosion area. Among seven subwatersheds within the Congaree River Basin, the urban areas of the Congaree Creek and the Gills Creek subwatersheds as well as the agricultural area of the Cedar Creek subwatershed appeared to be exposed to the risk of severe soil loss. As a prototype model for examining future effect of human and/or nature-induced changes on soil erosion, the RUSLE model customized for the area was embedded into ESRI ArcGIS ArcMap 9.0 using Visual Basic for Applications. Using the embedded model, users can modify C, LS, and P-factor values for each subwatershed by changing conditions such as land cover, canopy type, ground cover type, slope, type of agriculture, and agricultural practice types. The result mean annual soil loss and critical soil erosion areas can be compared to the ones with existing conditions and used for further soil loss management for the area.

• Journal of Environmental Science International
• /
• v.27 no.8
• /
• pp.665-675
• /
• 2018

The purpose of this study was to identify the characteristics of the home range and habitat use of Rhinolophus ferrumequinum individuals that inhabit urban areas. The bats were tracked using GPS tags. For analysis of the home rage, Minimum Convex Polygon (MCP) and Kernel Home Range (KHR) methods were used. The landscape types of all positional information were analyzed using ArcGIS 9.3.1 (ESRI Inc.). The average home range of 16 R. ferrumequinum individuals was $68.63{\pm}25.23ha$, and the size of the overall home range for the females ($85.49{\pm}25.40ha$) was larger than that for the males ($51.76{\pm}8.30ha$). The highest average home range for the males was found in August ($61.21{\pm}0.01ha$), whereas that for the females was found in September ($112.27{\pm}5.94ha$). The size of 50% KHR ranged from a minimum of 13.26 ha to a maximum of 31.00 for the males and a minimum of 8.02 ha to a maxinum of 42.16 ha for the females, showing no significant differences between the two sexes. In addition, males and females showed no differences in the size of 50% KHR in the monthly comparisons. However, the females showed differences in the size of their core area between periods before and after giving birth. The comparisons between 100% MCP and 50% KHR showed that the types of habitats used by R. ferrumequinum were mostly forest areas, including some farmlands. In addition, comparisons with a land cover map showed that the proportion of broad-leaved forests was the highest, followed by that of mixed forests.