• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.81-91
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• 2021

Synthetic Apreture Radar (SAR) is attracting attentions with its possibility of producing high resolution data that can be used for soil moisture estimation. High resolution soil moisture data enables more specific observation of soil moisture than existing soil moisture products from other satellites. It can also be used for studies of wildfire, landslide, and flood. The SAR based soil moisture estimation should be conducted considering vegetation, which affects backscattering signals from the SAR sensor. In this study, a SAR based soil moisture estimation at regions covered with various vegetation types on the middle area of Korea (Cropland, Grassland, Forest) is conducted. The representative backscattering model, Water Cloud Model (WCM) is used for soil moisture estimation over vegetated areas. Radar Vegetation Index (RVI) and in-situ soil moisture data are used as input factors for the model. Total 6 study areas are selected for 3 vegetation types according to land cover classification with 2 sites per each vegetation type. Soil moisture evaluation result shows that the accuracy of each site stands out in the order of grassland, forest, and cropland. Forested area shows correlation coefficient value higher than 0.5 even with the most dense vegetation, while cropland shows correlation coefficient value lower than 0.3. The proper vegetation and soil moisture conditions for SAR based soil moisture estimation are suggested through the results of the study. Future study, which utilizes additional ancillary vegetation data (vegetation height, vegetation type) is thought to be necessary.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.174-177
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• 2004

Differential interferometric phases from JERS-1 L­band data sets show spatial variation of path-length ranging from a few mm to several cm. The variation may be caused by changes in soil moisture contents, i.e. variation of penetration depth and the swelling of soils. Although the amount of total effect caused by soil moisture is not measurable, it is clear that the soil moisture according to precipitation is another factor to be considered in DInSAR analysis. We also discuss DInSAR characteristics in a rice paddy according to irrigation conditions, and discrimination of hydrological features such as stream channels and watershed boundaries by applying DInSAR technique.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.4
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• pp.459-465
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• 2013

This paper presents a differential interferometry technique for soil moisture change detection by measuring surface-height variation. COSMO-SkyMed SAR images were used to verify the DInSAR(differential interferometric SAR) technique. The soil penetration depth changes according to soil moisture, that causes phase change of the received signal. The height of soil surface and its displacement can be detected by a radar interferometry technique using phase difference of two received signals. To retrieve displacement variation, one of three SAR images is used as a reference image. Reference image and other two images are processed by the differential interferometry technique in the same area. The soil moisture was measured for the test sites to verify the DInSAR technique. The penetration depth is calculated by using the in-situ measured soil moisture data and it is compared with the displacement values acquired by the DInSAR technique.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.11-20
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• 2020

We estimated the spatio-temporally distributed soil moisture using Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images and soil moisture data assimilation technique in South Korea. Soil moisture data assimilation technique can extract the hydraulic parameters of soils using observed soil moisture and GA (Genetic Algorithm). The SWAP (Soil Water Atmosphere Plant) model associated with a soil moisture assimilation technique simulates the soil moisture using the soil hydraulic parameters and meteorological data as input data. The soil moisture based on Sentinel-1A/B was validated and evaluated using the pearson correlation and RMSE (Root Mean Square Error) analysis between estimated soil moisture and TDR soil moisture. The soil moisture data assimilation technique derived the soil hydraulic parameters using Sentinel-1A/B based soil moisture images, ASOS (Automated Synoptic Observing System) weather data and TRMM (Tropical Rainfall Measuring Mission)/GPM (Global Precipitation Measurement) rainfall data. The derived soil hydrological parameters as the input data to SWAP were used to simulate the daily soil moisture values at the spatial domain from 2001 to 2018 using the TRMM/GPM satellite rainfall data. Overall, the simulated soil moisture estimates matched well with the TDR measurements and Sentinel-1A/B based soil moisture under various land surface conditions (bare soil, crop, forest, and urban).

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

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.973-983
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• 2020

Recently, SAR (Synthetic Aperture Radar) is being highlighted as a solution to the coarse spatial resolution of remote sensing data in water resources research field. Spatial resolution up to 10 m of SAR backscattering coefficient has facilitated more elaborate analyses of the spatial distribution of soil moisture, compared to existing satellite-based coarse resolution (>10 km) soil moisture data. It is essential, however, to multilaterally analyze how various hydrological and environmental factors affect the backscattering coefficient, to utilize the data. In this study, soil moisture estimated by WCM (Water Cloud Model) and linear regression is compared with in-situ soil moisture data at 5 soil moisture observatories in the Korean peninsula. WCM shows suitable estimates for observing instant changes in soil moisture. However, it needs to be adjusted in terms of errors. Soil moisture estimated from linear regression shows a stable error range, but it cannot capture instant changes. The result also shows that the effect of soil moisture on backscattering coefficients differs greatly by land cover, distribution of vegetation, and water content of vegetation, hence that there're still limitations to apply preexisting models directly. Therefore, it is crucial to analyze variable effects from different environments and establish suitable soil moisture model, to apply SAR to water resources fields in Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.89-99
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• 2019

In this study, we estimated the spatially-distributed soil moisture at the high resolution ($10m{\times}10m$) using the satellite-based Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images. The Sentinel-1A/B raw data were pre-processed using the SNAP (Sentinel Application Platform) tool provided from ESA (European Space Agency), and then the pre-processed data were converted to the backscatter coefficients. The regression equations were derived based on the relationships between the TDR (Time Domain Reflectometry)-based soil moisture measurements and the converted backscatter coefficients. The TDR measurements from the 51 RDA (Rural Development Administration) monitoring sites were used to derive the regression equations. Then, the soil moisture values were estimated using the derived regression equations with the input data of Sentinel-1A/B based backscatter coefficients. Overall, the soil moisture estimates showed the linear trends compared to the TDR measurements with the high Pearson's correlations (more than 0.7). The Sentinel-1A/B based soil moisture values matched well with the TDR measurements with various land surface conditions (bare soil, crop, forest, and urban), especially for bare soil (R: 0.885~0.910 and RMSE: 3.162~4.609). However, the Mandae-ri (forest) and Taean-eup (urban) sites showed the negative correlations with the TDR measurements. These uncertainties might be due to limitations of soil surface penetration depths of SAR sensors and complicated land surface conditions (artificial constructions near the TDR site) at urban regions. These results may infer that qualities of Sentinel-1A/B based soil moisture products are dependent on land surface conditions. Although uncertainties exist, the Sentinel-1A/B based high-resolution soil moisture products could be useful in various areas (hydrology, agriculture, drought, flood, wild fire, etc.).

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.571-585
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• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1185-1193
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• 2023

The increasing interest in soil moisture data from satellite imagery for applications in hydrology, meteorology, and agriculture has led to the development of methods to produce variable-resolution soil moisture maps. Research on accurate soil moisture estimation using satellite imagery is essential for remote sensing applications. The purpose of this study is to generate a soil moisture estimation map for a test area using KOMPSAT-3/3A and KOMPSAT-5 SAR imagery and to quantitatively compare the results with soil moisture data from the Soil Moisture Active Passive (SMAP) mission provided by NASA, with a focus on accuracy validation. In addition, the Korean Environmental Geographic Information Service (EGIS) land cover map was used to determine soil moisture, especially in agricultural and forested regions. The selected test area for this study is the western part of Jeju, South Korea, where input data were available for the soil moisture estimation algorithm based on the Water Cloud Model (WCM). Synthetic Aperture Radar (SAR) imagery from KOMPSAT-5 HV and Sentinel-1 VV were used for soil moisture estimation, while vegetation indices were calculated from the surface reflectance of KOMPSAT-3 imagery. Comparison of the derived soil moisture results with SMAP (L-3) and SMAP (L-4) data by differencing showed a mean difference of 4.13±3.60 p% and 14.24±2.10 p%, respectively, indicating a level of agreement. This research suggests the potential for producing highly accurate and precise soil moisture maps using future South Korean satellite imagery and publicly available data sources, as demonstrated in this study.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.947-960
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• 2017

Soil moisture plays an important role to affect the Earth's radiative energy balance and water cycle. In general, satellite observations are useful for estimating the soil moisture content. Passive microwave satellites have an advantage of direct sensitivity on surface soil moisture. However, their coarse spatial resolutions (10-36 km) are not suitable for regional-scale hydrological applications. Meanwhile, in-situ ground observations of point-based soil moisture content have the disadvantage of spatially discontinuous information. This paper presents an experimental soil moisture retrieval using Sentinel-1 SAR (Synthetic Aperture Radar) with 10m spatial resolution for cropland in South Korea. We developed a soil moisture retrieval algorithm based on the technique of linear regression and SVR (support vector regression) using the ground observations at five in-situ sites and Sentinel-1 SAR data from April to October in 2015-2017 period. Our results showed the polarization dependency on the different soil sensitivities at backscattered signals, but no polarization dependence on the accuracies. No particular seasonal characteristics of the soil moisture retrieval imply that soil moisture is generally more affected by hydro-meteorology and land surface characteristics than by phenological factors. At the narrower range of incidence angles, the relationship between the backscattered signal and soil moisture content was more distinct because the decreasing surface interference increased the retrieval accuracies under the condition of evenly distributed soil moisture (during the raining period or on the paddy field). We had an overall error estimate of RMSE (root mean square error) of approximately 6.5%. Our soil moisture retrieval algorithm will be improved if the effects of surface roughness, geomorphology, and soil properties would be considered in the future works.