Sentinel-1A/B SAR 센서 기반 고해상도 토양수분 산정
Estimation of High-Resolution Soil Moisture based on Sentinel-1A/B SAR Sensors
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다운로드
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참고문헌
키워드
초록
In this study, we estimated the spatially-distributed soil moisture at the high resolution (10 m×10 m) 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 providedfrom ESA (European Space Agency), and then the pre-processed data were converted to the backscatter coefficients. The regression equations werederived based on the relationships between the TDR (Time Domain Reflectometry)-based soil moisture measurements and the converted backscattercoefficients. The TDR measurements from the 51 RDA (Rural Development Administration) monitoring sites were used to derive the regressionequations. Then, the soil moisture values were estimated using the derived regression equations with the input data of Sentinel-1A/B based backscattercoefficients. Overall, the soil moisture estimates showed the linear trends compared to the TDR measurements with the high Pearson’s correlations (morethan 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) sitesshowed the negative correlations with the TDR measurements. These uncertainties might be due to limitations of soil surface penetration depths of SARsensors and complicated land surface conditions (artificial constructions near the TDR site) at urban regions. These results may infer that qualities ofSentinel-1A/B based soil moisture products are dependent on land surface conditions. Although uncertainties exist, the Sentinel-1A/B basedhigh-resolution soil moisture products could be useful in various areas (hydrology, agriculture, drought, flood, wild fire, etc.).
Abstract
In this study, we estimated the spatially-distributed soil moisture at the high resolution (10 m×10 m) 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 providedfrom ESA (European Space Agency), and then the pre-processed data were converted to the backscatter coefficients. The regression equations werederived based on the relationships between the TDR (Time Domain Reflectometry)-based soil moisture measurements and the converted backscattercoefficients. The TDR measurements from the 51 RDA (Rural Development Administration) monitoring sites were used to derive the regressionequations. Then, the soil moisture values were estimated using the derived regression equations with the input data of Sentinel-1A/B based backscattercoefficients. Overall, the soil moisture estimates showed the linear trends compared to the TDR measurements with the high Pearson’s correlations (morethan 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) sitesshowed the negative correlations with the TDR measurements. These uncertainties might be due to limitations of soil surface penetration depths of SARsensors and complicated land surface conditions (artificial constructions near the TDR site) at urban regions. These results may infer that qualities ofSentinel-1A/B based soil moisture products are dependent on land surface conditions. Although uncertainties exist, the Sentinel-1A/B basedhigh-resolution soil moisture products could be useful in various areas (hydrology, agriculture, drought, flood, wild fire, etc.).
참고문헌 (14)
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