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http://dx.doi.org/10.3741/JKWRA.2022.55.6.393

A preliminary assessment of high-spatial-resolution satellite rainfall estimation from SAR Sentinel-1 over the central region of South Korea  

Nguyen, Hoang Hai (Sejong Rain Co., Ltd, In-house Venture of K-water)
Jung, Woosung (K-water Research Institute)
Lee, Dalgeun (National Disaster Management Research Institute)
Shin, Daeyun (Sejong Rain Co., Ltd, In-house Venture of K-water)
Publication Information
Journal of Korea Water Resources Association / v.55, no.6, 2022 , pp. 393-404 More about this Journal
Abstract
Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.
Keywords
High-spatial-resolution; SAR Sentinel-1; Satellite rainfall; Bottom-up approach; Soil moisture; Rain gauges;
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