Towards a novel high-spatial-resolution satellite rainfall product from C-band SAR Sentinel-1 over Central South Korea based on a bottom-up approach

Abstract

ABSTRACT Finer spatial resolution (roughly 1 km) of terrestrial rainfall is necessary for urban hydrology and microscale agriculture demands but still limited in various ‘top-down’ satellite rainfall products (SRPs). This study aims to evaluate the potential application of an emerged ‘bottom-up’ approach, the parameterized SM2RAIN model, to the C-band Synthetic Aperture Radar Sentinel-1 (S1) satellites for a high-spatial-resolution SRP (SM2RAIN-S1, 0.01º/6-day) over Central South Korea. By comparing its performance with the Integrated Multi-satellitE Retrieval for Global Precipitation Measurement (IMERG) Early Run product at a similar resolution against a common reanalysis rainfall product and rain gauges network during a 1-year period, we explore whether this novel bottom-up SRP can add improvements to the conventional top-down SRP regarding differences in climate areas (coast and inland), vegetation covers (cropland and mixed forest), and seasonality (rainy and non-rainy seasons). The SM2RAIN-S1 demonstrates its feasibility in capturing general spatiotemporal rainfall patterns in the study area compared to other rainfall products; however, noises associated with the S1 images still occurred. For the comparison over climate-vegetation areas, the extended triple collocation revealed that the SM2RAIN-S1 is relatively superior to the IMERG mainly over inland and forest regions, suggesting the suitability of 6-day SM2RAIN-S1 data to the stable and lower temporal rainfall variation in such regions, where the IMERG data are limited. The comparison against rain gauge data over rainfall seasons indicated that the SM2RAIN-S1 generally outperformed IMERG for bias and Root-Mean-Square Error, particularly during the rainy season. Moreover, the SM2RAIN-S1 shows its feasibility for moderate and heavy rain detection, while it is less effective in detecting light rain events compared to the IMERG. This study encourages the potential use of the bottom-up SM2RAIN-S1 rainfall product to complement, downscale, and correct the biases in top-down SRPs; together with supporting their integration for improving high spatio-temporal resolution rainfall products.