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Spatial disaggregation of ASCAT soil moisture under all sky condition using support vector machine

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Abstract

With recent advances in downscaling methodologies, soil moisture (SM) estimation using microwave remote sensing has become feasible for local application. However, disaggregation of SM under all sky conditions remains challenging. This study suggests a new downscaling approach under all sky conditions based on support vector regression (SVR) using microwave and optical/infrared data and geolocation information. Optically derived estimates of land surface temperature and normalized difference vegetation index from MODerate Resolution Imaging Spectroradiometer land and atmosphere products were utilized to obtain a continuous spatio-temporal input datasets to disaggregate SM observation from Advanced SCATterometer in South Korea during 2015 growing season. SVR model was compared to synergistic downscaling approach (SDA), which is based on physical relationship between SM and hydrometeorological factors. Evaluation against in situ observations showed that the SVR model under all sky conditions (R: 0.57 to 0.81, ubRMSE: 0.0292 m3 m−3 to 0.0398 m3 m−3) outperformed coarse ASCAT SM (R: 0.55 to 0.77, ubRMSE: 0.0300 m3 m−3 to 0.0408 m3 m−3) and SDA model (mean R: 0.56 to 0.78, ubRMSE: 0.0324 m3 m−3 to 0.0436 m3 m−3) in terms of statistical results as well as sensitivity with precipitation. This study suggests that the spatial downscaling technique based on remote sensing has the potential to derive high resolution SM regardless of weather conditions without relying on data from other sources. It offers an insight for analyzing hydrological, climate, and agricultural conditions at regional to local scale.

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Acknowledgements

This research was supported by Space Core Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2014M1A3A3A02034789) and the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (NRF-2016R1A2B4008312).

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  1. Environment and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, 440-746, Gyeonggi-do, Republic of Korea

    Seongkyun Kim, Jaehwan Jeong, Muhammad Zohaib & Minha Choi

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  1. Seongkyun Kim
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Correspondence to Minha Choi.

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Kim, S., Jeong, J., Zohaib, M. et al. Spatial disaggregation of ASCAT soil moisture under all sky condition using support vector machine. Stoch Environ Res Risk Assess 32, 3455–3473 (2018). https://doi.org/10.1007/s00477-018-1620-3

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