Assessment of remotely sensed soil moisture products and their quality improvement: a case study in South Korea

Moonhyuk Kwon*, Dawei Han

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)
57 Downloads (Pure)


Soil moisture (SM) retrieved from satellite observations has become available at a global scale with relatively high spatial-temporal resolution, and the satellite-derived SM can be useful data sources where in-situ measurements are scarce or not available. In this study, the SM data from two different satellite sensors, the Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer 2 (AMSR2), are evaluated through the comparison with in-situ observation collected from twelve sites over a three-year period (2013-2015) in South Korea. The results reveal that the ASCAT descending overpass (09:30, the local equatorial crossing time) shows a better correlation with the in-situ observation than the ascending overpass (21:30, the local equatorial crossing time), while no significant difference in performance is found for AMSR2. Moreover, ASCAT SM retrieval shows a generally better agreement with in-situ observation. Considering the spatial mismatch and different measurement depths, a cumulative distribution function (CDF) matching method, as well as an exponential filter method, are employed to improve the applicability of satellite-derived SM. Specifically, the observation operators based on CDF matching are derived to find the optimal temporal period and tested by cross-validation. It is found that the CDF matching method split into two groups (i.e., growing and non-growing season) outperforms the other temporal groups. Additionally, considering different observation depths between the in-situ (> 10 cm) and the satellite products (the top soil layer), the root-zone SM (RZSM) is derived from satellite surface SM by using the exponential filter method. For this study, a characteristic time length (T) at each observation depth is optimized by maximizing the r value between the SWI and the in-situ observation. Although the optimal T value generally increases with observation depth, it is clearly seen that T values are highly location-dependent. Given an encouraging improvement of the satellite SM estimation when scaling and filtering method applied, the results obtained in this study show that the satellite SM products have the useful potential for operational applications.
Original languageEnglish
Pages (from-to)14-27
Number of pages14
JournalJournal of Hydro-environment Research
Early online date24 Apr 2019
Publication statusPublished - 1 May 2019


  • AMSR2
  • Soil moisture
  • Remote sensing
  • Cumulative distribution matching

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