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http://dx.doi.org/10.7780/kjrs.2020.36.4.8

Ensemble Downscaling of Soil Moisture Data Using BMA and ATPRK  

Youn, Youjeong (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Kim, Kwangjin (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Chung, Chu-Yong (Innovative Meteorological Research Department, National Institute of Meteorological Sciences)
Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
Lee, Yangwon (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.4, 2020 , pp. 587-607 More about this Journal
Abstract
Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.
Keywords
Ensemble; Downscaling; Soil moisture; Accuracy; Resolution;
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Times Cited By KSCI : 5  (Citation Analysis)
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