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Open AccessArticle

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
Climate Research Department, Asia-Pacific Economic Cooperation (APEC) Climate Center, Busan 48058, Korea
National Meteorological Satellite Center, Korea Meteorological Administration, Jincheon 27803, Korea
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Alexander Löw and Jian Peng
Water 2017, 9(5), 332;
Received: 15 March 2017 / Revised: 21 April 2017 / Accepted: 3 May 2017 / Published: 7 May 2017
(This article belongs to the Special Issue Remote Sensing of Soil Moisture)
Soil moisture is a key part of Earth’s climate systems, including agricultural and hydrological cycles. Soil moisture data from satellite and numerical models is typically provided at a global scale with coarse spatial resolution, which is not enough for local and regional applications. In this study, a soil moisture downscaling model was developed using satellite-derived variables targeting Global Land Data Assimilation System (GLDAS) soil moisture as a reference dataset in East Asia based on the optimization of a modified regression tree. A total of six variables, Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced SCATterometer (ASCAT) soil moisture products, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and MODerate resolution Imaging Spectroradiometer (MODIS) products, including Land Surface Temperature, Normalized Difference Vegetation Index, and land cover, were used as input variables. The optimization was conducted through a pruning approach for operational use, and finally 59 rules were extracted based on root mean square errors (RMSEs) and correlation coefficients (r). The developed downscaling model showed a good modeling performance (r = 0.79, RMSE = 0.056 m3·m−3, and slope = 0.74). The 1 km downscaled soil moisture showed similar time series patterns with both GLDAS and ground soil moisture and good correlation with ground soil moisture (average r = 0.47, average RMSD = 0.038 m3·m−3) at 14 ground stations. The spatial distribution of 1 km downscaled soil moisture reflected seasonal and regional characteristics well, although the model did not result in good performance over a few areas such as Southern China due to very high cloud cover rates. The results of this study are expected to be helpful in operational use to monitor soil moisture throughout East Asia since the downscaling model produces daily high resolution (1 km) real time soil moisture with a low computational demand. This study yielded a promising result to operationally produce daily high resolution soil moisture data from multiple satellite sources, although there are yet several limitations. In future research, more variables including Global Precipitation Measurement (GPM) precipitation, Soil Moisture Active Passive (SMAP) soil moisture, and other vegetation indices will be integrated to improve the performance of the proposed soil moisture downscaling model. View Full-Text
Keywords: soil moisture; Cubist; downscaling; GLDAS; ASCAT; AMSR2 soil moisture; Cubist; downscaling; GLDAS; ASCAT; AMSR2
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MDPI and ACS Style

Park, S.; Park, S.; Im, J.; Rhee, J.; Shin, J.; Park, J.D. Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees. Water 2017, 9, 332.

AMA Style

Park S, Park S, Im J, Rhee J, Shin J, Park JD. Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees. Water. 2017; 9(5):332.

Chicago/Turabian Style

Park, Seonyoung; Park, Sumin; Im, Jungho; Rhee, Jinyoung; Shin, Jinho; Park, Jun D. 2017. "Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees" Water 9, no. 5: 332.

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