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Remote Sens. 2017, 9(8), 870; doi:10.3390/rs9080870

A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

1
Department of Civil, Environmental and Plant Engneering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
2
Hydrometeorological Cooperation Center, Korea Water Resources Corporation, 11 Gyoyookwon-ro, Gwacheon-si, Gyeonggi-do 13841, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: George P. Petropoulos, Simonetta Paloscia, Prashant K. Srivastava, Guangsheng Zhou and Prasad S. Thenkabail
Received: 25 July 2017 / Revised: 11 August 2017 / Accepted: 19 August 2017 / Published: 22 August 2017
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
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Abstract

This study attempts to estimate spatial soil moisture in South Korea (99,000 km2) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R2) of the original LST and observed LST was 0.71, and the R2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R2 values were between 0.28 and 0.67. The reason for R2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R2 and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the variability of soil moisture. View Full-Text
Keywords: spatial soil moisture; MODIS LST; MODIS NDVI; conditional merging; multiple linear regression model spatial soil moisture; MODIS LST; MODIS NDVI; conditional merging; multiple linear regression model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jung, C.; Lee, Y.; Cho, Y.; Kim, S. A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging. Remote Sens. 2017, 9, 870.

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