Estimation of Soil Moisture Using Multiple Linear Regression Model and COMS Land Surface Temperature Data
2017
Lee, Y.G., Konkuk university, Seoul, Republic of Korea | Jung, C.G., Konkuk university, Seoul, Republic of Korea | Cho, Y., K-water, Daejeon, Republic of Korea | Kim, S.J., Konkuk university, Seoul, Republic of Korea
This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.
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