LAUSR

Soil moisture (SM) is a critical parameter for interpreting the status of land surfaces and vegetation because it has an important role in the exchange of water and heat energy between the land surface and atmosphere. SM can be retrieved with satellite microwave sensors, which offer several advantages in that they are cost-effective, give rapid results, and provide data for inaccessible or isolated areas. In addition, these sensors cover large areas and perform periodic multi-channel observations. The advanced microwave scanning radiometer-2 (AMSR2), a successor of Advanced Microwave Scanning Radiometer for the Earth Observing System, provides regular global SM data products, which the Korea Meteorological Administration uses to calculate daily mean and seven-day moving window SM do not agree well with ground measurements in South Korea, and must be corrected for reliable drought monitoring. In this study, we used Global Land Data Assimilation System SM data to calibrate AMSR2 SM. In addition, we applied and combined two methods to correct AMSR2 SM: 1) linear regression with input variables, including insolation, daily precipitation, and the normalized difference vegetation index and 2) the cumulative distribution function (CDF) matching method. The results of the combined linear regression and CDF matching method were in better agreement with the in situ data than those of either method alone. Correcting AMSR2 SM by using the combined method improved the correlation coefficient (R) from 0.19 to 0.59 and the root-mean-square error from 0.23 to 0.09.