The COMS (Communication, Ocean and Meteorological Satellite) has been used in numerical weather prediction and meteorological monitoring over East Asia and Oceania since it has been launched in 2010. For more active utilization in climate research, the COMS level 3 products should be available in appropriate spatial and temporal resolutions. We compared different methods to generate monthly sea surface temperature (SST) products from the COMS time-series data. We employed three techniques for aggregating the time series, which are arithmetic mean, timeslot average, and moving average, and also used mean ensemble of them. Each level 3 dataset around South Korea was compared with monthly SST product from the Moderate Resolution Imaging Spectroradiometer (MODIS) of Aqua satellite during April 2011-March 2014. The timeslot average showed better root mean squared difference (RMSD) during the initial operational period of the COMS, when the retrieved values could be somewhat unstable. Daytime aggregations were derived more accurately by using the arithmetic mean or moving average, and the accuracy of nighttime aggregation was improved by the mean ensemble. Also, the timeslot average presented reasonable results particularly for coastlines where the standard deviation and missing value ratio were greater than normal. Because an optimal aggregation technique was variable depending on spatial and temporal conditions, we should be careful in selecting appropriate method for generation of the COMS level 3 products according to research objectives.

This paper described the estimation of corn and soybeans yields of four states in the US Midwest using time-series satellite imagery and climate dataset between 2001 and 2012. We first constructed a database for (1) satellite imagery acquired from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) including NDVI (Normalized Di°erence Vegetation Index), EVI (Enhanced Vegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), and GPP (Gross Primary Productivity), (2) climate dataset created by PRISM (Parameter-Elevation Regressions on Independent Slopes Model) such as precipitation and mean temperature, and (3) US yield statistics of corn and soybeans. ˜en we built OLS (Ordinary Least Squares) regression models for corn and soybeans yields between 2001 and 2010 after correlation analyses and multicollinearity tests. These regression models were used in estimating the yields of 2011 and 2012. Comparisons with the US yield statistics showed the RMSEs (Root Mean Squared Errors) of 0.892 ton/ha and 1.095 ton/ha for corn yields in 2011 and 2012 respectively, and those of 0.320 ton/ha and 0.391 ton/ha for soybeans yields. ˜is result can be thought of as a good agreement with the in-situ statistics because the RMSEs were approximately 10% of the usual yields: 9 ton/ha for corn and 3 ton/ha for soybeans. Our approach presented a possibility for extending to more advanced statistical modeling of crop yields using satellite imagery and climate dataset.

In this study, we produced satellite-based drought indices such as NDVI(normalized difference vegetation index), NDWI(normalized difference water index), and NDDI(normalized difference drought index) and extracted time-series principal components from the drought indices using EOF(empirical orthogonal function) method to examine their relationships with climatic variables. We found that the first principal components of the drought indices for Sonid Right Banner, Inner Mongolia were dominant in cold seasons and were closely related to low temperature, little rainfall, and high surface albedo. The satellite-based drought indices and their EOF analyses can be utilized for the studies of cold-season drought and warm-season drought as well.

Wildfires are recently increasing in frequencies and intensities worldwide. Hence, reliable and continuous monitoring of sudden occurrences of wildfire is demanded, and geostationary meteorological satellites are an alternative to detection of wildfire in large areas. We currently have two geostationary meteorological satellites for the Korean Peninsula: the Korean COMS(Communication, Ocean and Meteorological Satellite) and the Japanese MTSAT(Multifunctional Transport Satellite). However, neither of them provides satellite products for wildfire detection although the MODIS(Moderate Resolution Imaging Spectroradiometer) on polar-orbiting satellites has been operated for wildfire detection for a decade. In this study, we applied the MODIS algorithm for wildfire detection to the COMS and the MTSAT in order to evaluate the detection performances for South Korea. Both satellites were successful in detection of big fires, but the COMS was better in detecting small fires because of its higher saturation temperature of 350 K approx. at 4-μm band. The comparison results will be informative for an emergency plan of COMS and for the preparation of next-generation geostationary meteorological satellite.