Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth’s energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 ~ 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer’s Accuracy), UA (User’s Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.
Water vapor is main absorption factor of outgoing longwave radiation. Because increase of water vapor accelerate to become high land surface temperature, it is essential to monitoring the changes in the amount of water vapor and to investigating the causes of such changes. This paper, we monitor variability pattern of Total Precipitable Water (TPW) which observed by satellite. But long-term investigation of climate over Korea peninsula is very difficult due to climatic characteristic in middle latitude of instable atmospheric. El Nino that is one of climate variables appears regularly when compared to the others. Also, precipitation of all climate variables play an important part to analyze variability pattern of water vapor because it is produced by water vapor. Therefore, if we know climatic variability by them, correlation analysis between TPW and climate variables can be improved. In this study, we analyze long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) and precipitation change in middle area of Korea peninsula quantitatively and El Nino was compared to relation of TPW and precipitation. The aim of study is to investigate precipitation and El Nino has an impact on variability pattern of TPW. First, time series analysis is used to calculate TPW and precipitation quantitatively, and anomaly analysis is performed to analyze their correlation. From the results obtained, TPW and precipitation has correlation mostly but the part had inverse correlation was found. We compare it with El Nino of anomaly results. As a result, after El Nino occurred, TPW and precipitation had inverse correlation.
Sea ice is an important factor for understanding Antarctic climate change. Especially, annual change of sea ice shows different trend in Antarctica and Arctic. This different variation need to continuously observe the Polar Regions. Sea Ice Albedo (SIA) and Sea Ice Concentration (SIC) are an indicator of variation on sea ice. In addition, albedo is key parameter to understand the energy budget in Antarctica. This being so, it is important to analyze long-term variation of the two factors for observing of change of Antarctic environment. In this study, we analyzed long-term variability of SIC and SIA to understand the changes of sea ice over Antarctic and researched the relationship with two factors. We used the SIA data at The Satellite Application Facility on Climate Monitoring (CM SAF) and the SIC data provided by Ocean and Sea Ice Satellite Application Facility (OSI-SAF) from 1982 to 2009. The study period was selected to Antarctic summer season due to polar nights. We divided study periods into two terms, Nov-Dec(ND) and Jan-Feb(JF) in order to reflect the characteristics of sea ice area, which minimum extend occurred in September and maximum extend occurred in February. We analyzed the correlation between SIA and SIC. As a results, two variables have a strong positive correlation (each correlation coefficients are 0.91 in Nov-Dec and 0.90 in Jan-Feb). We performed time series analysis using linear regression to understand the spatial and temporal tendency of SIA and SIC. As a results, SIA and SIC have a same spatial trend such as Weddle sea and Ross sea sections show the positive trend and Bellingshausen Amundsen sea shows the negative trend of two factors. Moreover, annual SIA change rate is 0.26% ~ 0.04% yr-1 over section where represent positive trend during two study periods. And annual SIA change rate is - 0.14 ~ - 0.25 % yr-1 of in the other part where represent negative trend during two study periods.