Many countries try to launch satellite to observe the Earth surface. As important of surface remote sensing is increased, the reflectance of surface is a core parameter of the ground climate. But observing the reflectance of surface by satellite have weakness such as temporal resolution and being affected by view or solar angles. The bidirectional effects of the surface reflectance may make many noises to the time series. These noises can lead to make errors when determining surface reflectance. To correct bidirectional error of surface reflectance, using correction model for normalized the sensor data is necessary. A Bidirectional Reflectance Distribution Function (BRDF) is making accuracy higher method to correct scattering (Isotropic scattering, Geometric scattering, Volumetric scattering). To correct bidirectional error of surface reflectance, BRDF was used in this study. To correct bidirectional error of surface reflectance, we apply Bidirectional Reflectance Distribution Function (BRDF) to retrieve surface reflectance. And we apply 2 steps for retrieving Background Surface Reflectance (BSR). The first step is retrieving Bidirectional Reflectance Distribution (BRD) coefficients. Before retrieving BSR, we did pre-running BRDF to retrieve BRD coefficients to correct scatterings (Isotropic scattering, Geometric scattering, Volumetric scattering). In pre-running BRDF, we apply BRDF with observed surface reflectance of SPOT/VEGETATION (VGT-S1) and angular data to get BRD coefficients for calculating scattering. After that, we apply BRDF again in the opposite direction with BRD coefficients and angular data to retrieve BSR as a second step. As a result, BSR has very similar reflectance to one of VGT-S1. And reflectance in BSR is shown adequate. The highest reflectance of BSR is not over 0.4μm in blue channel, 0.45μm in red channel, 0.55μm in NIR channel. And for validation we compare reflectance of clear sky pixel from SPOT/VGT status map data. As a result of comparing BSR with VGT-S1, bias is from 0.0116 to 0.0158 and RMSE is from 0.0459 to 0.0545. They are very reasonable results, so we confirm that BSR is similar to VGT-S1. And weakness of this study is missing pixel in BSR which are observed less time to retrieve BRD components. If missing pixels are filled, BSR is better to retrieve surface products with more accuracy. And we think that after filling the missing pixel and being more accurate, it can be useful data to retrieve surface product which made by surface reflectance like cloud masking and retrieving aerosol.
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.
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