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Development of Cloud Detection Method with Geostationary Ocean Color Imagery for Land Applications
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  • Journal title : Korean Journal of Remote Sensing
  • Volume 31, Issue 5,  2015, pp.371-384
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2015.31.5.2
 Title & Authors
Development of Cloud Detection Method with Geostationary Ocean Color Imagery for Land Applications
Lee, Hwa-Seon; Lee, Kyu-Sung;
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 Abstract
Although GOCI has potential for land surface monitoring, there have been only a few cases for land applications. It might be due to the lack of reliable land products derived from GOCI data for end-users. To use for land applications, it is often essential to provide cloud-free composite over land surfaces. In this study, we proposed a cloud detection method that was very important to make cloud-free composite of GOCI reflectance and vegetation index. Since GOCI does not have SWIR and TIR spectral bands, which are very effective to separate clouds from other land cover types, we developed a multi-temporal approach to detect cloud. The proposed cloud detection method consists of three sequential steps of spectral tests. Firstly, band 1 reflectance threshold was applied to separate confident clear pixels. In second step, thick cloud was detected by the ratio (b1/b8) of band 1 and band 8 reflectance. In third step, average of b1/b8 ratio values during three consecutive days was used to detect thin cloud having mixed spectral characteristics of both cloud and land surfaces. The proposed method provides four classes of cloudiness (thick cloud, thin cloud, probably clear, confident clear). The cloud detection method was validated by the MODIS cloud mask products obtained during the same time as the GOCI data acquisition. The percentages of cloudy and cloud-free pixels between GOCI and MODIS are about the same with less than 10% RMSE. The spatial distributions of clouds detected from the GOCI images were also similar to the MODIS cloud mask products.
 Keywords
GOCI;cloud detection;multi-temporal method;land applications;MODIS cloud product;
 Language
Korean
 Cited by
1.
Improvement of Temporal Resolution for Land Surface Monitoring by the Geostationary Ocean Color Imager Data, Korean Journal of Remote Sensing, 2016, 32, 1, 25  crossref(new windwow)
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