气候变化领域集成服务门户(CCPortal): Thin cloud detection over land using background surface reflectance based on the BRDF model applied to Geostationary Ocean Color Imager (GOCI) satellite data sets

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DOI	10.1016/j.rse.2019.111610
	Thin cloud detection over land using background surface reflectance based on the BRDF model applied to Geostationary Ocean Color Imager (GOCI) satellite data sets
	Yeom J.-M.; Roujean J.-L.; Han K.-S.; Lee K.-S.; Kim H.-W.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	Geostationary Ocean Color Imager (GOCI) sensor onboard the COMS (Communication, Ocean and Meteorological Satellite) launched in 2010 was primarily designed to provide high-frequency observations in and around the Korean Peninsula to ensure the thorough monitoring of ocean properties. Owing to its pixel resolution of 500 m and large set of spectral solar channels, GOCI can also be considered for applications related to the characterization of vegetation and the retrieval of aerosol properties over land. However, to apply it for the full characterization of land, it is mandatory to properly remove clouds from the images. Such a procedure has limitations when there is a lack of thermal bands, as is the case with GOCI. However, GOCI data are impacted by shadows and radiation scattering effects during the daily course of the sun. Although this yields strong directional effects, the bidirectional reflectance distribution function (BRDF) can be determined to a high level of accuracy. This information is used as a reference to detect clouds over land because surface BRDF varies slowly with time compared to that of clouds. The proposed algorithm relies on knowledge of the BRDF field derived from the application of a semi-empirical model that simulates the minimum difference between top and bottom of atmosphere reflectance values as the baseline of clear atmosphere. This step also serves to estimate background surface reflectance underneath clouds. Accuracy assessment of the new GOCI cloud mask product is appraised through a comparison with high-resolution vertical profiles of lidar data from the polar orbiting Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The results for the Probability Of Detection (POD) of all cloud types was found to be 0.831 for GOCI; this is comparable to that of MODIS (0.772). For the case of only thin cirrus, GOCI POD value was assessed to be 0.849, similar to that of MODIS, underlining the improved efficiency of determining thin cloud pixels. © 2019 The Authors
语种	英语
scopus关键词	Aerosols; Distribution functions; Oceanography; Optical radar; Orbits; Pixels; Radiation effects; Radiometers; Reflection; Weather satellites; Bidirectional reflectance distribution functions; Cloud mask products; Cloud-aerosol lidar and infrared pathfinder satellite observations; Directional effects; Probability of detection; Radiation scattering; Satellite data sets; Semi-empirical modeling; Geostationary satellites; aerosol; algorithm; bidirectional reflectance; CALIPSO; cloud cover; GOCI; lidar; MODIS; pixel; satellite data; surface reflectance; Korea
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179469
作者单位	Korea Aerospace Research Institute, Daejeon, South Korea; CESBIO, Toulouse, France; Department of Spatial Information Engineering, Pukyong National University, Busan, South Korea
推荐引用方式 GB/T 7714	Yeom J.-M.,Roujean J.-L.,Han K.-S.,et al. Thin cloud detection over land using background surface reflectance based on the BRDF model applied to Geostationary Ocean Color Imager (GOCI) satellite data sets[J],2020,239.
APA	Yeom J.-M.,Roujean J.-L.,Han K.-S.,Lee K.-S.,&Kim H.-W..(2020).Thin cloud detection over land using background surface reflectance based on the BRDF model applied to Geostationary Ocean Color Imager (GOCI) satellite data sets.Remote Sensing of Environment,239.
MLA	Yeom J.-M.,et al."Thin cloud detection over land using background surface reflectance based on the BRDF model applied to Geostationary Ocean Color Imager (GOCI) satellite data sets".Remote Sensing of Environment 239(2020).