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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). |
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