An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea
In this study, a new assessment of thin cloud detection with the application of bidirectional reflectance distribution function (BRDF) model‐based background surface reflectance was undertaken by interpreting surface spectra characterized using the Geostationary Ocean Color Imager (GOCI) over a land...
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| Veröffentlicht in: | Journal of geophysical research. Atmospheres 2017-08, Vol.122 (15), p.8153-8172 |
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| container_title | Journal of geophysical research. Atmospheres |
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| creator | Kim, Hye‐Won Yeom, Jong‐Min Shin, Daegeun Choi, Sungwon Han, Kyung‐Soo Roujean, Jean‐Louis |
| description | In this study, a new assessment of thin cloud detection with the application of bidirectional reflectance distribution function (BRDF) model‐based background surface reflectance was undertaken by interpreting surface spectra characterized using the Geostationary Ocean Color Imager (GOCI) over a land surface area. Unlike cloud detection over the ocean, the detection of cloud over land surfaces is difficult due to the complicated surface scattering characteristics, which vary among land surface types. Furthermore, in the case of thin clouds, in which the surface and cloud radiation are mixed, it is difficult to detect the clouds in both land and atmospheric fields. Therefore, to interpret background surface reflectance, especially underneath cloud, the semiempirical BRDF model was used to simulate surface reflectance by reflecting solar angle‐dependent geostationary sensor geometry. For quantitative validation, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data were used to make a comparison with the proposed cloud masking result. As a result, the new cloud masking scheme resulted in a high probability of detection (POD = 0.82) compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) (POD = 0.808) for all cloud cases. In particular, the agreement between the CALIPSO cloud product and new GOCI cloud mask was over 94% when detecting thin cloud (e.g., altostratus and cirrus) from January 2014 to June 2015. This result is relatively high in comparison with the result from the MODIS Collection 6 cloud mask product (MYD35).
Key Points
Thin cloud detection algorithm over land area was suggested with geostationary ocean color satellite imagery
Background reflectance of the surface underneath the cloud is simulated with bidirectional reflectance distribution function model
Agreement between CALIPSO and new cloud detection algorithm was over 94% when detecting thin cloud (altostratus and cirrus) during study period |
| doi_str_mv | 10.1002/2017JD026707 |
| format | Article |
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Key Points
Thin cloud detection algorithm over land area was suggested with geostationary ocean color satellite imagery
Background reflectance of the surface underneath the cloud is simulated with bidirectional reflectance distribution function model
Agreement between CALIPSO and new cloud detection algorithm was over 94% when detecting thin cloud (altostratus and cirrus) during study period</description><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1002/2017JD026707</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Altostratus clouds ; Bidirectional reflectance ; BRDF model ; CALIPSO ; CALIPSO (Pathfinder satellite) ; Case studies ; Cirrus clouds ; Cloud detection ; Clouds ; Color imagery ; Computer simulation ; Continental interfaces, environment ; Data processing ; Detection ; Distribution ; Geophysics ; GOCI ; Ice ; Image processing ; Imagery ; Imaging techniques ; Lidar ; Masking ; Mathematical models ; Meteorological satellites ; MODIS ; Ocean color ; Ocean colour ; Oceans ; Probability theory ; Radiation ; Reflectance ; Satellite imagery ; Satellite observation ; Satellites ; Scattering ; Sciences of the Universe ; Spaceborne remote sensing ; Spectra ; thin cloud</subject><ispartof>Journal of geophysical research. Atmospheres, 2017-08, Vol.122 (15), p.8153-8172</ispartof><rights>2017. The Authors.</rights><rights>2017. American Geophysical Union. All Rights Reserved.</rights><rights>Attribution - NonCommercial - ShareAlike</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3795-7182b1fd41a8649e80da3bf62a226ee58b0832737e8a536a160b162a1d96eae43</citedby><cites>FETCH-LOGICAL-c3795-7182b1fd41a8649e80da3bf62a226ee58b0832737e8a536a160b162a1d96eae43</cites><orcidid>0000-0003-2321-731X ; 0000-0001-5898-1137 ; 0000-0003-3469-6563 ; 0000-0002-5031-0256 ; 0000-0003-2340-8394</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2017JD026707$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2017JD026707$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,1411,1427,27901,27902,45550,45551,46384,46808</link.rule.ids><backlink>$$Uhttps://hal.science/hal-02354407$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Hye‐Won</creatorcontrib><creatorcontrib>Yeom, Jong‐Min</creatorcontrib><creatorcontrib>Shin, Daegeun</creatorcontrib><creatorcontrib>Choi, Sungwon</creatorcontrib><creatorcontrib>Han, Kyung‐Soo</creatorcontrib><creatorcontrib>Roujean, Jean‐Louis</creatorcontrib><title>An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea</title><title>Journal of geophysical research. Atmospheres</title><description>In this study, a new assessment of thin cloud detection with the application of bidirectional reflectance distribution function (BRDF) model‐based background surface reflectance was undertaken by interpreting surface spectra characterized using the Geostationary Ocean Color Imager (GOCI) over a land surface area. Unlike cloud detection over the ocean, the detection of cloud over land surfaces is difficult due to the complicated surface scattering characteristics, which vary among land surface types. Furthermore, in the case of thin clouds, in which the surface and cloud radiation are mixed, it is difficult to detect the clouds in both land and atmospheric fields. Therefore, to interpret background surface reflectance, especially underneath cloud, the semiempirical BRDF model was used to simulate surface reflectance by reflecting solar angle‐dependent geostationary sensor geometry. For quantitative validation, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data were used to make a comparison with the proposed cloud masking result. As a result, the new cloud masking scheme resulted in a high probability of detection (POD = 0.82) compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) (POD = 0.808) for all cloud cases. In particular, the agreement between the CALIPSO cloud product and new GOCI cloud mask was over 94% when detecting thin cloud (e.g., altostratus and cirrus) from January 2014 to June 2015. This result is relatively high in comparison with the result from the MODIS Collection 6 cloud mask product (MYD35).
Key Points
Thin cloud detection algorithm over land area was suggested with geostationary ocean color satellite imagery
Background reflectance of the surface underneath the cloud is simulated with bidirectional reflectance distribution function model
Agreement between CALIPSO and new cloud detection algorithm was over 94% when detecting thin cloud (altostratus and cirrus) during study period</description><subject>Algorithms</subject><subject>Altostratus clouds</subject><subject>Bidirectional reflectance</subject><subject>BRDF model</subject><subject>CALIPSO</subject><subject>CALIPSO (Pathfinder satellite)</subject><subject>Case studies</subject><subject>Cirrus clouds</subject><subject>Cloud detection</subject><subject>Clouds</subject><subject>Color imagery</subject><subject>Computer simulation</subject><subject>Continental interfaces, environment</subject><subject>Data processing</subject><subject>Detection</subject><subject>Distribution</subject><subject>Geophysics</subject><subject>GOCI</subject><subject>Ice</subject><subject>Image processing</subject><subject>Imagery</subject><subject>Imaging techniques</subject><subject>Lidar</subject><subject>Masking</subject><subject>Mathematical models</subject><subject>Meteorological satellites</subject><subject>MODIS</subject><subject>Ocean color</subject><subject>Ocean colour</subject><subject>Oceans</subject><subject>Probability theory</subject><subject>Radiation</subject><subject>Reflectance</subject><subject>Satellite imagery</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Scattering</subject><subject>Sciences of the Universe</subject><subject>Spaceborne remote sensing</subject><subject>Spectra</subject><subject>thin cloud</subject><issn>2169-897X</issn><issn>2169-8996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp9kcluE0EQhkcIJKIkNx6gJC5EwqGXWblZDjgOliyxSNxGNdM1dofxtNMLaG48As_HkSehnUFRTvSlu6u-_69SVZK84OySMybeCMaLmysm8oIVT5ITwfNqVlZV_vThXXx9npw7d8viKZlMs_Qk-T0fAJ0j5_Y0eDAd-J0eoO1NUKDIU-u1GaAZAQ-HftTDFhqttJ3i2IOlro8fHFoCpZ23ugn3ki4Mk3ZvFPV_fv5q0JGCBttvW2vCoMAF22GUPbYI7lhiScZ5vK9gR9i0hAMsTG8srPa4JQuvlpvF6uItzKGNruB8UCN0Mf_JBL-DD8YSniXPOuwdnf-7T5Mv7999XlzP1pvlajFfz1pZVNms4KVoeKdSjmWeVlQyhbLpcoFC5ERZ2bBSikIWVGImc-Q5a3jMclXlhJTK0-Ri8t1hXx-s3seea4O6vp6v62OMCZmlKSu-88i-nNiDNXeBnK9vTbBxjq7mleSxH8GOjq8nqrXGuTieB1vO6uOy68fLjric8B-6p_G_bH2z_HiVySrL5F_a-67n</recordid><startdate>20170816</startdate><enddate>20170816</enddate><creator>Kim, Hye‐Won</creator><creator>Yeom, Jong‐Min</creator><creator>Shin, Daegeun</creator><creator>Choi, Sungwon</creator><creator>Han, Kyung‐Soo</creator><creator>Roujean, Jean‐Louis</creator><general>Blackwell Publishing Ltd</general><general>American Geophysical Union</general><scope>24P</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-2321-731X</orcidid><orcidid>https://orcid.org/0000-0001-5898-1137</orcidid><orcidid>https://orcid.org/0000-0003-3469-6563</orcidid><orcidid>https://orcid.org/0000-0002-5031-0256</orcidid><orcidid>https://orcid.org/0000-0003-2340-8394</orcidid></search><sort><creationdate>20170816</creationdate><title>An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea</title><author>Kim, Hye‐Won ; Yeom, Jong‐Min ; Shin, Daegeun ; Choi, Sungwon ; Han, Kyung‐Soo ; Roujean, Jean‐Louis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3795-7182b1fd41a8649e80da3bf62a226ee58b0832737e8a536a160b162a1d96eae43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Altostratus clouds</topic><topic>Bidirectional reflectance</topic><topic>BRDF model</topic><topic>CALIPSO</topic><topic>CALIPSO (Pathfinder satellite)</topic><topic>Case studies</topic><topic>Cirrus clouds</topic><topic>Cloud detection</topic><topic>Clouds</topic><topic>Color imagery</topic><topic>Computer simulation</topic><topic>Continental interfaces, environment</topic><topic>Data processing</topic><topic>Detection</topic><topic>Distribution</topic><topic>Geophysics</topic><topic>GOCI</topic><topic>Ice</topic><topic>Image processing</topic><topic>Imagery</topic><topic>Imaging techniques</topic><topic>Lidar</topic><topic>Masking</topic><topic>Mathematical models</topic><topic>Meteorological satellites</topic><topic>MODIS</topic><topic>Ocean color</topic><topic>Ocean colour</topic><topic>Oceans</topic><topic>Probability theory</topic><topic>Radiation</topic><topic>Reflectance</topic><topic>Satellite imagery</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Scattering</topic><topic>Sciences of the Universe</topic><topic>Spaceborne remote sensing</topic><topic>Spectra</topic><topic>thin cloud</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Hye‐Won</creatorcontrib><creatorcontrib>Yeom, Jong‐Min</creatorcontrib><creatorcontrib>Shin, Daegeun</creatorcontrib><creatorcontrib>Choi, Sungwon</creatorcontrib><creatorcontrib>Han, Kyung‐Soo</creatorcontrib><creatorcontrib>Roujean, Jean‐Louis</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Journal of geophysical research. Atmospheres</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Hye‐Won</au><au>Yeom, Jong‐Min</au><au>Shin, Daegeun</au><au>Choi, Sungwon</au><au>Han, Kyung‐Soo</au><au>Roujean, Jean‐Louis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea</atitle><jtitle>Journal of geophysical research. Atmospheres</jtitle><date>2017-08-16</date><risdate>2017</risdate><volume>122</volume><issue>15</issue><spage>8153</spage><epage>8172</epage><pages>8153-8172</pages><issn>2169-897X</issn><eissn>2169-8996</eissn><abstract>In this study, a new assessment of thin cloud detection with the application of bidirectional reflectance distribution function (BRDF) model‐based background surface reflectance was undertaken by interpreting surface spectra characterized using the Geostationary Ocean Color Imager (GOCI) over a land surface area. Unlike cloud detection over the ocean, the detection of cloud over land surfaces is difficult due to the complicated surface scattering characteristics, which vary among land surface types. Furthermore, in the case of thin clouds, in which the surface and cloud radiation are mixed, it is difficult to detect the clouds in both land and atmospheric fields. Therefore, to interpret background surface reflectance, especially underneath cloud, the semiempirical BRDF model was used to simulate surface reflectance by reflecting solar angle‐dependent geostationary sensor geometry. For quantitative validation, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data were used to make a comparison with the proposed cloud masking result. As a result, the new cloud masking scheme resulted in a high probability of detection (POD = 0.82) compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) (POD = 0.808) for all cloud cases. In particular, the agreement between the CALIPSO cloud product and new GOCI cloud mask was over 94% when detecting thin cloud (e.g., altostratus and cirrus) from January 2014 to June 2015. This result is relatively high in comparison with the result from the MODIS Collection 6 cloud mask product (MYD35).
Key Points
Thin cloud detection algorithm over land area was suggested with geostationary ocean color satellite imagery
Background reflectance of the surface underneath the cloud is simulated with bidirectional reflectance distribution function model
Agreement between CALIPSO and new cloud detection algorithm was over 94% when detecting thin cloud (altostratus and cirrus) during study period</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2017JD026707</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0003-2321-731X</orcidid><orcidid>https://orcid.org/0000-0001-5898-1137</orcidid><orcidid>https://orcid.org/0000-0003-3469-6563</orcidid><orcidid>https://orcid.org/0000-0002-5031-0256</orcidid><orcidid>https://orcid.org/0000-0003-2340-8394</orcidid><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 2169-897X |
| ispartof | Journal of geophysical research. Atmospheres, 2017-08, Vol.122 (15), p.8153-8172 |
| issn | 2169-897X 2169-8996 |
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| source | Wiley Free Content; Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection |
| subjects | Algorithms Altostratus clouds Bidirectional reflectance BRDF model CALIPSO CALIPSO (Pathfinder satellite) Case studies Cirrus clouds Cloud detection Clouds Color imagery Computer simulation Continental interfaces, environment Data processing Detection Distribution Geophysics GOCI Ice Image processing Imagery Imaging techniques Lidar Masking Mathematical models Meteorological satellites MODIS Ocean color Ocean colour Oceans Probability theory Radiation Reflectance Satellite imagery Satellite observation Satellites Scattering Sciences of the Universe Spaceborne remote sensing Spectra thin cloud |
| title | An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea |
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