A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea

Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This stu...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Yun-Jeong Choi, Hyun-Ju Ban, Hee-Jeong Han, Sungwook Hong
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:https://doi.org/10.3390/rs14030793
id ftmdpi:oai:mdpi.com:/2072-4292/14/3/793/
record_format openpolar
spelling ftmdpi:oai:mdpi.com:/2072-4292/14/3/793/ 2023-08-20T04:09:46+02:00 A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea Yun-Jeong Choi Hyun-Ju Ban Hee-Jeong Han Sungwook Hong agris 2022-02-08 application/pdf https://doi.org/10.3390/rs14030793 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs14030793 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 3; Pages: 793 cloud detection cloud mask NDWI visible near-infrared ocean color MODIS Text 2022 ftmdpi https://doi.org/10.3390/rs14030793 2023-08-01T04:05:51Z Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This study proposes a cloud-detection method using reflectance in four bands: 0.56 μm, 0.86 μm, 1.38 μm, and 1.61 μm. Methodologically, we present a conversion relationship between the normalized difference water index (NDWI) and the green band in the visible spectrum for thick cloud detection using moderate-resolution imaging spectroradiometer (MODIS) observations. NDWI consists of reflectance at the 0.56 and 0.86 μm bands. For thin cloud detection, the 1.38 and 1.61 μm bands were applied with empirically determined threshold values. Case study analyses for the four seasons from 2000 to 2019 were performed for the sea surface area of the Yellow Sea and Bohai Sea. In the case studies, the comparison of the proposed cloud-detection method with the MODIS cloud mask (CM) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data indicated a probability of detection of 0.933, a false-alarm ratio of 0.086, and a Heidke Skill Score of 0.753. Our method demonstrated an additional important benefit in distinguishing clouds from sea ice or yellow dust, compared to the MODIS CM products, which usually misidentify the latter as clouds. Consequently, our cloud-detection method could be applied to a variety of low-orbit and geostationary satellites with 0.56, 0.86, 1.38, and 1.61 μm bands. Text Sea ice MDPI Open Access Publishing Remote Sensing 14 3 793
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic cloud detection
cloud mask
NDWI
visible
near-infrared
ocean color
MODIS
spellingShingle cloud detection
cloud mask
NDWI
visible
near-infrared
ocean color
MODIS
Yun-Jeong Choi
Hyun-Ju Ban
Hee-Jeong Han
Sungwook Hong
A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea
topic_facet cloud detection
cloud mask
NDWI
visible
near-infrared
ocean color
MODIS
description Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This study proposes a cloud-detection method using reflectance in four bands: 0.56 μm, 0.86 μm, 1.38 μm, and 1.61 μm. Methodologically, we present a conversion relationship between the normalized difference water index (NDWI) and the green band in the visible spectrum for thick cloud detection using moderate-resolution imaging spectroradiometer (MODIS) observations. NDWI consists of reflectance at the 0.56 and 0.86 μm bands. For thin cloud detection, the 1.38 and 1.61 μm bands were applied with empirically determined threshold values. Case study analyses for the four seasons from 2000 to 2019 were performed for the sea surface area of the Yellow Sea and Bohai Sea. In the case studies, the comparison of the proposed cloud-detection method with the MODIS cloud mask (CM) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data indicated a probability of detection of 0.933, a false-alarm ratio of 0.086, and a Heidke Skill Score of 0.753. Our method demonstrated an additional important benefit in distinguishing clouds from sea ice or yellow dust, compared to the MODIS CM products, which usually misidentify the latter as clouds. Consequently, our cloud-detection method could be applied to a variety of low-orbit and geostationary satellites with 0.56, 0.86, 1.38, and 1.61 μm bands.
format Text
author Yun-Jeong Choi
Hyun-Ju Ban
Hee-Jeong Han
Sungwook Hong
author_facet Yun-Jeong Choi
Hyun-Ju Ban
Hee-Jeong Han
Sungwook Hong
author_sort Yun-Jeong Choi
title A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea
title_short A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea
title_full A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea
title_fullStr A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea
title_full_unstemmed A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea
title_sort maritime cloud-detection method using visible and near-infrared bands over the yellow sea and bohai sea
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://doi.org/10.3390/rs14030793
op_coverage agris
genre Sea ice
genre_facet Sea ice
op_source Remote Sensing; Volume 14; Issue 3; Pages: 793
op_relation Ocean Remote Sensing
https://dx.doi.org/10.3390/rs14030793
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs14030793
container_title Remote Sensing
container_volume 14
container_issue 3
container_start_page 793
_version_ 1774723429842288640