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

Choi, Yun-Jeong; Ban, Hyunju; Han, Hee-Jeong; Hong, Sungwook

doi:10.3390/rs14030793

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…The correct SCDA clear-sky detection percentage was 88.33%, lower than Himawari-8 clear-sky products (90.54%). The authors in [76] proposed a MODIS cloud detection over the Yellow Sea and Bohai Sea, based on a relationship between the Normalized Difference Water Index (NDWI) (estimated at 0.56 and 0.86 μm) and the reflectance at 0.56 μm as well as the radiance at 1.38 and 1.61 μm to identify thin clouds. The comparison with different products (MOD35/MYD35, Caliop and Infrared Pathfinder Satellite Observation) showed a detection probability of 0.933% and a false alarm of 0.086.…”

Section: Cloud Detection Based On Threshold Testsmentioning

confidence: 99%

The Evolution of Meteorological Satellite Cloud Detection Methodologies

Romano,

Cimini,

Di Paola

et al. 2024

Preprint

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The accurate detection of clouds is an important first-step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology have exploded, creating a new era in space exploration. Additionally, initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, more recently cloud screening in satellite acquired data is playing an increasingly critical role in the investigation of cloud free data for the retrieval of soil moisture, vegetation cover, ocean colour concentration and sea surface temperatures, as well as environmental monitoring of a host of products, e.g. atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to both accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this paper, the evolution of cloud detection technology is highlighted with advancement in sensor hardware technology and possible AI algorithmic advances. Additionally, a discussion is presented on methods to obtain the cloud truth data needed to determine the accuracy of these cloud detection approaches.

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Section: Cloud Detection Based On Threshold Testsmentioning

confidence: 99%

The Evolution of Meteorological Satellite Cloud Detection Methodologies

Romano,

Cimini,

Di Paola

et al. 2024

Preprint

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“…The Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observe the global distribution of clouds and aerosols in Earth's atmosphere. This study used the CALIPSO Level 2 lidar vertical feature mask (VFM) data products providing vertical and horizontal distribution information on cloud and aerosol layers [58]. The cloud types in the CALIPSO VFM data were classified into nine flags according to the International Satellite Cloud Climatology Project's standard definitions for meteorological cloud types [8].…”

Section: Calipso Dataset For Validationmentioning

confidence: 99%

Hypothetical Cirrus Band Generation for Advanced Himawari Imager Sensor Using Data-to-Data Translation With Advanced Meteorological Imager Observations

Park

Choi

Jeong

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Cirrus cloud contributes significantly to Earth's radiation budget and the greenhouse effect. The Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite lacks a 1.37 µm band, sensitive to monitoring cirrus clouds. This study proposed a conditional generative adversarial network-based data-to-data translation (D2D) model to generate a hypothetical AHI 1.37 µm band. For training and testing the D2D model, the Geo-Kompsat-2A Advanced Meteorological Imager (AMI) 1.37 µm bands and other highly correlated bands to cirrus from July 24, 2019, to July 31, 2020, were used. The D2D model exhibited a high level of agreement (mean of statistics: correlation coefficient (CC) = 0.9827, bias = 0.0004, and root mean square error (RMSE) = 0.0086 in albedo units) between the observed and D2D-generated AMI 1.37 µm bands from validation datasets. The application of the D2D model to the AHI sensor showed that the D2D-generated AHI 1.37 µm band was qualitatively analogous to the observed AMI 1.37 µm band (average of statistics: bias = 0.0026, RMSE = 0.0191 in albedo units, and CC = 0.9158) on the 1st, 15th, and 28th of each month of 2020 in the common observing regions between Korea and Japan. The validation results with the CALIPSO data also showed that the D2D-generated AHI 1.37 µm band performed similarly to the observed AMI 1.37 µm band. Consequently, this study can significantly contribute to cirrus detection and its application to climatology.

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“…The NDWI can be used to identify water and other objects and is commonly used to extract water body information. The calculation method is shown in Formula (1) [27,28]:…”

Section: Satellite Data Acquisition and Preprocessingmentioning

confidence: 99%

The Classification of Riparian Habitats and Assessment of Fish-Spawning Habitat Suitability: A Case Study of the Three Gorges Reservoir, China

Mao,

Ding,

Yuan

et al. 2023

Sustainability

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After the completion of the Three Gorges Reservoir (TGR), there was a significant and drastic transformation of the original river habitat. These changes led to the loss of the original fish habitat and the emergence of a new habitat. To effectively classify and assess fish-spawning habitats in the TGR, a novel coastal complexity index (CCI) was developed. The CCI was formulated utilizing satellite remote sensing data and considering the river coastal line and river centerline on the river-reach scale. By integrating the CCI with river morphology, five river habitats were identified: the backwater bay, point bar, straight river channel, convex-bank point bar, and concave-bank deep pool. In order to evaluate the suitability of these habitats for sticky-egg-spawning fish, a single-factor habitat suitability curve was constructed using three key habitat factors: the CCI, slope, and vegetation coverage. This process involved the employment of two distinct methods: the habitat utilization method and the habitat preference method. The former only considered the survey data of spawning grounds, while the latter integrated the overall distribution of habitats in the TGR. Subsequently, a habitat suitability index (HSI) was established to assess the overall suitability of the identified habitats for sticky-egg-spawning fish. The results demonstrated a high classification accuracy, with the backwater bay representing the most prevalent habitat type, accounting for 43.31% of the total habitat types. When considering slope and vegetation coverage, the optimal ranges obtained through the two habitat suitability analysis methods were similar. However, for the CCI, there were variations in the optimal ranges obtained using the two methods. The habitat utilization method indicated an optimal interval of 2–4, while the habitat preference method provided an optimal interval of 4–8. Nonetheless, the assessment results for the spawning habitats’ suitability using both methods yielded essentially identical outcomes. Specifically, the backwater bay, convex-bank point bar, and concave-bank deep pool habitats exhibited higher suitability for spawning than point bar and straight river channel habitats. Further analysis revealed that approximately 75% of the 230 identified backwater bays were categorized as high-quality or higher-quality spawning habitats. In the time since this research was conducted, its findings have served as a theoretical foundation for the protection of aquatic biological resources and habitats.

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

Cited by 6 publications

References 42 publications

The Evolution of Meteorological Satellite Cloud Detection Methodologies

The Evolution of Meteorological Satellite Cloud Detection Methodologies

Hypothetical Cirrus Band Generation for Advanced Himawari Imager Sensor Using Data-to-Data Translation With Advanced Meteorological Imager Observations

The Classification of Riparian Habitats and Assessment of Fish-Spawning Habitat Suitability: A Case Study of the Three Gorges Reservoir, China

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