A Fast Cloud Detection Algorithm Applicable to Monitoring and Nowcasting of Daytime Cloud Systems

Zhuge, Xiaoyong; Zou, Xiaolei; Wang, Yuan

doi:10.1109/tgrs.2017.2720664

Cited by 39 publications

(31 citation statements)

References 22 publications

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“…It is not necessary to use all the four channels for constructing CI predictors reflecting cloud optical thickness. With regard to the contrast between the surface and cloud top, channel 1 reflectance ( ρ 0.47 ) is the most significant among channels 1 -4 (Zhuge et al 2017). Therefore, a subset of 12 candidate interest fields are selected for describing the atmospheric states and cloud-top properties (Table Table 1 3).…”

Section: Interest Fields Selectionmentioning

confidence: 99%

“…The first step separates thick cloud pixels from clear sky and thin cirrus pixels. Since the clouds and the Earth surface have different reflectance spectra in the visible and near-infrared frequencies, a fast cloud detection method involving AHI 0.47-, 0.64-, and 0.86-μm channels (Zhuge et al 2017) is used to distinguish cloudy pixels from clear sky pixels. After that, thin cirrus pixels are discarded if the normalized reflectance of 0.47-μm visible channel is lower than 0.35.…”

Section: Cumulus Cloud Maskmentioning

confidence: 99%

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Summertime Convective Initiation Nowcasting over Southeastern China Based on Advanced Himawari Imager Observations

Zhuge

Zou

2018

Journal of the Meteorological Society of Japan

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Convective initiation (CI) nowcasting often has a low probability of detection (POD) and a high false-alarm ratio (FAR) at subtropical regions where the warm-rain processes often occur. Using the high spatial and temporal resolution and multispectral data from the Advanced Himawari Imager (AHI) on board Japanese new-generation geostationary satellite Himawari-8, a standalone CI nowcasting algorithm is developed in this study. The AHIbased CI algorithm utilizes the reflectance observations from channels 1 (0.47 μm) and 7 (3.9 μm); brightness temperature observations from infrared window channel 13 (10.4 μm), the dual-spectral differences between channels 10 (7.3 μm) and 13, 13, and 15 (12.4 μm), and a tri-spectral combination of channels 11, 15, and 13, as CI predictors without relying on any dynamic ancillary data (e.g., cloud type and atmospheric motion vector products). The proposed AHI-based algorithm is applied to CI cases over Fujian province in the Southeastern China. When validated by S-band radar observations, the CI algorithm produced a POD as high as 93.33 %, and an FAR as low as 33.33 % for a CI case day that occurred on August 1, 2015, over Northern Fujian. For over 216 CI events that occurred in a 3-month period from July to September 2015, the CI nowcasting lead time has a mean value of ~ 64 min, with a longest lead time over 120 min. It is suggested that false-alarm nowcasts that occur in the presence of capping inversion require further investigation and algorithm enhancements.

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Section: Interest Fields Selectionmentioning

confidence: 99%

Section: Cumulus Cloud Maskmentioning

confidence: 99%

Summertime Convective Initiation Nowcasting over Southeastern China Based on Advanced Himawari Imager Observations

Zhuge

Zou

2018

Journal of the Meteorological Society of Japan

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“…Specifically, Himawari-8/9 and GK-2A are appropriate for monitoring the sea fog in the Yellow Sea because they have widespread spatial coverage and a high temporal resolution. This study used Himawari-8 data equipped with an AHI sensor containing three VIS channels (0.47, 0.51, and 0.64 µm), three near-IR (NIR) channels (0.86, 1.61, and 2.26 µm), and 10 IR channels [8,25]. The spatial resolution at the nadir point is 0.5 km for the VIS channel at 0.64 µm, 1 km for the VIS channels at 0.47, 0.51, and 0.86 µm, and 2 km for the remaining NIR and all IR channels.…”

Section: Study Area and Datamentioning

confidence: 99%

“…In the IR band, generally, the difference of brightness temperature between shortwave infrared (SWIR) (3.9 µm) and longwave infrared (LWIR) (10.8 µm) [22], referred to as bispectral image processing (BIP) [23], has been used to distinguish fog from clouds [24]. For example, the negative or near zero values of brightness temperature differences (BTD) between 3.9 and 11 µm channels during the nighttime indicates low stratus/fog or vegetated/ocean surfaces, respectively [23,25]. However, the IR-based sea fog detection algorithm is limited under twilight conditions.…”

Section: Introductionmentioning

confidence: 99%

Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations

Ryu

Hong

2020

Remote Sensing

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Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.

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“…Most of current cloud detection methods extract the clouds from the imagery through ruled based classification which applies a set of thresholds (both static and dynamic) of reflectance and brightness temperature. [1][2][3] Most widespread threshold methods are ACCA (Automatic Cloud Cover Assessment) 4 and Fmask (Function of mask) 5,6 which was originally designed for Landsat imagery. A threshold based method is also used for the development of the Sentinel-2 cloud masks provided by the level 2A product.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery

Kristollari

Karathanassi

2020

Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020)

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Cloud contamination represents a large obstacle for mapping the earth's surface using remotely sensed data. Therefore, cloudy pixels should be identified and eliminated before any further data processing can be achieved. Although several threshold, multi-temporal and machine learning applications have been developed to tackle this issue, it still remains a challenge. The main challenges are imposed by the difficulty to detect thin clouds and to separate bright clouds from bright non-cloud objects. Convolutional neural networks (CNNs) have proven to be one of the most promising methods for image classification tasks and their use is rapidly increasing in remote sensing problems. CNNs present interesting properties for image processing since they directly exploit not only the spectral information but also the spatial covariance of the data. In this work, we study the applicability of CNNs in cloud detection of Sentinel-2 imagery, a complex remote sensing problem with crucial spatial context. A patch-to-pixel CNN architecture consisting of three convolutional layers and two fully connected layers is trained on a recently available manually created public dataset. The results were evaluated both qualitatively and quantitatively through comparison with ground truth cloud masks and state-of-the art pixel-based algorithms (Fmask, Sen2Cor). It was shown that the proposed architecture even though simpler than the deep learning architectures proposed by recent literature, performs very favorably, especially in the challenging cases. Besides the evaluation of the results, feature maps where observed as an initial effort to extract the weights of the useful kernels for cloud masking applications.

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A Fast Cloud Detection Algorithm Applicable to Monitoring and Nowcasting of Daytime Cloud Systems

Cited by 39 publications

References 22 publications

Summertime Convective Initiation Nowcasting over Southeastern China Based on Advanced Himawari Imager Observations

Summertime Convective Initiation Nowcasting over Southeastern China Based on Advanced Himawari Imager Observations

Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations

Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery

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