The use of global synthetic data for pre-launch tuning of the VIIRS cloud mask algorithm

Hutchison, Keith D.; Iisager, Barbara D.; Hauss, B.

doi:10.1080/01431161.2011.571299

Cited by 24 publications

(18 citation statements)

References 7 publications

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“…These results are shown in Table . Performance results (i.e., PCT, false alarm, and leakage) are computed for the binary cloud mask, which is defined as pixels classified as confidently clear and cloudy pixels [ Hutchison et al ., ]. Only clouds with optical depths greater than or equal to 1.0 are considered.…”

Section: Resultsmentioning

confidence: 99%

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The VIIRS Cloud Mask: Progress in the first year of S‐NPP toward a common cloud detection scheme

et al. 2014

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The Visible Infrared Imager Radiometer Suite (VIIRS) Cloud Mask (VCM) determines, on a pixel-by-pixel basis, whether or not a given location contains cloud. The VCM serves as an intermediate product (IP) between the production of VIIRS sensor data records and 22 downstream Environmental Data Records that each depends upon the VCM output. As such, the validation of the VCM IP is critical to the success of the Suomi National Polar-orbiting Partnership (S-NPP) product suite. The methods used to validate the VCM and the current results are presented in this paper. Detailed analyses of golden granules along with tools providing deep insights into granule performance, and specific cloud detection tests reveal the details behind a given granule's performance. Matchup results with CALIPSO, in turn, indicate the large-scale performance of the VCM and whether or not it is meeting its specifications. Comparisons with other cloud masks indicate comparable performance for the determination of clear pixels. As of September 2013 the VCM is either meeting or within 2% of all of its documented requirements.

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Section: Resultsmentioning

confidence: 99%

“…The mathematical definitions of these performance metrics are shown in Hutchison et al . []. PCT quantifies the raw number/percentage of clear/cloudy pixels as correct.…”

Section: The Vcm Algorithmmentioning

confidence: 99%

The VIIRS Cloud Mask: Progress in the first year of S‐NPP toward a common cloud detection scheme

et al. 2014

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“…The 1.38 μm channel of SGLI should be very effective for detection of thin cirrus. However, as discussed by Ackerman et al [], Miller and Lee [], and Hutchison et al [], the 1.38 μm signal can be highly variable for the dry atmospheric conditions commonly encountered over Greenland and Antarctica and needs to be carefully tuned to minimize the out‐of‐band responses of SGLI's 1.38 μm channel. Since snow and clouds are basically indistinguishable in visible and near IR channels (as shown in Figure 5 of Imaoka et al []), we will focus on using the two SWIR channels of SGLI, SW3 centered at 1.63 μm and SW4 at 2.20 μm, for cloud detection.…”

Section: Methodsmentioning

confidence: 99%

Cloud mask over snow‐/ice‐covered areas for the GCOM‐C1/SGLI cryosphere mission: Validations over Greenland

Chen

Tanikawa

et al. 2014

JGR Atmospheres

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Cloud detection is a critically important first step required to derive many satellite data products. A novel cloud detection algorithm designed for the cryosphere mission of Global Climate Observation Mission First Climate satellite/Second Generation Global Imager (GCOM‐C1/SGLI) is presented. This reflectance‐based cloud detection scheme mainly utilizes only two short wavelength infrared channels with dynamic thresholds that depend on Sun‐satellite viewing geometry to perform accurate cloud detection over snow/ice surfaces in high latitude as well as high‐elevation regions. Profiles of atmospheric absorbing and scattering molecules as well as surface elevation are considered in the determination of the thresholds for the resulting snow/ice cloud mask (SCM) algorithm. Image‐based tests and statistical results have been used to validate the performance of the SCM over the Greenland plateau. Statistics using collocated Cloud‐Aerosol Lidar with Orthogonal Polarization and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua observations over Greenland in 2007 show that over snow/ice surfaces the performance of the SCM is generally better than that of the MODIS cloud mask.

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“…The design of the HCHM algorithm builds upon the work and valuable experience of MOD‐CLD‐MASK [ Ackerman et al , ], MOD‐CLD‐DT [ Levy et al , ], MOD‐CLD‐DB [ Hsu et al , ], VII‐CLD‐MASK [ Hutchison et al , ; Baker , ], CLD‐Claudia [ Ishida and Nakajima , ; Nakajima et al , ], and a regime‐dependent cloud mask algorithm [ Zou and Da , ]. Based on the available AHI band sets, the tests are divided into two groups to identify cloud and clear and haze pixels.…”

Section: Hchm Algorithmmentioning

confidence: 99%

Development of a daytime cloud and haze detection algorithm for Himawari‐8 satellite measurements over central and eastern China

et al. 2017

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Cloud detection by passive satellite sensors is very challenging in hazy weather over China because the reflective characteristics of haze and clouds are very similar. Consequently, hazy areas tend to be mistaken as cloudy or clear areas by current cloud mask algorithms. The Advanced Himawari Imager (AHI) aboard Himawari‐8 is a multispectral Earth observation sensor with high temporal and spatial resolutions. A cloud and haze detection algorithm for AHI measurements is urgently needed for monitoring atmospheric pollution and its transport over China. This study presents the new Himawari‐8 Cloud and Haze Mask (HCHM) algorithm that classifies image pixels from central and eastern China into one of three categories: clear, cloudy, or hazy. Based on the observations that haze occurs near the ground and accumulates in low‐elevation plains and basins while clouds form at high altitudes, the proposed HCHM algorithm incorporates altitude information to adjust the thresholds used in the selected threshold tests to separate haze and cloud pixels. We find that combining auxiliary digital elevation model data with traditional indicators, such as the R0.86/R0.64, R0.86/R1.6, and BT11‐BT3.9, improves the accuracy of cloud and haze discrimination. The HCHM algorithm is applied to Himawari‐8 observations from August 2015, November 2015, January 2016, and May 2016 and validated against the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation vertical feature mask results. The validation shows that the average leakage rate, false alarm rate, and haze missing rate of the HCHM algorithm are 3.95%, 5.88%, and 4.17%, respectively.

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The use of global synthetic data for pre-launch tuning of the VIIRS cloud mask algorithm

Cited by 24 publications

References 7 publications

The VIIRS Cloud Mask: Progress in the first year of S‐NPP toward a common cloud detection scheme

The VIIRS Cloud Mask: Progress in the first year of S‐NPP toward a common cloud detection scheme

Cloud mask over snow‐/ice‐covered areas for the GCOM‐C1/SGLI cryosphere mission: Validations over Greenland

Development of a daytime cloud and haze detection algorithm for Himawari‐8 satellite measurements over central and eastern China

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