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Minnis, Alastair

doi:10.9783/9780812205718

Cited by 17 publications

(4 citation statements)

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“…At other times of day, the solar-infrared infrared splitwindow technique (SIST) is used to determine each of the parameter values. The SIST, an improved version of the original 3-channel nighttime method 7 , only uses thermal and solar-infrared data. Thus, its retrievals are valid only for optically thin clouds.…”

Section: Methodsmentioning

confidence: 99%

“…Effective droplet radius r e or effective ice crystal diameter D e , optical depth τ, ice or liquid water path IWP/LWP, effective cloud temperature T e and height Z e , cloud thickness, and cloud phase are derived from these same radiances using one of three different techniques. The visible infrared solar-infrared split-window technique (VISST), an updated version of the original 3-channel daytime method [7][8] , is used during daytime, which is defined as the time when the solar zenith angle (SZA) is less than 82°. At other times of day, the solar-infrared infrared splitwindow technique (SIST) is used to determine each of the parameter values.…”

Section: Methodsmentioning

confidence: 99%

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Integrated cloud-aerosol-radiation product using CERES, MODIS, CALIPSO, and CloudSat data

et al. 2007

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This paper documents the development of the first integrated data set of global vertical profiles of clouds, aerosols, and radiation using the combined NASA A-Train data from the Aqua Clouds and Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and CloudSat. As part of this effort, cloud data from the CALIPSO lidar and the CloudSat radar are merged with the integrated column cloud properties from the CERES-MODIS analyses. The active and passive datasets are compared to determine commonalities and differences in order to facilitate the development of a 3-dimensional cloud and aerosol dataset that will then be integrated into the CERES broadband radiance footprint. Preliminary results from the comparisons for April 2007 reveal that the CERES-MODIS global cloud amounts are, on average, 0.14 less and 0.15 greater than those from CALIPSO and CloudSat, respectively. These new data will provide unprecedented ability to test and improve global cloud and aerosol models, to investigate aerosol direct and indirect radiative forcing, and to validate the accuracy of global aerosol, cloud, and radiation data sets especially in polar regions and for multi-layered cloud conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Integrated cloud-aerosol-radiation product using CERES, MODIS, CALIPSO, and CloudSat data

et al. 2007

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“…As a relatively common Saxby & Farmer 'Type 5', there was a less compelling case for listing other than being the best preserved 41 and, in recognition to the modular nature of standard designs, the largest surviving example of its type. However, community pressure seems to be a factor in this listing 42 , an example of the communal heritage value of preserving a building in an original setting.…”

Section: Communalmentioning

confidence: 99%

Policy for conservation of heritage railway signal boxes in Great Britain

Reeves

2016

The Historic Environment: Policy & Practice

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“…Thermal infrared (IR) channels can add brightness temperature information. Minnis et al predict clear-sky brightness temperature values using ambient temperature and humidity and then excise pixels outside these intervals [30]. Texture cues can be also used to recognize clouds by their high spatial heterogeneity [31].…”

Section: Introductionmentioning

confidence: 99%

Spectral and Spatial Cloud Detection Onboard for Hyperspectral Remote Sensing Image

Li¹,

Zheng²,

Han³

et al. 2017

Preprint

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It is strongly desirable to accurately detect the clouds in hyperspectral images onboard before compression. However, conventional onboard cloud detection methods are not appropriate to all situation such as shadowed cloud or darken snow covered surfaces which are not identified properly in the NDSI test. In this paper, we propose a new spectral-spatial classification strategy to enhance the orbiting cloud screen performances obtained on hyperspectral images by integrating threshold assisted exponential spectral angle map (TESAM), adaptive Markov random fields (aMRFs) and dynamic stochastic resonance (DSR). First, TESAM is performed to classify the cloud pixels coarsely based on spectral information. Then aMRFs is performed to do optimal process by using spatial information, which improved the classification performance significantly. But some misclassification points still exist after aMRFs processing because of the noise of data in the onboard environment. DSR is used to eliminate misclassification points in binary labeling image after aMRFs. Taking level 0.5 data from hyperion as dataset, the average overall accuracy of the proposed algorithm is 96.28% after test. The method can provide an accurate cloud mask for the on-going EO-1 images and the similar satellites with the same spectral settings without manual intervention. The experiment indicate that the proposed method reveals better performance than the classical onboard cloud detection or current advanced hyperspectral classification methods.

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Fallible Authors

Cited by 17 publications

References 0 publications

Integrated cloud-aerosol-radiation product using CERES, MODIS, CALIPSO, and CloudSat data

Integrated cloud-aerosol-radiation product using CERES, MODIS, CALIPSO, and CloudSat data

Policy for conservation of heritage railway signal boxes in Great Britain

Spectral and Spatial Cloud Detection Onboard for Hyperspectral Remote Sensing Image

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