Journal ArticleDOI
Discriminating clear sky from clouds with MODIS
Steven A. Ackerman,Kathleen I. Strabala,W. Paul Menzel,Richard A. Frey,Christopher C. Moeller,Liam E. Gumley +5 more
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TLDR
The MODIS cloud mask algorithm as discussed by the authors uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies, which is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options.Abstract:
The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies. It will be produced globally at single-pixel resolution; the algorithm uses as many as 14 of the MEDIS 36 spectral bands to maximize reliable cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or fewer spectral bands. The MEDIS cloud mask is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options. The MEDIS cloud mask algorithm will operate in near real time in a limited computer processing and storage facility with simple easy-to-follow algorithm paths. The MEDIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a series of threshold tests attempts to detect the presence of clouds in the instrument field of view. Each cloud detection test returns a confidence level that the pixel is clear ranging in value from 1 (high) to zero (low). There are several types of tests, where detection of different cloud conditions relies on different tests. Tests capable of detecting similar cloud conditions are grouped together. While these groups are arranged so that independence between them is maximized, few, if any, spectral tests are completely independent. The minimum confidence from all tests within a group is taken to be representative of that group. These confidences indicate absence of particular cloud types. The product of all the group confidences is used to determine the confidence of finding clear-sky conditions. This paper outlines the MEDIS cloud masking algorithm. While no present sensor has all of the spectral bands necessary for testing the complete MEDIS cloud mask, initial validation of some of the individual cloud tests is presented using existing remote sensing data sets.read more
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Journal ArticleDOI
Observational diagnosis of cloud phase in the winter Antarctic atmosphere for parameterizations in climate models
TL;DR: In this paper, the authors explored the cloud phase composition of cold clouds in the Antarctic atmosphere using data from the MODIS and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instruments for the period 2000-2006.
Journal ArticleDOI
A method to retrieve super-thin cloud optical depth over ocean background with polarized sunlight
TL;DR: In this paper, an algorithm that uses the polarization angle of the backscattered solar radiation to detect clouds with optical depth (OD) was proposed, which can detect clouds in a single image.
Patent
Cloud shadow detection: VNIR-SWIR
Arthur L. Boright,John C. Sluder +1 more
TL;DR: In this paper, a data point from top-of-the-air data from an imaging study of an area potentially covered by a cloud shadow is selected, and at least one spectral data measurement associated with the data point is compared with a spectral data threshold delineating between a shadow-covered ground point and a non-shadow covered ground point.
Journal ArticleDOI
Evaluating MODIS dust-detection indices over the Arabian Peninsula
TL;DR: This paper evaluates the suitability of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula and suggests that the BTD (31–32) method and the RSB index are the most suitable indices for detecting dust storms over different land-cover types across the Arabian peninsula.
Journal ArticleDOI
A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations
TL;DR: In this paper, a statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations.
References
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Journal ArticleDOI
Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data
TL;DR: The SNOMAP algorithm as discussed by the authors was developed to map global snow cover using Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning at launch in 1998.
Journal ArticleDOI
Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS)
TL;DR: The authors describe the status ofMODIS-N and its companion instrument MODIS-T (tilt), a tiltable cross-track scanning spectrometer with 32 uniformly spaced channels between 0.410 and 0.875 mu m, used for determining the total precipitable water vapor and atmospheric stability.
Journal ArticleDOI
An improved method for detecting clear sky and cloudy radiances from AVHRR data
R. W. Saunders,K. T. Kriebel +1 more
TL;DR: In this article, the authors proposed a scheme to identify cloud-free and cloud-filled pixels (i.e. fields of view) from satellite radiance data, which consists of five daytime or five night-time tests applied to each individual pixel to determine whether that pixel is cloud free, partly cloudy or cloud filled.
Journal ArticleDOI
GEMI: a non-linear index to monitor global vegetation from satellites
TL;DR: A new vegetation index is proposed which has been designed specifically to reduce the relative effects of these undesirable atmospheric perturbations, while maintaining the information about the vegetation cover.
Journal ArticleDOI
Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for ISCCP
TL;DR: In this article, the cloud detection part of the International Satellite Cloud Climatology Project (ISCCP) analysis is described, and the detection algorithm is supported by global, multiyear surveys of the statistical behavior of satellite-measured infrared and visible radiances to determine those characteristics that differentiate cloudy and clear scenes and how these characteristics vary among climate regimes.