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
Reads0
Chats0
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
Citations
More filters
Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack
TL;DR: Using the NOAA National Ice Centers Interactive Multisensor Snow and Ice Mapping System (IMS) 4km snow maps, this paper identified at least 32 lake effect (LE) storms emanating from Lake Erie and/or Lake Ontario that deposited snow in the CatskillDelaware Watershed in southern New York State between 2004 and 2017.
Journal ArticleDOI
Use of Satellite Data in Monitoring of Hydrophysical Parameters of the Baltic Sea Environment
TL;DR: In this article, the authors show some examples of analysis of processes in marine environment which are possible due to satellite data and algorithms of its processing developed in SatBaltic Project, which concerns supporting of modelling of solar energy inflow to the sea with space-borne input data, identification and analysis of sea ice cover, supporting of oil spill detection, and identification of phenomena which modify spatial distribution of the sea surface temperature.
Journal ArticleDOI
Cloud Detection and Classification Algorithms for Himawari-8 Imager Measurements Based on Deep Learning
TL;DR: In this paper , a deep learning-based cloud detection and classification algorithm for advanced Himawari imager (AHI) measurements from the geostationary satellite HIMAWARI-8 has been developed.
Journal ArticleDOI
Analysis and validation of ocean color and aerosol properties over coastal regions from SGLI based on a simultaneous method
Journal ArticleDOI
Assessment of AMSR2 Ice Extent and Ice Edge in the Arctic Using IMS
TL;DR: This work assesses the AMSR2 (the Advanced Microwave Scanning Radiometer 2) ice extent and ice edge in the Arctic using the ice extent products of NOAA’s Interactive Multisensor Snow and Ice Mapping System (IMS) from the period of July 2015 to July 2019.
References
More filters
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.