High Spatial Resolution Mid‐ and Far‐infrared Imaging Study of NGC 2346

2004 ◽  
Vol 154 (1) ◽  
pp. 302-308 ◽  
Author(s):  
K. Y. L. Su ◽  
D. M. Kelly ◽  
W. B. Latter ◽  
K. A. Misselt ◽  
A. Frank ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
Imaging Study ◽  
Far Infrared

scholarly journals A Blended Sea Ice Concentration Product from AMSR2 and VIIRS

2021 ◽  
Vol 13 (15) ◽  
pp. 2982
Author(s):  
Richard Dworak ◽  
Yinghui Liu ◽  
Jeffrey Key ◽  
Walter N. Meier
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
Sea Ice Concentration ◽  
Clear Sky ◽  
Ice Concentration ◽  
Best Linear Unbiased ◽  
The Individual ◽  
Sky Conditions

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.

scholarly journals Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey

2018 ◽  
Vol 10 (8) ◽  
pp. 1256 ◽  
Author(s):  
Mitchell Goldberg ◽  
Sanmei Li ◽  
Steven Goodman ◽  
Dan Lindsey ◽  
Bill Sjoberg ◽  
...  
Keyword(s):  
United States ◽  
Emergency Management ◽  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
Economic Loss ◽  
The United States ◽  
Flood Monitoring ◽  
Flooded Areas ◽  
Flood Maps

Hurricane Harvey made landfall as a Category-4 storm in the United States on 25 August 2017 in Texas, causing catastrophic flooding in the Houston metropolitan area and resulting in a total economic loss estimated to be about $125 billion. To monitor flooding in the areas affected by Harvey, we used data from sensors aboard the Suomi National Polar-Orbiting Partnership Satellite (SNPP) and the new Geostationary Operational Environmental Satellite (GOES)-16. The GOES-16 Advanced Baseline Imager (ABI) observations are available every 5 min at 1-km spatial resolution across the entire United States, allowing for the possibility of frequent cloud free views of the flooded areas; while the higher resolution 375-m imagery available twice per day from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the SNPP satellite can observe more details of the flooded regions. Combining the high spatial resolution from VIIRS with the frequent observations from ABI offers an improved capability for flood monitoring. The flood maps derived from the SNPP VIIRS and GOES-16 ABI observations were provided to the Federal Emergency Management Agency (FEMA) continuously during Hurricane Harvey. According to FEMA’s estimate on 3 September 2017, approximately 155,000 properties might have been affected by the floodwaters of Hurricane Harvey.

High spatial resolution infrared imaging of L1551-IRS 5 - Direct observations of its circumstellar envelope

1988 ◽  
Vol 327 ◽  
pp. 870 ◽  
Author(s):  
Andrea Moneti ◽  
William J. Forrest ◽  
Judith L. Pipher ◽  
Charles E. Woodward
Keyword(s):  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
Circumstellar Envelope ◽  
Direct Observations

Far-infrared observations of W51 with high spatial resolution

1975 ◽  
Vol 196 ◽  
pp. L31 ◽  
Author(s):  
P. M. Harvey ◽  
W. F. Hoffmann ◽  
M. F. Campbell
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Far Infrared ◽  
Infrared Observations

scholarly journals An Approach for Improving Cirrus Cloud-Top Pressure/Height Estimation by Merging High-Spatial-Resolution Infrared-Window Imager Data with High-Spectral-Resolution Sounder Data

2012 ◽  
Vol 51 (8) ◽  
pp. 1477-1488 ◽  
Author(s):  
Elisabeth Weisz ◽  
W. Paul Menzel ◽  
Nadia Smith ◽  
Richard Frey ◽  
Eva E. Borbas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
High Sensitivity ◽  
Satellite Observation ◽  
High Spectral Resolution ◽  
Vertical Resolution ◽  
Absorption Bands ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Top Pressure

AbstractThe next-generation Visible and Infrared Imaging Radiometer Suite (VIIRS) offers infrared (IR)-window measurements with a horizontal spatial resolution of at least 1 km, but it lacks IR spectral bands that are sensitive to absorption by carbon dioxide (CO2) or water vapor (H2O). The CO2 and H2O absorption bands have high sensitivity for the inference of cloud-top pressure (CTP), especially for semitransparent ice clouds. To account for the lack of vertical resolution, the “merging gradient” (MG) approach is introduced, wherein the high spatial resolution of an imager is combined with the high vertical resolution of a sounder for improved CTP retrievals. The Cross-Track Infrared Sounder (CrIS) is on the same payload as VIIRS. In this paper Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) data are used as proxies for VIIRS and CrIS, respectively, although the approach can be applied to any imager–sounder pair. The MG method establishes a regression relationship between gradients in both the sounder radiances convolved to imager bands and the sounder CTP retrievals. This relationship is then applied to the imager radiance measurements to obtain CTP retrievals at imager spatial resolution. Comparisons with Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud altitudes are presented for a variety of cloud scenes. Results demonstrate the ability of the MG algorithm to add spatial definition to the sounder retrievals with a higher accuracy and precision than those obtained solely from the imager.

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