Applications of the 16 spectral bands on the Advanced Baseline Imager (ABI).

Schmit, Timothy J.; Lindstrom, Scott B.; Gerth, Jordan; Gunshor, Mathew M.

doi:10.15191/nwajom.2018.0604

Cited by 118 publications

(74 citation statements)

References 24 publications

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“…The primary focus of their contribution historically has been in the atmospheric and oceanic sciences. In recent years, much attention has been paid to the applications of GEO satellite to land observation [1][2][3][4][5] because of the enhanced band configuration and calibration strategy [4,6,7]. These "new generation" GEO satellites, such as the Himawari-8 (2014-) [6], FY-4A [4], and GOES-16 (2016-) [7], have land monitoring capabilities with high radiometric and temporal resolutions, in addition to their primary atmosphere and ocean sciences monitoring capabilities.…”

Section: Introductionmentioning

confidence: 99%

Relative Azimuthal-Angle Matching (RAM): A Screening Method for GEO-LEO Reflectance Comparison in Middle Latitude Forests

et al. 2019

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This study introduced a data screening method for comparing the reflectances in middle latitude forest regions collected by a Geostationary Earth Observing (GEO) satellite and a Low Earth Orbit (LEO) satellite. This method attempts to reduce the differences between the relative azimuth angles of the GEO and LEO observations. The method, called relative azimuthal-angle matching (RAM), takes advantage of the high temporal resolution of the GEO satellites, which enables collection of a wide range of relative azimuth angles within a day. The performance of the RAM method was evaluated using data in the visible and near-infrared bands collected by the Himawari-8/Advanced Himawari Imager (AHI) and the Terra/Moderate Resolution Imaging Spectroradiometer (MODIS). The consistency of the reflectance pairs of MODIS and AHI selected by the RAM method was compared with the consistency of the reflectance pairs acquired simultaneously by the two sensors. The data were matched pixel-by-pixel after applying atmospheric corrections and cloud screening. The results show that RAM improved the reflectance ratio by approximately 10% for the red and NIR bands on average relative to the simultaneous observations. Significant improvements in the two bands were observed (20%), especially among data collected in the fall and winter. Performance of RAM depends largely on season. Especially in summer, the reflectance pair chosen by RAM showed less consistency than solar zenith-angle matching (SZM). The results also indicated the relatively large influence of the spectral response functions on the green and red bands of the two sensors.

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Section: Introductionmentioning

confidence: 99%

Relative Azimuthal-Angle Matching (RAM): A Screening Method for GEO-LEO Reflectance Comparison in Middle Latitude Forests

et al. 2019

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“…Each water vapor band provides information about water vapor in approximately the upper, middle, and lower troposphere, respectively. Other bands provide useful information about land/ocean surfaces, the cryosphere, atmospheric temperature and moisture structure, and atmospheric aerosol (Schmit et al, 2017(Schmit et al, , 2018.…”

Section: Goes-16 Abimentioning

confidence: 99%

Observations of Lower Tropospheric Water Vapor Structures in GOES‐16 ABI Imagery

2018

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During the afternoon of 16 April 2017 over Durango, Mexico, Geostationary Operational Environmental Satellite (GOES)‐16 Advanced Baseline Imager (ABI) imagery near 1.38 and 7.34 μm exhibited nonstationary banded features. Alternating patterns of bright (dry) and dark (moist) bands were evident in images of 1.38‐μm reflectance, while corresponding warm (dry) and cool (moist) bands were evident in images of 7.34‐μm brightness temperatures. Based on observations, ambient southwesterly flow across the region is hypothesized to have channeled water vapor through major valleys over western Durango, followed by terrain lifting of water vapor over the plateau of central Durango. Due to lifting, moist bands appeared relatively cool in imagery near 7.34 μm. Similar bright and dark banded patterns were also evident in ABI imagery over Chihuahua, Mexico, on 8 May 2017. During the afternoon over Chihuahua, broken linear segments of cumulus formed within moist portions of the bands. Imagery from the 12.3‐ to 10.3‐μm split window difference also supported the presence of moist bands. In the 8 May 2017 case, the banded features are hypothesized to be the result of horizontal convective rolls. Observations suggested that dark (moist) bands in imagery near 1.38 μm corresponded to the rising branch of horizontal rolls. Due to vertical motion, broken linear segments of cloud streets formed within the rolls. One consequence of newly identified features of the clear‐sky water vapor field indicated the importance of new ABI measurements to aid forecasters in their interpretation of complex mesoscale dynamics.

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“…At this long distance, they have the benefit of being able to view the entire hemisphere of the earth, but the cost is relatively coarse resolution. In the GOES-16 and GOES-17 satellites, the infrared bands currently used to detect fire have ground sample distances of 2 km × 2 km covering the entire hemisphere in one view and the contiguous United States (CONUS) in another view [15]. Each of the two present GOES satellites also provides two moveable mesoscale sectors covering areas of 1000 km by 1000 km, with 1 km × 1 km ground sample distance.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images

Govil

Welch

Ball³

et al. 2020

Remote Sensing

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Pioneering networks of cameras that can search for wildland fire signatures have been in development for some years (High Performance Wireless Research & Education Network—HPWREN cameras and the ALERT Wildfire camera). While these cameras have proven their worth in monitoring fires reported by other means, we have developed a functioning prototype system that can detect smoke from fires usually within 15 min of ignition, while averaging less than one false positive per day per camera. This smoke detection system relies on machine learning-based image recognition software and a cloud-based work-flow capable of scanning hundreds of cameras every minute. The system is operating around the clock in Southern California and has already detected some fires earlier than the current best methods—people calling emergency agencies or satellite detection from the Geostationary Operational Environmental Satellite (GOES) satellites. This system is already better than some commercial systems and there are still many unexplored methods to further improve accuracy. Ground-based cameras are not going to be able to detect every wildfire, and so we are building a system that combines the best of terrestrial camera-based detection with the best approaches to satellite-based detection.

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Applications of the 16 spectral bands on the Advanced Baseline Imager (ABI).

Cited by 118 publications

References 24 publications

Relative Azimuthal-Angle Matching (RAM): A Screening Method for GEO-LEO Reflectance Comparison in Middle Latitude Forests

Relative Azimuthal-Angle Matching (RAM): A Screening Method for GEO-LEO Reflectance Comparison in Middle Latitude Forests

Observations of Lower Tropospheric Water Vapor Structures in GOES‐16 ABI Imagery

Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images

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