ABI Imagery from the GOES-R Series

Schmit, Timothy J.; Gunshor, Mathew M.

doi:10.1016/b978-0-12-814327-8.00004-4

Cited by 20 publications

(17 citation statements)

References 23 publications

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“…The ABI is the primary instrument on board GOES-16/17 and is designed for monitoring land and ocean surfaces, the atmosphere, and cloud formation (Schmit et al, 2017(Schmit et al, , 2018. The ABI has 16 spectral bands that measure solar reflected radiance in the visible and near-infrared wavelengths and emitted radiance at infrared wavelengths (Schmit and Gunshor, 2020). With multiple infrared bands positioned in atmospheric absorption regions and in atmospheric windows, the ABI can collect information from the Earth's surface and multiple levels in the atmosphere (Schmit and Gunshor, 2020).…”

Section: Advanced Baseline Imager (Abi)mentioning

confidence: 99%

“…The ABI has 16 spectral bands that measure solar reflected radiance in the visible and near-infrared wavelengths and emitted radiance at infrared wavelengths (Schmit and Gunshor, 2020). With multiple infrared bands positioned in atmospheric absorption regions and in atmospheric windows, the ABI can collect information from the Earth's surface and multiple levels in the atmosphere (Schmit and Gunshor, 2020). Multiple scan modes are used to provide nearhemispheric geographic coverage, with spatial resolutions between 0.5 and 2 km.…”

Section: Advanced Baseline Imager (Abi)mentioning

confidence: 99%

“…Operating in the flex mode, the ABI collected a full disk image every 15 min until April 2019 but now collects a full disk image every 10 min (with the exception of GOES-17 during parts of the year). The ABI also col-lects a CONUS scene every 5 min and two mesoscale scenes every minute in the flex mode (Schmit and Gunshor, 2020).…”

Section: Advanced Baseline Imager (Abi)mentioning

confidence: 99%

See 2 more Smart Citations

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

Khan¹,

Stoy

Douglas

et al. 2021

Biogeosciences

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Abstract. Environmental science is increasingly reliant on remotely sensed observations of the Earth's surface and atmosphere. Observations from polar-orbiting satellites have long supported investigations on land cover change, ecosystem productivity, hydrology, climate, the impacts of disturbance, and more and are critical for extrapolating (upscaling) ground-based measurements to larger areas. However, the limited temporal frequency at which polar-orbiting satellites observe the Earth limits our understanding of rapidly evolving ecosystem processes, especially in areas with frequent cloud cover. Geostationary satellites have observed the Earth's surface and atmosphere at high temporal frequency for decades, and their imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 min. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solar radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential to expand ABI observations to estimate vegetation greenness, moisture, and productivity at a high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarse resolution of ABI observations through multisensor fusion to resolve landscape heterogeneity and to leverage observations from ABI to study the carbon cycle and ecosystem function at unprecedented temporal frequency.

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Section: Advanced Baseline Imager (Abi)mentioning

confidence: 99%

Section: Advanced Baseline Imager (Abi)mentioning

confidence: 99%

Section: Advanced Baseline Imager (Abi)mentioning

confidence: 99%

See 1 more Smart Citation

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

Khan¹,

Stoy

Douglas

et al. 2021

Biogeosciences

View full text Add to dashboard Cite

show abstract

“…ABI can monitor multiple layers of the Earth-atmosphere system with a wide range of imagery and radiometric information related to cloud products (chap. 4 and 6 [49]). The nominal spatial resolution at the sub satellite pixel is 0.5 km for the "Red" band, 1 km for "Blue", "Vegetation", "Snow/Ice" bands and 2 km for other thermal emissive bands.…”

Section: Geostationary Satellite Observationsmentioning

confidence: 97%

Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

et al. 2020

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Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.

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“…Operating in the flex mode, ABI collected a full disk image every 15 minutes until April 2019 but now collects a full disk image every 10 minutes (with the exception of GOES-17 during parts of the year). ABI also collects a CONUS scene every 5 minutes, and two mesoscale scenes every minute in the flex mode (Schmit and Gunshor, 2020).…”

Section: Introductionmentioning

confidence: 99%

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

Khan¹,

Stoy²,

Douglas³

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Environmental science is increasingly reliant on remotely-sensed observations of the Earth's surface and atmosphere. Observations from polar-orbiting satellites have long supported investigations on land cover change, ecosystem productivity, hydrology, climate, the impacts of disturbance, and more, and are critical for extrapolating (upscaling) ground-based measurements to larger areas. However, the limited temporal frequency at which polar-orbiting satellites observe the Earth limits our understanding of rapidly evolving ecosystem processes, especially in areas with frequent cloud cover. Geostationary satellites have observed the Earth's surface and atmosphere at high temporal frequency for decades, and their imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly-used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 minutes. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solar radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential for expanding ABI observations to estimate vegetation greenness, moisture, and productivity at high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarse resolution of ABI observations through multi-sensor fusion to resolve landscape heterogeneity and to leverage observations from ABI to study the carbon cycle and ecosystem function at unprecedented temporal frequency.

show abstract

ABI Imagery from the GOES-R Series

Cited by 20 publications

References 23 publications

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

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