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

2021 ◽  
Vol 18 (13) ◽  
pp. 4117-4141
Author(s):  
Anam M. Khan ◽  
Paul C. Stoy ◽  
James T. Douglas ◽  
Martha Anderson ◽  
George Diak ◽  
...  
Keyword(s):  
Land Surface ◽  
Environmental Science ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Landscape Heterogeneity ◽  
Temporal Frequency ◽  
Western Hemisphere ◽  
High Temporal Frequency ◽  
Ongoing Work ◽  
Moderate Resolution Imaging Spectroradiometer

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.

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

2021 ◽  
Author(s):  
Anam M. Khan ◽  
Paul C. Stoy ◽  
James T. Douglas ◽  
Martha Anderson ◽  
George Diak ◽  
...  
Keyword(s):  
Land Surface ◽  
Environmental Science ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Landscape Heterogeneity ◽  
Temporal Frequency ◽  
Western Hemisphere ◽  
High Temporal Frequency ◽  
Ongoing Work ◽  
Moderate Resolution Imaging Spectroradiometer

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.

scholarly journals Use of multi-spectral visible and near-infrared satellite data for timely estimates of the Earth's surface reflectance in cloudy conditions: Part 2 - image restoration with HICO satellite data in overcast conditions

2021 ◽  
Author(s):  
Joanna Joiner ◽  
Zachary Fasnacht ◽  
Bo-Cai Gao ◽  
Wenhan Qin
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Land Surface ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Space Station ◽  
Water Quality Assessment ◽  
Low Contrast

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.

scholarly journals BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

2016 ◽  
Vol III-7 ◽  
pp. 3-8 ◽  
Author(s):  
M. Matsuoka ◽  
M. Takagi ◽  
S. Akatsuka ◽  
R. Honda ◽  
A. Nonomura ◽  
...  
Keyword(s):  
Surface Properties ◽  
Land Surface ◽  
Goodness Of Fit ◽  
Near Infrared ◽  
Temporal Frequency ◽  
Atmospheric Correction ◽  
Time Of Day ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

scholarly journals HYDRA2: A Multispectral Data Analysis Toolkit for Sensors on Suomi-NPP and Other Current Satellite Platforms

2016 ◽  
Vol 97 (7) ◽  
pp. 1283-1294
Author(s):  
Tom Rink ◽  
W. Paul Menzel ◽  
Liam Gumley ◽  
Kathy Strabala
Keyword(s):  
Cross Sections ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Hyperspectral Data ◽  
Advanced Technology ◽  
Multispectral Data ◽  
Brightness Temperatures ◽  
Sensing Applications ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Multispectral Analysis

Abstract The Hyperspectral Data Viewer for Development of Research Applications, version 2 (HYDRA2), is a freeware-based multispectral analysis toolkit for satellite data that assists scientists in research and development, as well as education and training of remote sensing applications. HYDRA2 users can explore and visualize relationships between sensor measurements (brightness temperatures for infrared and reflectances for visible/near-infrared wavelengths) using spectral diagrams, cross sections, scatterplots, multiband combinations, and color enhancements on a pixel-by-pixel basis. HYDRA2 can be used with direct broadcast and archived data from sensors on board the National Oceanic and Atmospheric Administration (NOAA)/National Aeronautics and Space Administration (NASA) Suomi–National Polar-Orbiting Partnership (Suomi-NPP), NASA Aqua/Terra, European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteorological Operational (MetOp), and Chinese Fengyun-3 platforms. This paper describes HYDRA2 and presents some examples using data retrievals from the Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS), Cross-Track Infrared Sounder (CrIS), Advanced Technology Microwave Sounder (ATMS), and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments.

scholarly journals Description and validation of an AOT product over land at the 0.6 μm channel of the SEVIRI sensor onboard MSG

2011 ◽  
Vol 4 (11) ◽  
pp. 2543-2565 ◽  
Author(s):  
E. Bernard ◽  
C. Moulin ◽  
D. Ramon ◽  
D. Jolivet ◽  
J. Riedi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Weather Prediction ◽  
Temporal Frequency ◽  
Reasonable Assumption ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Aerosol Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Level 2 ◽  
Good Agreement

Abstract. The Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) launched in 2003 by EUMETSAT is dedicated to the Nowcasting applications and Numerical Weather Prediction and to the provision of observations for climate monitoring and research. We use the data in visible and near infrared (NIR) channels to derive the aerosol optical thickness (AOT) over land. The algorithm is based on the assumption that the top of the atmosphere (TOA) reflectance increases with the aerosol load. This is a reasonable assumption except in case of absorbing aerosols above bright surfaces. We assume that the minimum in a 14-days time series of the TOA reflectance is, once corrected from gaseous scattering and absorption, representative of the surface reflectance. The AOT and the aerosol model (a set of 5 models is used), are retrieved by matching the simulated TOA reflectance with the TOA reflectances measured by SEVIRI in its visible and NIR spectral bands. The high temporal resolution of the data acquisition by SEVIRI allows to retrieve the AOT every 15 min with a spatial resolution of 3 km at sub-satellite point, over the entire SEVIRI disk covering Europe, Africa and part of South America. The resulting AOT, a level 2 product at the native temporal and spatial SEVIRI resolutions, is presented and evaluated in this paper. The AOT has been validated using ground based measurements from AErosol RObotic NETwork (AERONET), a sun-photometer network, focusing over Europe for 3 months in 2006. The SEVIRI estimates correlate well with the AERONET measurements, r = 0.64, with a slight overestimate, bias = −0.017. The sources of errors are mainly the cloud contamination and the bad estimation of the surface reflectance. The temporal evolutions exhibited by both datasets show very good agreement which allows to conclude that the AOT Level 2 product from SEVIRI can be used to quantify the aerosol content and to monitor its daily evolution with a high temporal frequency. The comparison with daily maps of Moderate Resolution Imaging Spectroradiometer (MODIS) AOT level 3 product shows qualitative good agreement in the retrieved geographic patterns of AOT. Given the high spatial and temporal resolutions obtained with this approach, our results have clear potential for applications ranging from air quality monitoring to climate studies. This paper presents a first evaluation and validation of the derived AOT over Europe in order to document the overall quality of a product that will be made publicly available to the users of the aforementioned research communities.

scholarly journals Improving GOES Advanced Baseline Imager (ABI) Aerosol Optical Depth (AOD) Retrievals using an Empirical Bias Correction Algorithm

2020 ◽  
Author(s):  
Hai Zhang ◽  
Shobha Kondragunta ◽  
Istvan Laszlo ◽  
Mi Zhou
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Bias Correction ◽  
Land Surface ◽  
Infrared Imaging ◽  
Retrieval Algorithm ◽  
Correction Algorithm ◽  
Time Step ◽  
Modis Aod ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multi-band algorithm similar to those of polar-orbiting satellites’ sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Therefore, ABI AOD is expected to have accuracy and precision comparable to MODIS AOD and VIIRS AOD. However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to errors in the land surface reflectance relationship between the bands used in the ABI AOD retrieval algorithm, which vary with respect to the Sun-satellite geometry. To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30-day period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period August 6 to December 31, 2018 are used to validate the bias correction algorithm. For the top 2 qualities ABI AOD, after bias correction, the correlation between ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root mean square error (RMSE) improves from 0.09 to 0.05. These results for the bias corrected top 2 qualities ABI AOD are comparable to those of the uncorrected high-quality ABI AOD. Thus, by using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the area coverage of ABI AOD is substantially increased without loss of data accuracy.

scholarly journals Calibration of the DSCOVR EPIC Visible and NIR Channels using Multiple LEO Radiometers

2021 ◽  
Vol 2 ◽  
Author(s):  
Igor V. Geogdzhayev ◽  
Alexander Marshak ◽  
Mikhail Alexandrov
Keyword(s):  
Near Infrared ◽  
Infrared Imaging ◽  
Statistical Tests ◽  
Calibration Procedure ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Full Dataset ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Calibration Coefficients

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

scholarly journals Determining the Boundary and Probability of Surface Urban Heat Island Footprint Based on a Logistic Model

2019 ◽  
Vol 11 (11) ◽  
pp. 1368 ◽  
Author(s):  
Zhi Qiao ◽  
Chen Wu ◽  
Dongqi Zhao ◽  
Xinliang Xu ◽  
Jilin Yang ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Landscape Heterogeneity ◽  
Remote Sensing Data ◽  
Precise Determination ◽  
Heat Island ◽  
Rural Regions ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Moderate Resolution Imaging Spectroradiometer

Studies of the spatial extent of surface urban heat island (SUHI or UHISurf) effects require precise determination of the footprint (FP) boundary. Currently available methods overestimate or underestimate the SUHI FP boundary, and can even alter its morphology, due to theoretical limitations on the ability of their algorithms to accurately determine the impacts of the shape, topography, and landscape heterogeneity of the city. The key to determining the FP boundary is identifying background temperatures in reference rural regions. Due to the instability of remote sensing data, these background temperatures should be determined automatically rather than manually, to eliminate artificial bias. To address this need, we developed an algorithm that adequately represents the decay of land surface temperature (LST) from the urban center to surrounding rural regions, and automatically calculates thresholds for reference rural LSTs in all directions based on a logistic curve. In this study, we applied this algorithm with data from the Aqua Moderate Resolution Imaging Spectroradiometer (Aqua/MODIS) 8-day level 3 (L3) LST global grid product to delineate precise SUHI FPs for the Beijing metropolitan area during the summers of 2004–2018 and determine the interannual and diurnal variations in FP boundaries and their relationship with SUHI intensity.

scholarly journals Use of Hyper-Spectral Visible and Near-Infrared Satellite Data for Timely Estimates of the Earth’s Surface Reflectance in Cloudy Conditions: Part 2- Image Restoration With HICO Satellite Data in Overcast Conditions

2021 ◽  
Vol 2 ◽  
Author(s):  
Joanna Joiner ◽  
Zachary Fasnacht ◽  
Bo-Cai Gao ◽  
Wenhan Qin
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Land Surface ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Space Station ◽  
Low Contrast ◽  
Hyper Spectral

Satellite-based visible and near-infrared imaging of the Earth’s surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using hyper-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion of the panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and operational applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment, as well as disturbance, change, hazard, and disaster detection.

Daytime Global Cloud Typing from AVHRR and VIIRS: Algorithm Description, Validation, and Comparisons

2005 ◽  
Vol 44 (6) ◽  
pp. 804-826 ◽  
Author(s):  
Michael J. Pavolonis ◽  
Andrew K. Heidinger ◽  
Taneil Uttal
Keyword(s):  
Near Infrared ◽  
Infrared Imaging ◽  
Processing System ◽  
Satellite System ◽  
The Other ◽  
Phase Identification ◽  
Cloud Type ◽  
Spectral Bands ◽  
Atmospheric Radiation Measurement ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Three multispectral algorithms for determining the cloud type of previously identified cloudy pixels during the daytime, using satellite imager data, are presented. Two algorithms were developed for use with 0.65-, 1.6-/3.75-, 10.8-, and 12.0-μm data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites. The AVHRR algorithms are identical except for the near-infrared data that are used. One algorithm uses AVHRR channel 3a (1.6 μm) reflectances, and the other uses AVHRR channel 3b (3.75 μm) reflectance estimates. Both of these algorithms are necessary because the AVHRRs on NOAA-15 through NOAA-17 have the capability to transmit either channel 3a or 3b data during the day, whereas all of the other AVHRRs on NOAA-7 through NOAA-14 can only transmit channel 3b data. The two AVHRR cloud-typing schemes are used operationally in NOAA’s extended Clouds from AVHRR (CLAVR)-x processing system. The third algorithm utilizes additional spectral bands in the 1.38- and 8.5-μm regions of the spectrum that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible–Infrared Imaging Radiometer Suite (VIIRS). The VIIRS will eventually replace the AVHRR on board the National Polar-Orbiting Operational Environmental Satellite System (NPOESS), which is currently scheduled to be launched in 2009. Five cloud-type categories are employed: warm liquid water, supercooled water–mixed phase, opaque ice, nonopaque high ice (cirrus), and cloud overlap (multiple cloud layers). Each algorithm was qualitatively evaluated through scene analysis and then validated against inferences of cloud type that were derived from ground-based observations of clouds at the three primary Atmospheric Radiation Measurement (ARM) Program sites to help to assess the potential continuity of a combined AVHRR channel 3a–AVHRR channel 3b–VIIRS cloud-type climatology. In this paper, “validation” is strictly defined as comparisons with ground-based estimates that are completely independent of the satellite retrievals. It was determined that the two AVHRR algorithms produce nearly identical results except for certain thin clouds and cloud edges. The AVHRR 3a algorithm tends to incorrectly classify the thin edges of some low- and midlevel clouds as cirrus and opaque ice more often than the AVHRR 3b algorithm. The additional techniques implemented in the VIIRS algorithm result in a significant improvement in the identification of cirrus clouds, cloud overlap, and overall phase identification of thin clouds, as compared with the capabilities of the AVHRR algorithms presented in this paper.

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