Cloud Adjacency Effects on Top-of-atmosphere Radiance and Ocean Color Data Products: A Statistical Assessment

Ocean color measurements taken near cloud boundaries suffer from cloud adjacency effects (AEs). As a result, ~ 50% of the cloud-free ocean data are flagged as low quality. Quantitative assessment of such effects, as well as the methodology required to minimize, or correct for them, is rarely availab...

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Published in:Remote Sensing of Environment
Main Authors: Feng, Lian, Hu, Chuanmin
Format: Article in Journal/Newspaper
Language:unknown
Published: Digital Commons @ University of South Florida 2016
Subjects:
OCI
Online Access:https://digitalcommons.usf.edu/msc_facpub/1959
https://doi.org/10.1016/j.rse.2015.12.020
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spelling ftunisfloridatam:oai:digitalcommons.usf.edu:msc_facpub-2975 2023-05-15T17:37:09+02:00 Cloud Adjacency Effects on Top-of-atmosphere Radiance and Ocean Color Data Products: A Statistical Assessment Feng, Lian Hu, Chuanmin 2016-01-01T08:00:00Z https://digitalcommons.usf.edu/msc_facpub/1959 https://doi.org/10.1016/j.rse.2015.12.020 unknown Digital Commons @ University of South Florida https://digitalcommons.usf.edu/msc_facpub/1959 https://doi.org/10.1016/j.rse.2015.12.020 Marine Science Faculty Publications Cloud adjacency effects Memory effects Stray light Ocean color TOA radiance Remote sensing reflectance (Rrs) Chl-a OCI MODIS SeaWiFS Life Sciences article 2016 ftunisfloridatam https://doi.org/10.1016/j.rse.2015.12.020 2022-01-20T18:39:53Z Ocean color measurements taken near cloud boundaries suffer from cloud adjacency effects (AEs). As a result, ~ 50% of the cloud-free ocean data are flagged as low quality. Quantitative assessment of such effects, as well as the methodology required to minimize, or correct for them, is rarely available. The goal of this study is to quantify such effects on top-of-atmosphere (TOA) radiance and ocean color data products for MODIS/Terra, MODIS/Aqua, and SeaWiFS measurements. The AEs estimation was based on statistics and an objective method applied to carefully selected clear-water scenes (the number of cloud patches was > 100,000 for each instrument) where ocean properties are relatively homogeneous, over both the North Atlantic and South Pacific. The AEs were quantified as the relative difference between the near-cloud pixels and pixels at least 20 km away from any cloud. Results show that the AEs on TOA radiance share similar patterns among the three missions. Specifically, the AEs decrease sharply as distance increases from cloud edges, and the AEs increase monotonically with increasing wavelengths because they were evaluated in relative rather than absolute terms. However, while discernable memory effects (MEs) are observed on cloud-adjacency pixels of both MODIS missions, they are insignificant on the SeaWiFS mission, and are found in measurements along the scan direction downstream of the clouds, representing > 15% of the total AEs in TOA radiance. The AEs on the retrieved remote sensing reflectance (Rrs) data products are different among the three missions possibly due to their differences in vicarious calibration and uncertainties in atmospheric correction, leading to different patterns in the chlorophyll-a (Chl-a) and normalized Florescence Line Height (nFLH) data products. Large AEs (> 50%) are observed in nFLH of both MODIS/Terra and MODIS/Aqua, likely due to the opposite AEs on Rrs between 667 and 678 nm. Finally, when the OCI Chl-a algorithm is used, the current MODIS stray-light masking window (7 × 5) used to mask the AE-contaminated pixels may be relaxed to 3 × 3 without sacrificing data quality, leading to > 40% of the previously masked low-quality data being recovered for clear waters. Article in Journal/Newspaper North Atlantic Digital Commons University of South Florida (USF) Pacific Remote Sensing of Environment 174 301 313
institution Open Polar
collection Digital Commons University of South Florida (USF)
op_collection_id ftunisfloridatam
language unknown
topic Cloud adjacency effects
Memory effects
Stray light
Ocean color
TOA radiance
Remote sensing reflectance (Rrs)
Chl-a
OCI
MODIS
SeaWiFS
Life Sciences
spellingShingle Cloud adjacency effects
Memory effects
Stray light
Ocean color
TOA radiance
Remote sensing reflectance (Rrs)
Chl-a
OCI
MODIS
SeaWiFS
Life Sciences
Feng, Lian
Hu, Chuanmin
Cloud Adjacency Effects on Top-of-atmosphere Radiance and Ocean Color Data Products: A Statistical Assessment
topic_facet Cloud adjacency effects
Memory effects
Stray light
Ocean color
TOA radiance
Remote sensing reflectance (Rrs)
Chl-a
OCI
MODIS
SeaWiFS
Life Sciences
description Ocean color measurements taken near cloud boundaries suffer from cloud adjacency effects (AEs). As a result, ~ 50% of the cloud-free ocean data are flagged as low quality. Quantitative assessment of such effects, as well as the methodology required to minimize, or correct for them, is rarely available. The goal of this study is to quantify such effects on top-of-atmosphere (TOA) radiance and ocean color data products for MODIS/Terra, MODIS/Aqua, and SeaWiFS measurements. The AEs estimation was based on statistics and an objective method applied to carefully selected clear-water scenes (the number of cloud patches was > 100,000 for each instrument) where ocean properties are relatively homogeneous, over both the North Atlantic and South Pacific. The AEs were quantified as the relative difference between the near-cloud pixels and pixels at least 20 km away from any cloud. Results show that the AEs on TOA radiance share similar patterns among the three missions. Specifically, the AEs decrease sharply as distance increases from cloud edges, and the AEs increase monotonically with increasing wavelengths because they were evaluated in relative rather than absolute terms. However, while discernable memory effects (MEs) are observed on cloud-adjacency pixels of both MODIS missions, they are insignificant on the SeaWiFS mission, and are found in measurements along the scan direction downstream of the clouds, representing > 15% of the total AEs in TOA radiance. The AEs on the retrieved remote sensing reflectance (Rrs) data products are different among the three missions possibly due to their differences in vicarious calibration and uncertainties in atmospheric correction, leading to different patterns in the chlorophyll-a (Chl-a) and normalized Florescence Line Height (nFLH) data products. Large AEs (> 50%) are observed in nFLH of both MODIS/Terra and MODIS/Aqua, likely due to the opposite AEs on Rrs between 667 and 678 nm. Finally, when the OCI Chl-a algorithm is used, the current MODIS stray-light masking window (7 × 5) used to mask the AE-contaminated pixels may be relaxed to 3 × 3 without sacrificing data quality, leading to > 40% of the previously masked low-quality data being recovered for clear waters.
format Article in Journal/Newspaper
author Feng, Lian
Hu, Chuanmin
author_facet Feng, Lian
Hu, Chuanmin
author_sort Feng, Lian
title Cloud Adjacency Effects on Top-of-atmosphere Radiance and Ocean Color Data Products: A Statistical Assessment
title_short Cloud Adjacency Effects on Top-of-atmosphere Radiance and Ocean Color Data Products: A Statistical Assessment
title_full Cloud Adjacency Effects on Top-of-atmosphere Radiance and Ocean Color Data Products: A Statistical Assessment
title_fullStr Cloud Adjacency Effects on Top-of-atmosphere Radiance and Ocean Color Data Products: A Statistical Assessment
title_full_unstemmed Cloud Adjacency Effects on Top-of-atmosphere Radiance and Ocean Color Data Products: A Statistical Assessment
title_sort cloud adjacency effects on top-of-atmosphere radiance and ocean color data products: a statistical assessment
publisher Digital Commons @ University of South Florida
publishDate 2016
url https://digitalcommons.usf.edu/msc_facpub/1959
https://doi.org/10.1016/j.rse.2015.12.020
geographic Pacific
geographic_facet Pacific
genre North Atlantic
genre_facet North Atlantic
op_source Marine Science Faculty Publications
op_relation https://digitalcommons.usf.edu/msc_facpub/1959
https://doi.org/10.1016/j.rse.2015.12.020
op_doi https://doi.org/10.1016/j.rse.2015.12.020
container_title Remote Sensing of Environment
container_volume 174
container_start_page 301
op_container_end_page 313
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