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Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint

Overview of attention for article published in Frontiers in Remote Sensing, May 2022
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)

Mentioned by

twitter
9 X users

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
5 Mendeley
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Title
Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint
Published in
Frontiers in Remote Sensing, May 2022
DOI 10.3389/frsen.2022.846174
Authors

Zachary Fasnacht, Joanna Joiner, David Haffner, Wenhan Qin, Alexander Vasilkov, Patricia Castellanos, Nickolay Krotkov

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 20%
Researcher 1 20%
Unknown 3 60%
Readers by discipline Count As %
Unspecified 1 20%
Earth and Planetary Sciences 1 20%
Unknown 3 60%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 May 2022.
All research outputs
#7,364,613
of 25,992,468 outputs
Outputs from Frontiers in Remote Sensing
#1
of 1 outputs
Outputs of similar age
#139,181
of 450,842 outputs
Outputs of similar age from Frontiers in Remote Sensing
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 4.4. This one scored the same or higher as 0 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 450,842 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them