Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

There is currently a scarcity of paired in-situ aquatic optical and biogeophysical data for productive inland waters, which critically hinders our capacity to develop and validate robust retrieval models for Earth Observation applications. This study aims to address this limitation through the devel...

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Bibliographic Details
Main Authors: Jeremy Kravitz, Mark Matthews, Lisl Lain, Sarah Fawcett, Stewart Bernard
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-03-01
Series:Frontiers in Environmental Science
Subjects:
eutrophication
Earth observation
water quality
inland waters
machine learning
radiative transfer modeling
Online Access:https://www.frontiersin.org/articles/10.3389/fenvs.2021.587660/full

Internet

https://www.frontiersin.org/articles/10.3389/fenvs.2021.587660/full

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