Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentratio...

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Vydáno v:Remote sensing of environment Ročník 266; s. 112685 - 14
Hlavní autoři: Seegers, Bridget N., Werdell, P. Jeremy, Vandermeulen, Ryan A., Salls, Wilson, Stumpf, Richard P., Schaeffer, Blake A., Owens, Tommy J., Bailey, Sean W., Scott, Joel P., Loftin, Keith A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Goddard Space Flight Center Elsevier Inc 01.12.2021
Elsevier
Elsevier BV
Témata:
Abbreviations
Alaska
Algorithm validation
Algorithms
Aquatic ecosystems
Aquatic organisms
Case studies
Chlorophyll
Chlorophylla
Cyanobacteria
Drinking water
Earth Resources And Remote Sensing
environment
environmental health
Imaging spectrometers
Inland waters
Lakes
MERIS timeseries
Phytoplankton
radiometry
Remote sensing
Satellites
spectrometers
stakeholders
surface water
Time series
time series analysis
Water monitoring
Water quality
Water quality management
Water quality monitoring
United States > US
Alaska
MERIS timeseries
Algorithm validation
Chlorophylla
Remote sensing
Water quality
Inland waters
Algorithm Validation
Remote Sensing
Inland Waters
Meris Timeseries
Water Quality
ISSN:0034-4257, 1879-0704
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Abstract Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS. •Timeseries data set was developed of satellite remote sensing of US inland waters.•The dataset includes radiometry and an estimation of cyanobacteria abundance.•The dataset is public and easily modifiable for a variety of end-user applications.•A case study on algorithm development to estimate cyanoHAB chlorophyll is presented.•This case study provides steps for end-user algorithm development.
AbstractList Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.
Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS. •Timeseries data set was developed of satellite remote sensing of US inland waters.•The dataset includes radiometry and an estimation of cyanobacteria abundance.•The dataset is public and easily modifiable for a variety of end-user applications.•A case study on algorithm development to estimate cyanoHAB chlorophyll is presented.•This case study provides steps for end-user algorithm development.
Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.
Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll lgorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, Chl , which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.
ArticleNumber 112685
Audience PUBLIC
Author Owens, Tommy J.
Seegers, Bridget N.
Bailey, Sean W.
Werdell, P. Jeremy
Vandermeulen, Ryan A.
Salls, Wilson
Schaeffer, Blake A.
Scott, Joel P.
Loftin, Keith A.
Stumpf, Richard P.
AuthorAffiliation d U.S. Environmental Protection Agency, Office of Research and Development, Durham, NC 27711, USA
a NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Greenbelt, MD 20771, USA
c Science Systems and Applications Inc., Lanham, MD 20706, USA
f Science Application International Corp., Reston, VA 20190, USA
e NOAA, National Ocean Service, Silver Spring, MD 20910,USA
g U.S. Geological Survey, Kansas Water Science Center, Lawrence, KS 66049, USA
b Universities Space Research Association (USRA), Columbia, MD 21046, USA
AuthorAffiliation_xml – name: g U.S. Geological Survey, Kansas Water Science Center, Lawrence, KS 66049, USA
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  organization: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Greenbelt, MD 20771, USA
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  surname: Werdell
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  organization: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Greenbelt, MD 20771, USA
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  surname: Salls
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  email: richard.stumpf@noaa.gov
  organization: NOAA, National Ocean Service, Silver Spring, MD 20910,USA
– sequence: 6
  givenname: Blake A.
  surname: Schaeffer
  fullname: Schaeffer, Blake A.
  email: Schaeffer.Blake@epa.gov
  organization: U.S. Environmental Protection Agency, Office of Research and Development, Durham, NC 27711, USA
– sequence: 7
  givenname: Tommy J.
  surname: Owens
  fullname: Owens, Tommy J.
  email: tommy.owens@nasa.gov
  organization: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Greenbelt, MD 20771, USA
– sequence: 8
  givenname: Sean W.
  surname: Bailey
  fullname: Bailey, Sean W.
  email: sean.w.bailey@nasa.gov
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  givenname: Joel P.
  surname: Scott
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– sequence: 10
  givenname: Keith A.
  surname: Loftin
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  email: kloftin@usgs.gov
  organization: U.S. Geological Survey, Kansas Water Science Center, Lawrence, KS 66049, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36424983$$D View this record in MEDLINE/PubMed
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Keywords MERIS timeseries
Algorithm validation
Chlorophylla
Remote sensing
Water quality
Inland waters
Algorithm Validation
Remote Sensing
Inland Waters
Meris Timeseries
Water Quality
Language English
License This is an open access article under the CC BY license.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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MergedId FETCHMERGED-LOGICAL-c533t-54810c8522ef946e7c44893c4bd1cd1ca7d34d1a819b90aa211e81bc36b648d93
Notes GSFC
Goddard Space Flight Center
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crossref_citationtrail_10_1016_j_rse_2021_112685
crossref_primary_10_1016_j_rse_2021_112685
nasa_ntrs_20210026776
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PublicationDate 2021-12-01
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PublicationDate_xml – month: 12
  year: 2021
  text: 2021-12-01
  day: 01
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PublicationTitle Remote sensing of environment
PublicationTitleAlternate Remote Sens Environ
PublicationYear 2021
Publisher Elsevier Inc
Elsevier
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Snippet Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic...
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StartPage 112685
SubjectTerms Abbreviations
Alaska
Algorithm validation
Algorithms
Aquatic ecosystems
Aquatic organisms
Case studies
Chlorophyll
Chlorophylla
Cyanobacteria
Drinking water
Earth Resources And Remote Sensing
environment
environmental health
Imaging spectrometers
Inland waters
Lakes
MERIS timeseries
Phytoplankton
radiometry
Remote sensing
Satellites
spectrometers
stakeholders
surface water
Time series
time series analysis
Water monitoring
Water quality
Water quality management
Water quality monitoring
Title Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a
URI https://dx.doi.org/10.1016/j.rse.2021.112685
https://ntrs.nasa.gov/citations/20210026776
https://www.ncbi.nlm.nih.gov/pubmed/36424983
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https://www.proquest.com/docview/2636514174
https://www.proquest.com/docview/2740512959
https://pubmed.ncbi.nlm.nih.gov/PMC9680834
Volume 266
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