Evidence map›Paper›PMID 40784949›Full record

ArticleScientific reports2025

From community to science to community, enhancing remote sensing of water quality in Chesapeake Bay tributaries through participatory science.

Min-Sun Lee, Maria Tzortziou, Ji-Eun Park, Tong Lin, Patrick Neale, Shelby Brown, Tara Sill, Alison Cawood

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Min-Sun Lee *The City College of New York, City University of New York, New York, NY, USA.
Maria Tzortziou *The City College of New York, City University of New York, New York, NY, USA. mtzortziou@ccny.cuny.edu.
Ji-Eun ParkThe City College of New York, City University of New York, New York, NY, USA.
Tong LinThe City College of New York, City University of New York, New York, NY, USA.
Patrick NealeSmithsonian Institution, Smithsonian Environmental Research Center, Edgewater, MD, USA.
Shelby BrownSmithsonian Institution, Smithsonian Environmental Research Center, Edgewater, MD, USA.
Tara SillSmithsonian Institution, Smithsonian Environmental Research Center, Edgewater, MD, USA.
Alison CawoodSmithsonian Institution, Smithsonian Environmental Research Center, Edgewater, MD, USA.

Funding

NASA Citizen Science for Earth Systems Program 80NSSC22K1912National Institute of Fisheries Science R2025015
6 · The paper itself

Abstract

Citizen, or participatory, science provides a powerful tool to both enrich environmental datasets as well as increase public awareness of pressing environmental issues - especially in coastal regions. Here, we used rich bio-optical datasets collected by trained volunteers to develop, optimize, and validate new satellite retrievals of key water quality indicators in the economically and ecologically valuable tributaries of the Chesapeake Bay Estuary. The optimized algorithms were applied to imagery from Landsat/OLI, Sentinel-2/MSI, and Sentinel-3/OLCI, and effectively captured the temporal and spatial distribution of turbidity, chlorophyll-a concentration, and dissolved organic matter dynamics in both optically complex tributaries and the main stem of the Bay. Our results highlight the significant benefits of engaging volunteers in estuarine water quality monitoring activities, particularly for participatory data collection, standardized data collection across coastal systems, and improvement of satellite biogeochemical retrievals in complex nearshore waters that directly impact coastal communities and economies.

Identifiers

PMID40784949
PMCPMC12336316

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.