ArticleScientific data2025
LAGOS-US LANDSAT: Remotely sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed.
- LAGOS-US LANDSAT: Remotely sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020.Scientific data · 2025Article
- Abrupt changes in algal biomass of thousands of US lakes are related to climate and are more likely in low-disturbance watersheds.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Broad-scale, long-term water quality (WQ) studies are critical for understanding increasing pressures on inland waters but remain rare due to cost. The LAGOS-US LANDSAT dataset provides 37-year remote sensing-derived WQ estimates for thousands of U.S. lakes ≥ 4 ha (1984-2020). WQ estimates use machine-learning models with Landsat imagery and ground-truthed Water Quality Portal data (LAGOS-US LIMNO). The dataset includes: (a) 45.9 million whole-lake water reflectance (six bands and 15 band ratios); (b) 740,627 matchups from 13,756 lakes with in situ data for six WQ variables: chlorophyll, Secchi depth, true color, dissolved organic carbon, total suspended solids, or turbidity; and (c) predictions for each WQ variable across lake-time combinations with quality imagery. Two random forest models were fit for each variable: Holdout-data (75/25 spatially representative train-test split) and Full-data (trained on all data). Variance explained for the Full-data predictions ranged from 20.7% for TSS to 63.7% for Secchi depth. Imagery underwent cloud and pixel quality control, and workflow components were validated guiding future research.
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Registered trials
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.