{"title":"Mapping four decades of lake chlorophyll-a across a continental watershed: A dataset for the lake winnipeg basin (1984-2023).","authors":"Sassan Mohammady, Irena F Creed","doi":"10.1016/j.dib.2025.112035","DOIUrl":null,"url":null,"abstract":"<p><p>We present a standardized pan-watershed dataset of annual chlorophyll-a concentration (Chl-a) in 27,313 lakes (≥ 10 ha) draining into Lake Winnipeg, Canada, spanning 1984-2023. Lake polygons from HydroLAKES were integrated with Landsat 5/7/8 Collection 2 imagery processed in Google Earth Engine (GEE) using a reproducible workflow that (1) filters July-October scenes (peak phytoplankton biomass season), (2) masks non-water pixels from each scene, (3) converts Landsat digital numbers to surface reflectance values, (4) applies a cross-sensor Chl-a retrieval model calibrated against in-situ samples, (5) calculates the spatial mean of Chl-a in each lake for each scene, and (6) calculates the median value of all spatial-mean values per lake per year. Outputs include per-lake annual Chl-a provided as both natural-log and back-transformed Chl-a (µg L⁻¹) plus annual trophic state classes delivered in an Excel workbook and two geodatabases for mapping. The accompanying annotated GEE and R codes, input lake boundaries, and documentation enable transparent reuse and straightforward adaptation to other regions, time periods, or sensors. This resource fills a critical monitoring gap for an agriculturally influenced, bloom-prone continental watershed and supports research and management by establishing productivity baselines, detecting departures from historical conditions, and assessing bloom timing at scales relevant to decision-making. All data, inputs, and code are openly available via Zenodo.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112035"},"PeriodicalIF":1.4000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477918/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.dib.2025.112035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/10/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
We present a standardized pan-watershed dataset of annual chlorophyll-a concentration (Chl-a) in 27,313 lakes (≥ 10 ha) draining into Lake Winnipeg, Canada, spanning 1984-2023. Lake polygons from HydroLAKES were integrated with Landsat 5/7/8 Collection 2 imagery processed in Google Earth Engine (GEE) using a reproducible workflow that (1) filters July-October scenes (peak phytoplankton biomass season), (2) masks non-water pixels from each scene, (3) converts Landsat digital numbers to surface reflectance values, (4) applies a cross-sensor Chl-a retrieval model calibrated against in-situ samples, (5) calculates the spatial mean of Chl-a in each lake for each scene, and (6) calculates the median value of all spatial-mean values per lake per year. Outputs include per-lake annual Chl-a provided as both natural-log and back-transformed Chl-a (µg L⁻¹) plus annual trophic state classes delivered in an Excel workbook and two geodatabases for mapping. The accompanying annotated GEE and R codes, input lake boundaries, and documentation enable transparent reuse and straightforward adaptation to other regions, time periods, or sensors. This resource fills a critical monitoring gap for an agriculturally influenced, bloom-prone continental watershed and supports research and management by establishing productivity baselines, detecting departures from historical conditions, and assessing bloom timing at scales relevant to decision-making. All data, inputs, and code are openly available via Zenodo.
期刊介绍:
Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.