Mapping four decades of lake chlorophyll-a across a continental watershed: A dataset for the lake winnipeg basin (1984-2023).

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES
Data in Brief Pub Date : 2025-09-11 eCollection Date: 2025-10-01 DOI:10.1016/j.dib.2025.112035
Sassan Mohammady, Irena F Creed
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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.

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绘制横跨大陆流域的40年湖泊叶绿素-a:温尼伯湖流域的数据集(1984-2023)。
本文提出了1984-2023年加拿大温尼伯湖27,313个湖泊(≥10公顷)年度叶绿素-a浓度(Chl-a)的标准化泛流域数据集。利用可重复的工作流程,将来自HydroLAKES的湖泊多边形与谷歌Earth Engine (GEE)处理的Landsat 5/7/8 Collection 2图像整合在一起,该工作流程:(1)过滤7月至10月的场景(浮游植物生物量高峰季节),(2)屏蔽每个场景中的非水像素,(3)将Landsat数字转换为地表反射率值,(4)应用针对原位样品校准的跨传感器Chl-a检索模型,(5)计算每个场景中每个湖泊的Chl-a的空间平均值。(6)计算各湖泊年空间均值的中位数。产出包括每个湖每年的Chl-a(以自然对数和反向转换的Chl-a(µg L⁻)形式提供),以及在Excel工作簿和两个地理数据库中提供的年度营养状态分类。附带的注释GEE和R代码、输入湖边界和文档支持透明的重用和直接适应其他区域、时间段或传感器。这一资源填补了对受农业影响、易发生水华的大陆流域的关键监测空白,并通过建立生产力基线、检测与历史条件的偏离以及评估与决策相关的尺度上的水华时间来支持研究和管理。所有数据、输入和代码都可以通过Zenodo公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
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.
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