A six-year, spatiotemporal dataset and data retrieval tool of chlorophyll-a, turbidity, and temperature in Utah Lake

We use imagery from Sentinel 2 and MODIS to generate a highly accessible, easy-to-use csv file of chlorophyll-a (which is an indicator of algal biomass), turbidity, and water temperature measurements on Utah Lake. From a collection of 937 Sentinel 2 images spanning the period from January 2019 to May 2025 we generated 262,081 estimates each of chlorophyll-a and turbidity, with an additional 1,140,777 data points interpolated from those estimates to provide a dataset with a consistent time step. From a collection of 2,333 MODIS images spanning the same time period we extracted 1,390,800 measurements each of daytime water surface temperature and nighttime water surface temperature and interpolated or imputed an additional 12,058 data points from those estimates. We demonstrate the processing steps required to extract usable, accurate estimates of these three water quality parameters from satellite imagery and format them for analysis. We include summary statistics and charts for the resulting dataset which show the usefulness of this data for informing Utah Lake management issues. We include the Jupyter Notebook with the implemented processing steps and the formatted csv file of data as supplemental materials. The Jupyter Notebook can be used to update the Utah Lake data, or can be easily modified to generate similar data for other waterbodies. ] The update added instructions in the jupyter notebook on how to custoize for other locations. The second updated added the paired data. This is the in situ measurement data and the associated satellite bands.我们利用哨兵二号（Sentinel 2）与中分辨率成像光谱仪（Moderate Resolution Imaging Spectroradiometer，简称MODIS）的影像数据，生成了一款高易用性、便于操作的犹他湖（Utah Lake）叶绿素a（chlorophyll-a，藻类生物量的表征指标）、浊度与水温测量值逗号分隔值（Comma-Separated Values，CSV）文件。我们基于2019年1月至2025年5月时段内的937幅哨兵二号影像，共生成262081组叶绿素a与浊度估算值，并通过对上述估算值进行插值处理，额外生成1140777个数据点，以构建具备统一时间步长的数据集。基于同期的2333幅MODIS影像，我们提取了1390800组日间水面温度与夜间水面温度测量值，并通过插值或插补处理，从上述估算值中额外生成12058个数据点。 我们演示了从卫星影像中提取可用且精准的上述三类水质参数估算值，并将其格式化为可用于分析的数据的完整处理流程。我们还提供了该数据集的统计汇总结果与可视化图表，以展示其在为犹他湖管理事务提供决策参考方面的应用价值。 我们将搭载完整处理流程的Jupyter笔记本（Jupyter Notebook）与格式化后的CSV数据文件作为补充材料一并提供。该Jupyter笔记本既可用于更新犹他湖的监测数据，也可通过简单修改，为其他水体生成同类数据集。 本次更新在Jupyter笔记本中新增了针对其他研究区域的自定义适配操作指南。 第二次更新新增了配对数据集，即原位（in situ）测量数据与对应卫星波段数据。