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.