A Six-Year, Spatiotemporally Comprehensive Dataset and Data Retrieval Tool for Analyzing Chlorophyll-a, Turbidity, and Temperature in Utah Lake Using Sentinel and MODIS Imagery

Kaylee B. Tanner, Anna C. Cardall and Gustavious P. Williams ()
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Kaylee B. Tanner: Department of Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA
Anna C. Cardall: Department of Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA
Gustavious P. Williams: Department of Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA

Data, 2025, vol. 10, issue 8, 1-27

Abstract: Data from earth observation satellites provide unique and valuable information about water quality conditions in freshwater lakes but require significant processing before they can be used, even with the use of tools like Google Earth Engine. We use imagery from Sentinel 2 and MODIS and in situ data from the State of Utah Ambient Water Quality Management System (AQWMS) database to develop models and 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 2333 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 interpolated the data using piecewise cubic Hermite interpolation polynomials to preserve the original distribution of the data and provide the most accurate estimates of measurements between observations. 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. We provide this method, tool set, and data to make remotely sensed water quality data more accessible to researchers, water managers, and others interested in Utah Lake and to facilitate the use of satellite data for those interested in applying remote sensing techniques to other waterbodies.

Keywords: Utah Lake; water quality; remote sensing; chlorophyll-a; turbidity; MODIS; Sentinel (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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