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Paper title Advanced water quality monitoring of optically variable lake systems using S2/MSI and L8/OLI imagery in an automated high performance computing environment
Authors
  1. Leif Olmanson University of Minnesota Speaker
  2. David Porter University of Minnesota
Form of presentation Poster
Topics
  • A7. Hydrology and Water Cycle
    • A7.06 EO for monitoring water quality and ecological status in inland waters
Abstract text In lake rich regions, protecting water quality is critically important because of the ecological and economic importance of recreational activities and tourism. To ensure the health of inland aquatic ecosystems on both a local and regional scale, more comprehensive monitoring techniques to complement conventional field sampling methodologies are needed for effective management. For over 25 years, our previous statewide water quality mapping in Minnesota, USA has primarily relied on Landsat satellites. However, measurements have been limited to water clarity and colored dissolved organic matter (CDOM) due to inherent Landsat sensor spectral band configurations. Sentinel-2 Multispectral Imager (S2/MSI) on the other hand offers several red-edge bands that increase the accuracy of chlorophyll concentrations. The increased temporal coverage of S2/MSI along with Landsat-8 Operational Land Imager L8/OLI and recently launched Landsat-9 L9/OLI-2 enables more frequent monitoring of Earth’s inland water bodies and permits routine mapping of water quality parameters.

To utilize these capabilities, we have developed field-validated methods and implemented S2/MSI and L8/OLI image processing techniques in an automated pipeline built in a high-performance computing environment that generates Level-3 (L-3) satellite data products for lake water quality monitoring and management. Machine-to-machine access to ESA Copernicus and U.S. Geological Survey servers allows for the synergistic acquisition of L-1 S2/MSI and L8/OLI imagery to supply the demand for near-real time data. Newly acquired imagery can be immediately sent through multiple scripted processing modules, which include (1) identifying and omitting potentially contaminated pixels caused by clouds, cloud shadow, atmospheric haze, wildfire smoke and specular reflection, and (2) classification of water pixels through a normalized difference water index (nNDWI) to delineate a scene specific water mask. The combined masks result in qualified pixels which advance to (3) a modified SWIR-based aerosol atmospheric correction to the retrieval of remote sensing reflectances (Rrs). The atmospheric correction produces a harmonized reflectance product between S2/MSI and L8/OLI pixels in which modeled L-3 type water quality data products are derived. Calibrated L-3 water quality models including water clarity, CDOM, and chlorophyll-a, rely heavily on field validated datasets to account for the dynamics of optically complex lake systems of the region. To this extent, sampling efforts in the summer months constrain uncertainties between satellite-derived and surface water properties caused by varying atmospheric conditions and calibrate/validate water quality retrieval algorithms to yield verifiable water products. As new field validation data become available at season-end, scripted modules within the processing chain can be modified accordingly and applied to incoming and previously processed imagery if any resulting water quality product models need improvement. Finally, the data can be made available to the public in an online map viewer linked to a spatial database that allow for statistical summaries at different delineations and time windows, temporal analysis and visualization of water quality variables. The Minnesota LakeBrowser (https://lakes.rs.umn.edu/) provides an example of the data that is being produced through this project. Due to the cloud cover in the Midwest, we determined that monthly open water (May through October) pixel level mosaics work best for statewide coverage. Lake level data is determined for each clear image occurrence and compiled in csv files that can be used to calculate water quality variables for different timeframes (e.g. monthly, summer (June-Sept)) and linked to a lake polygon layer that can be used for geospatial analysis and included in a web map interface. For Minnesota the lake level (2017-2020) data includes 603,678 daily lake measurements of chlorophyll, clarity and CDOM (1,811,034 total) and will be updated on a regular basis.

This unique data source dramatically improve data-driven resource management decisions and will help inform agencies about evolving water quality conditions statewide. In terms of decision-making, the production of frequent, near-real time data on water clarity, chlorophyll-a, and CDOM across large regions can enable water quality and fisheries managers to better understand lake ecosystems. The improved understanding will yield societal benefits by helping managers identify the most effective strategies to protect water quality and improve models for increased fisheries production.