LakeSense is a data processing platform designed to observe lake water clarity, turbidity, and chlorophyll over time using public satellite imagery (Sentinel 2A-B and Landsat 8).

The pipeline automatizes the processing of the satellite imagery including: (i) imagery acquisition, (ii) atmospheric corrections of the imagery using algorithms designed for aquatic remote sensing and correcting for adjacency effects, and (iii) computation of lake water quality indicators using published and machine-learning algorithms.

The research team developed and calibrated LakeSense using a nationwide sample of 102 lakes with rich in-situ lake water quality data from US-EPA’s Water Quality Exchange. The lakes were purposefully selected to represent a wide variety of lakes in CONUS (lake size, climate, region, water quality conditions and trends).

LakeSense is developed at the Department of Earth and Environment at Boston University in collaboration the U.S. Environmental Protection Agency (National Center for Environmental Economics) with grant support from NASA’s Applied Sciences program (Water Resources Applications #80NSSC22K0919, 2023-2026).

LakeSense has also supported a NASA DEVELOP project to monitor quality in five North Dakota recreational lakes with North Dakota’s Department of Environmental Quality.

Project team

  • Sachini Ranasinghe (Boston University, PhD candidate)
  • Christoph Nolte (Boston University, PI)
  • Cédric Fichot (Boston University, Co-I)
  • Alice Ni (Boston University, research fellow)
  • Chris Moore (US-EPA, Collaborator)
  • Kevin Boyle (Virginia Tech, Collaborator)
  • Mark Friedl (Boston University, Collaborator)
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