LakeSense: a portal for nationwide remotely sensed lake water quality indicators to inform benefit-cost analyses for federal and state-level rulemaking (NASA)

Funded by: NASA Applied Sciences: Water Resources Applications (#80NSSC22K0919)

This project will develop the prototype of LakeSense: an automated data pipeline that derives high-quality time series of lake water quality indicators (LWQI) from remote sensing data for lakes ≥5 hectares in the contiguous United States (CONUS) and that makes the data publicly accessible through an interactive web portal. Reliable, consistent, temporally dense, and nationally representative LWQI time series are necessary to close known information gaps in decision support tools developed by the U.S. Environmental Protection Agency (US-EPA) to support federal and state-level water resource rulemaking. Existing tools that (i) predict policy-induced changes in water quality and (ii) estimate the costs and benefits of such changes at regional and national scales currently do not explicitly represent lake-specific water quality processes and benefits. The development of reliable and nationally representative models for lake water quality prediction and valuation is currently severely constrained by the absence of LWQI datasets that are reliable, consistent, temporally dense, and nationally representative.

LakeSense will combine imagery from Sentinel 2A-B, Landsat 8, and the ECOSTRESS mission to develop high-quality time series of LWQI that are remotely observable and known to be valued by lake users: water clarity, turbidity, color, algal blooms, and temperature. The LakeSense pipeline will automatize the processing of the imagery using an adaptive procedure that involves: (i) imagery acquisition, (ii) optimal atmospheric correction of the imagery using state-of-the-art methods for aquatic systems to retrieve lake-water remote-sensing reflectance spectra, and (iii) the implementation of reliable algorithms to retrieve LWQI from these spectra. We will develop and calibrate the adaptive procedure based on a nationwide sample of 100-120 lakes with rich in-situ LWQI data that are purposefully selected to represent the wide variety of lakes in CONUS (lake size, climate, region, water quality conditions and trends). Upon successful calibration, the LakeSense prototype will be deployed to generate remotely sensed LWQI (RS-LWQI) time series for all resolvable lakes ≥5 hectares in two pilot regions with large numbers of lakes, lake users, and in-situ measurements (Minnesota-Wisconsin, Florida-Georgia-South Carolina) to prove scalability and facilitate data scrutiny. The resulting data will be made available via an interactive web portal that allows users to observe lake-specific conditions and trends and to download data in bulk.

To facilitate integration into decision making, the project team will conduct hedonic valuation studies to (i) establish whether RS-LWQI time series generated by LakeSense are a reliable substitute for in-situ LWQI data in water quality valuation, and to (ii) establish whether the enhanced spatial and temporal coverage of RS-LQWI data enhances the transferability of estimated relationships between changes in lake water quality and property prices across diverse settings. In collaboration with partners at US-EPA’s National Center for Environmental Economics, we will develop the prototype of a tool to estimate effects of (observed and simulated) changes to lake water quality on property values, laying the foundation for an operational integration of RS-LWQI data into US-EPA’s valuation and decision support tools.

Project Team

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