Augmenting estuary monitoring from space: New retrievals of fine-scale CDOM quality and DOC exchange

Details

• Journal Title:
  Journal of Applied Earth Observation and Geoinformation
• Personal Author:
  Menendez, Alana ; Tzortziou, Maria
• NOAA Program & Office:
  CESSRST (Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies)
• Sea Grant Program:
  CONN (Connecticut Sea Grant) ; NYSGI/NYEXT (New York Sea Grant)
• Description:
  Fueled by both terrestrial and marine inputs, estuaries worldwide are important biogeochemical reactors, strongly susceptible to natural and anthropogenic, episodic, or compounding disturbances. Satellite sensors provide a unique vantage point to capture estuarine processes at scales not feasible with in situ sampling alone; yet, remote sensing retrievals of estuarine biogeochemical dynamics remain challenging. Here, we developed new algorithms for high spatial resolution satellite retrievals of colored dissolved organic matter (CDOM) and dissolved organic carbon (DOC)—two key indicators of estuarine water quality and biogeochemical state. CDOM algorithms were optimized for Long Island Sound—one of the world’s most heavily urbanized estuaries—and included retrievals of CDOM absorption at 300 nm (aCDOM(300)), a proxy for CDOM amount, as well as absorption spectral slope in the 275–295 nm range (S275-295), a proxy for CDOM quality. Algorithms were specifically designed for Sentinel-2A/2B MSI and Landsat-8/9 OLI, a constellation of high spatial resolution sensors that provides coverage of the most dynamic zones for estuary exchange. MSI and OLI aCDOM(300) and S275-295 were most successfully retrieved using machine learning (ML) random forest regression, with the input features of remote sensing reflectance bands, band ratios, and month of acquisition. Satellite retrieval of DOC concentrations relied on a tight, widely applicable relationship between aCDOM(300) and S275-295. Timeseries generated for Long Island Sound, and its most impaired water quality region in the Western Narrows, revealed strong CDOM spatiotemporal dynamics associated with seasonal freshwater discharge, tidal wetland carbon export, recurring wastewater pollution, and episodic extreme events. Results highlight the value of these new ecosystem-scale observations for enhanced, sustainable estuarine management.
• Keywords:
  Estuaries Machine Learning Machine Learning Episodic Events Machine Learning Tidal Exchanges Wastewater
• Source:
  International Journal of Applied Earth Observation and Geoinformation 137 (2025) 104389
• DOI:
  https://doi.org/10.1016/j.jag.2025.104389
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant No. NA22SEC4810016
• License:
  https://creativecommons.org/licenses/by-nc-nd/4.0/
• Rights Information:
  CC BY-NC-ND
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:7a2bf4584cbd6287152447a4a6c79e91e014649c480085424ffed2811a9ddcb19bcd0991769a957182c87b52910e0dc11e03656c12c94dfb6065ca65304c908b
• Download URL:
  https://repository.library.noaa.gov/view/noaa/72890/noaa_72890_DS1.pdf
• File Type:
  [PDF - 14.13 MB ]