Chlorophyll-a in the Chesapeake Bay estimated by extra-trees machine learning modeling

Details

• Journal Title:
  Remote Sensing
• Personal Author:
  Nezlin, Nikolay P. ; Son, SeungHyun ; Salem, Salem I. ; Ondrusek, Michael E.
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CISESS (Cooperative Institute for Satellite Earth System Studies)
• Description:
  Monitoring chlorophyll-a concentration (Chl-a) is essential for assessing aquatic ecosystem health, yet its retrieval using remote sensing remains challenging in turbid coastal waters because of the intricate optical characteristics of these environments. Elevated levels of colored (chromophoric) dissolved organic matter (CDOM) and suspended sediments (aka total suspended solids, TSS) interfere with satellite-based Chl-a estimates, necessitating alternative approaches. One potential solution is machine learning, indirectly including non-Chl-a signals into the models. In this research, we develop machine learning models to predict Chl-a concentrations in the Chesapeake Bay, one of the largest estuaries on North America’s East Coast. Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. Using the entire ocean color datasets acquired by the satellite sensors MODIS-Aqua (>20 years) and VIIRS-SNPP (>10 years), we generated long-term Chl-a estimates covering the entire Chesapeake Bay area. The models achieve a multiplicative absolute error of approximately 1.40, demonstrating reliable performance. The predicted spatiotemporal Chl-a patterns align with known ecological processes in the Chesapeake Bay, particularly those influenced by riverine inputs and seasonal variability. This research emphasizes the potential of machine learning to enhance satellite-based water quality monitoring in optically complex coastal waters, providing valuable insights for ecosystem management and conservation.
• Keywords:
  Chlorophyll Machine Learning Remote Sensing Machine Learning Remote Sensing Chlorophyll-a (Chl-a) Extra-Trees
• Source:
  Remote Sens. 2025, 17(13), 2151
• DOI:
  https://doi.org/10.3390/rs17132151
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant no. NA24NESX432C0001
• Place as Subject:
  Chesapeake Bay (Md. and Va.)
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:cbb71514f67f9400ef057758df8a186df93103da42ddd16fe061f974895010dcb32c8669a6bf6d8cf33401f9908b73c3c0ce77f2c1aa3bef09733cfd15a1b898
• Download URL:
  https://repository.library.noaa.gov/view/noaa/73459/noaa_73459_DS1.pdf
• File Type:
  [PDF - 3.44 MB ]