Enhanced Estimate of Chromophoric Dissolved Organic Matter Using Machine Learning Algorithms from Landsat-8 OLI Data in the Pearl River Estuary
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.
i

Enhanced Estimate of Chromophoric Dissolved Organic Matter Using Machine Learning Algorithms from Landsat-8 OLI Data in the Pearl River Estuary

Filetype[PDF-5.72 MB]


Select the Download button to view the document
This document is over 5mb in size and cannot be previewed
Details Supporting Files You May Also Like

Details:

  • Journal Title:
    Remote Sensing
  • Personal Author:
  • NOAA Program & Office:
    CIMAR (Cooperative Institute for Marine and Atmospheric Research)
  • Description:
    Chromophoric Dissolved Organic Matter (CDOM) plays a critical role in the carbon and biogeochemical cycles within aquatic ecosystems. Satellite imagery can be employed to determine aquatic CDOM concentrations, highlighting the need for effective and precise algorithms for this task. In this study, a cruise survey dataset containing CDOM absorption coefficients and water-leaving radiances in the Pearl River estuary (PRE) was utilized to develop machine learning algorithms for CDOM retrieval from Landsat-8 Operational Land Imager (OLI) observations. Based on OLI wavelength bands, five bands and six band-ratios were chosen as input parameters for the machine learning models. Six machine learning models were trained to develop CDOM algorithms, including Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The results indicated that, among the six machine learning models, the XGBoost algorithm performed best, with the highest R2 value of 0.9 and the lowest CDOM root mean square error (RMSE) of 0.37 m−1, outperforming empirical algorithms. The XGBoost algorithm identified B4/B1 as the most critical input parameter, contributing 71%, followed by B3/B2 with a 16% contribution, where B1, B2, B3, and B4 are the wavelength bands of the OLI. These two band-ratios accounted for most of the contributions, suggesting their significant role in CDOM retrieval from Landsat OLI images. By employing the developed XGBoost algorithm, CDOM spatial patterns at six instances were derived from Landsat-8 OLI image reflectance, illustrating CDOM variations in the PRE influenced by various factors. Further analysis revealed that, in the PRE, tides and winds are the primary driving forces behind the spatial and temporal variability of CDOM. At present, the exploration of employing machine learning algorithms to infer CDOM concentrations in this region remains relatively limited; therefore, with a higher R2 value, the machine learning model we established unveils fresh and novel results.
  • Keywords:
  • Source:
    Remote Sensing, 15(8), 1963
  • DOI:
  • ISSN:
    2072-4292
  • Format:
    PDF
  • Publisher:
    MDPI AG
  • Document Type:
    Journal Article
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.27.1