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A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using

Hongwei Guo1, Jinhui Jeanne Huang1, Xiaotong Zhu1

  • 1College of Environmental Science and Engineering / Sino-Canada Joint R&D Centre for Water and Environmental Safety, Nankai University, Tianjin, 300350, China.

Environmental Pollution (Barking, Essex : 1987)
|July 11, 2021
PubMed
Summary
This summary is machine-generated.

Remote sensing can now estimate dissolved oxygen (DO) using Support Vector Regression models with Landsat and MODIS data. This method successfully mapped long-term DO changes in Lake Huron, revealing significant oxygen loss influenced by climate factors.

Keywords:
Dissolved oxygenLandsatMODISMachine learningRemote sensing

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Water Quality Monitoring

Background:

  • Dissolved oxygen (DO) is a key water quality indicator, but its estimation via remote sensing is challenging due to its non-optically active nature.
  • Existing research on multi-scale DO estimation using satellite data is limited, hindering long-term trend analysis.

Purpose of the Study:

  • To develop and validate robust remote sensing models for estimating dissolved oxygen (DO) across multiple spatial and temporal scales.
  • To reconstruct historical DO variability in Lake Huron and analyze the influence of climate factors on DO trends.

Main Methods:

  • Support Vector Regression (SVR) models were developed using remote sensing reflectance from Landsat and MODIS, alongside in-situ DO measurements and water temperature data.
  • The models were validated using data from Lake Huron and three inland water bodies.
  • Spatial distributions of DO variability were reconstructed, and the impact of climate factors on long-term DO trends was analyzed.

Main Results:

  • The developed SVR models demonstrated high accuracy and generalization (average R² = 0.91), outperforming Random Forest and Multiple Linear Regression.
  • Monthly DO estimates from Landsat and MODIS data showed high consistency (average R² = 0.88).
  • Lake Huron experienced a 6.56% oxygen loss from 1984 to 2019, with air temperature, solar radiation, and precipitation identified as key influencing climate factors.

Conclusions:

  • This study successfully demonstrates the capability of SVR-based models using Landsat and MODIS data for long-term DO retrieval at multiple scales.
  • The findings highlight the potential for improved water quality monitoring and climate change impact assessment in large water bodies.
  • Further improvements in model robustness can be achieved by incorporating water temperature and expanding training datasets to encompass a wider range of DO conditions.