Retrieval of Dissolved Organic Carbon Storage in Plateau Lakes Based on Remote Sensing and Analysis of Driving Factors: A Case Study of Lake Dianchi
Dissolved organic carbon (DOC) is an essential form of carbon in lakes and has significant impact on thermal structure and carbon source-supporting food webs. Current remote sensing studies on DOC mainly focus on the retrieval of surface concentration of lakes, with limited understanding of three-di...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/10/1791 |
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| Summary: | Dissolved organic carbon (DOC) is an essential form of carbon in lakes and has significant impact on thermal structure and carbon source-supporting food webs. Current remote sensing studies on DOC mainly focus on the retrieval of surface concentration of lakes, with limited understanding of three-dimensional carbon storage. This study proposes a novel vertical retrieval methodology for plateau lakes by integrating remote sensing and vertical profile analysis. Specifically, a Gaussian function-based vertical fitting model was developed to characterize DOC concentration distribution along water columns, where parameters (μ and σ) were calibrated against surface DOC concentrations retrieved from MODIS reflectance. A result-oriented storage algorithm was established by linking surface DOC concentration to DOC storage through linear relationships (R<sup>2</sup> > 0.9), with slope and intercept functions optimized as depth-dependent equations. The mixed-layer depth (2 m) was determined through error minimization analysis of 16 vertical profiles. Applied to the eutrophic Lake Dianchi, results show significant vertical DOC variations (CV up to 101.4%) but consistent distribution patterns across profiles. Spatially, higher DOC storage occurred in central regions (80–120 g·m<sup>−2</sup>) with seasonal peaks in summer and autumn. Interannual analysis reveals wind speed and forest coverage as dominant drivers, while monthly variations correlate strongly with water temperature. This methodology advances real-time monitoring of carbon storage in deep plateau lakes, providing critical insights into lacustrine carbon cycling. |
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| ISSN: | 2072-4292 |