Remote quantification of the trophic status of Chinese lakes
<p>Assessing eutrophication in lakes is of key importance, as this parameter constitutes a major aquatic ecosystem integrity indicator. The trophic state index (TSI), which is widely used to quantify eutrophication, is a universal paradigm in the scientific literature. In this study, a methodo...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-10-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/27/3581/2023/hess-27-3581-2023.pdf |
| Summary: | <p>Assessing eutrophication in lakes is of key importance, as this
parameter constitutes a major aquatic ecosystem integrity indicator. The
trophic state index (TSI), which is widely used to quantify eutrophication, is
a universal paradigm in the scientific literature. In this study, a
methodological framework is proposed for quantifying and mapping TSI using the
Sentinel Multispectral Imager sensor and fieldwork samples. The first step
of the methodology involves the implementation of stepwise multiple
regression analysis of the available TSI dataset to find some band ratios, such
as <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi mathvariant="normal">blue</mi><mo>/</mo><mi mathvariant="normal">red</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="44pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="9fec3a3ee81679f5b8317ce2b1de0660"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hess-27-3581-2023-ie00001.svg" width="44pt" height="14pt" src="hess-27-3581-2023-ie00001.png"/></svg:svg></span></span>, <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M2" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi mathvariant="normal">green</mi><mo>/</mo><mi mathvariant="normal">red</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="96cd85bbd34db5e253b9419a83cc490d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hess-27-3581-2023-ie00002.svg" width="52pt" height="14pt" src="hess-27-3581-2023-ie00002.png"/></svg:svg></span></span> and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi mathvariant="normal">red</mi><mo>/</mo><mi mathvariant="normal">red</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="40pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="35fa873a6dd9ead1a77828c10cb848cf"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hess-27-3581-2023-ie00003.svg" width="40pt" height="14pt" src="hess-27-3581-2023-ie00003.png"/></svg:svg></span></span>, which are sensitive to lake TSI. Trained
with in situ measured TSI and match-up Sentinel images, we established the
XGBoost of machine learning approaches to estimate TSI, with good agreement
(<span class="inline-formula"><i>R</i><sup>2</sup>=</span> 0.87, slope <span class="inline-formula">=</span> 0.85) and fewer errors (MAE <span class="inline-formula">=</span> 3.15 and
RMSE <span class="inline-formula">=</span> 4.11). Additionally, we discussed the transferability and
applications of XGBoost in three lake classifications: water quality,
absorption contribution and reflectance spectra types. We selected XGBoost to map TSI in 2019–2020 with good-quality Sentinel-2 Level-1C images
embedded in the ESA to examine the spatiotemporal variations of the lake trophic
state. In a large-scale observation, 10 m TSI products from 555 lakes in China facing eutrophication and unbalanced spatial patterns
associated with lake basin characteristics, climate and anthropogenic
activities were investigated. The methodological framework proposed herein could serve as a
useful resource for continuous, long-term and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource
management.</p> |
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| ISSN: | 1027-5606 1607-7938 |