Satellite retrievals of total phosphorus in Taihu Lake using Sentinel-2 images and an optimized XGBoost model

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators Pub Date : 2025-05-06 DOI:10.1016/j.ecolind.2025.113563

Jiayu Cui , Shiqiang Wu , Jiangyu Dai , Wanyun Xue , Yue Zhang , Jiayi You , Xueyan Lv , Xuan Yang

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引用次数: 0

Abstract

Satellite remote sensing technology holds significant potential for water quality monitoring due to its high spatio-temporal resolution and long-term observational capabilities. However, developing satellite-retrieved models for non-optical active parameters such as total phosphorus remains challenging. In this study, we utilized measured total phosphorus data from 17 floating boat stations in Taihu Lake and Sentinel-2 satellite imagery to construct an XGBoost model based on Bayesian optimization and feature selection for the retrieval of total phosphorus levels in the lake. The model employed single bands and band ratios at various resolutions as input features, optimized model parameters through Bayesian optimization, and incrementally introduced variables with substantial contributions to evaluate and determine the optimal feature combination. Results indicated that band ratios at different resolutions constituted 79 % of the optimal feature combination, demonstrating significantly better performance than single bands. The coefficient of determination (R²) was 0.7151, and the root mean square error (RMSE) was 0.0179 mg/L. Compared to using all feature variables, the R² increased by 35 %, RMSE decreased by 11.4 %, and the accuracy rate of total phosphorus retrieval concentration reached 81.7 %. The spatial distribution of total phosphorus derived from the satellite-retrieved model aligned well with the actual conditions. The optimal band combination model exhibited high stability and generalization ability. This study provides a practical reference for constructing a satellite retrieval model to determine inactive water quality parameters, and also provides strong technical support for the construction of an air-space-ground integrated monitoring network, especially in the context of open sharing of satellite data, and offers a feasible path for realizing large-scale and low-cost water quality supervision.

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基于Sentinel-2图像和优化XGBoost模型的太湖总磷卫星反演

卫星遥感技术由于其高时空分辨率和长期观测能力，在水质监测方面具有巨大的潜力。然而，开发非光学活性参数（如总磷）的卫星检索模型仍然具有挑战性。本研究利用太湖17个浮船站实测总磷数据和Sentinel-2卫星影像，构建了基于贝叶斯优化和特征选择的湖泊总磷反演XGBoost模型。该模型采用不同分辨率下的单波段和波段比作为输入特征，通过贝叶斯优化优化模型参数，并逐步引入有较大贡献的变量来评估和确定最优特征组合。结果表明，不同分辨率下的波段比占最优特征组合的79%，显著优于单波段。测定系数（R2）为0.7151，均方根误差（RMSE）为0.0179 mg/L。与使用所有特征变量相比，R2提高了35%，RMSE降低了11.4%，总磷检索浓度的准确率达到81.7%。卫星反演模型得到的全磷空间分布与实际情况吻合较好。最优波段组合模型具有较高的稳定性和泛化能力。本研究为构建卫星反演模型确定非活动水质参数提供了实用参考，也为构建空、地一体化监测网，特别是在卫星数据开放共享的背景下提供了强有力的技术支撑，为实现大规模、低成本的水质监测提供了可行路径。

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来源期刊

Ecological Indicators 环境科学-环境科学

CiteScore

11.80

自引率

8.70%

发文量

1163

审稿时长

78 days

期刊介绍： The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published. • All aspects of ecological and environmental indicators and indices. • New indicators, and new approaches and methods for indicator development, testing and use. • Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources. • Analysis and research of resource, system- and scale-specific indicators. • Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs. • How research indicators can be transformed into direct application for management purposes. • Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators. • Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.