College of Surveying and Geo-informatics, Tongji University, 1239 Siping Rd., Shanghai, 200092, China; Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, Milan, 20133, Italy.
College of Surveying and Geo-informatics, Tongji University, 1239 Siping Rd., Shanghai, 200092, China.
Water Res. 2024 Dec 1;267:122544. doi: 10.1016/j.watres.2024.122544. Epub 2024 Sep 29.
Remote sensing water quality monitoring technology can effectively supplement the shortcomings of traditional water quality monitoring methods in spatiotemporal dynamic monitoring capabilities. At present, although the spectral feature-based remote sensing water quality inversion models have achieved many successes, there could still be a problem of insufficient generalization ability in monitoring the water quality of complex river networks in large cities. In this paper, we propose a spectro-environmental factors integrated ensemble learning model for urban river network water quality inversion. We analyzed the correlation between water quality parameters, spectral reflectance, and environmental factors based on an in-situ dataset collected in the northern part of Shanghai. Using the Hot Spot Analysis (Getis-Ord Gi*), we found that river network water quality parameters have different patterns in different urban functional zones. Furthermore, daily average temperature, total rainfall within the seven days, and several band combinations were also selected as the environmental and spectral features using factor analysis and Pearson correlation coefficient analysis. After the feature analysis, the spectro-environmental factors integrated ensemble learning model was trained. Compared with the spectral-based machine learning inversion models, the coefficients of determination R increased by about 0.50. Our model was also tested in three different test areas within and outside the in-situ sampling areas in Shanghai based on low-altitude multispectral remote sensing images. The R results for total phosphorus (TP), ammonia nitrogen (NH-N), and chemical oxygen demand (COD) within the in-situ sampling areas were 0.52, 0.58, and 0.56 respectively. The mean absolute percentage error (MAPE) results were 53.36%, 63.95%, and 22.46% respectively. After adding the area outside the in-situ sampling areas, the R results for TP, NH-N, and COD were 0.47, 0.47, and 0.53. The MAPE were 49.38%, 74.46%, and 20.49%. Our research provided a new remote sensing water quality inversion method to be utilized in complex urban river networks which exhibited solid accuracy and generalization ability.
遥感水质监测技术可以有效地弥补传统水质监测方法在时空动态监测能力上的不足。目前，基于光谱特征的遥感水质反演模型虽然在水质监测方面取得了许多成功，但在监测大城市复杂河网的水质时，仍然存在概括能力不足的问题。在本文中，我们提出了一种基于光谱-环境因子的集成集成学习模型，用于城市河网水质反演。我们基于在上海北部采集的实地数据集，分析了水质参数、光谱反射率与环境因子之间的相关性。利用热点分析（Getis-Ord Gi*），我们发现河网水质参数在不同城市功能区具有不同的分布模式。此外，我们还采用因子分析和皮尔逊相关系数分析，选择日平均温度、七天内总降雨量和几个波段组合作为环境和光谱特征。在特征分析之后，我们训练了基于光谱-环境因子的集成集成学习模型。与基于光谱的机器学习反演模型相比，决定系数 R 增加了约 0.50。我们的模型还基于上海的低空多光谱遥感图像，在现场采样区内外的三个不同试验区进行了测试。在现场采样区内，总磷（TP）、氨氮（NH-N）和化学需氧量（COD）的 R 结果分别为 0.52、0.58 和 0.56。平均绝对百分比误差（MAPE）的结果分别为 53.36%、63.95%和 22.46%。加入现场采样区以外的区域后，TP、NH-N 和 COD 的 R 结果分别为 0.47、0.47 和 0.53。MAPE 分别为 49.38%、74.46%和 20.49%。我们的研究为复杂城市河网的水质遥感反演提供了一种新的方法，具有较高的准确性和概括能力。
