Fine-resolution estimation for urban surface water pollution susceptibility with multi-modal earth observation data

• Published in: Environmental research letters 2024-06, Vol.19 (6), p.64026
• Main Authors: Shao, Mengmeng, Xie, Xiao, E, Chao, Li, Jingzhong, Ren, Wanxia
• Format: Article
• Language: English
• Subjects: Classification; Computational efficiency; Computing time; Datasets; Deep learning; Field investigations; Field tests; High resolution; Image resolution; Land cover; Land pollution; Land use; multi-modal earth observation data; Remote sensing; Spatial discrimination; Spatial resolution; Surface water; surface water pollution susceptibility; Urban areas; Urban populations; very-high-resolution UAV image; Water pollution; Water quality; Water resources; Water supply
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/ad4a92

The sustainability and suitability of water resources are of great importance for maintaining urban populations. The landscapes and environment around urban waters have always been the main focus of maintaining water quality for sustainable water supplies. Early-stage field investigations recognized the influence of land use/land cover (LULC) on water quality. To extend the research scope in spatial and temporal dimensions, remote sensing techniques have been utilized to discover the relationships between LULC and water quality. However, these remote sensing datasets generally had a medium spatial resolution, making them unable to support the fine-detailed land classifications that are critical to explore the water quality in an urban area. Moreover, although more details regarding the land surface are available from the currently-generated high-resolution and very-high-resolution remote sensing images, this land surface information is too complex for the state-of-the-art deep learning approaches and benchmark datasets. This manuscript reports our efforts on developing a framework to explore the fine-resolution relationship between surface water pollution and LULC. To address the cost of computing time and limitations of well-labelled datasets, we employ a foundation model-enhanced approach for water extraction and water-surrounded LULC classification. We propose an estimator of surface water pollution susceptibility to main pollutants based on the surrounding LULCs. Selecting the Future City of Beijing as the study area, based on very-high-resolution remote sensing images, the experiment proved that our proposed approach could effectively map the susceptibility of surface water pollution caused by its surrounding land use and land cover. To our knowledge, the relationship of LULCs and water quality have not been investigated using 0.5 m spatial resolution data. We hope our work can provide a prospective fine-detailed water quality analysis in the community of water environment of remote sensing.