A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data

Bolin Fu; Sunzhe Li; Zhinan Lao; Yingying Wei; Kaishan Song; Tengfang Deng; Yeqiao Wang

doi:10.1016/j.watres.2024.121673

A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data

Water Res. 2024 Apr 24:257:121673. doi: 10.1016/j.watres.2024.121673. Online ahead of print.

Authors

Bolin Fu¹, Sunzhe Li², Zhinan Lao², Yingying Wei², Kaishan Song³, Tengfang Deng², Yeqiao Wang⁴

Affiliations

¹ College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China. Electronic address: fubolin@glut.edu.cn.
² College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
³ Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China.
⁴ Department of Natural Resources Science, University of Rhode Island, Kingston, RI 02881, USA.

PMID: 38688189
DOI: 10.1016/j.watres.2024.121673

Abstract

Wetlands cover only around 6 % of the Earth's land surface, and are recognized as one of the three major ecosystems, alongside forests and oceans. The ecological structure and function of karst wetlands are unique due to the influence of geologic structure. At present, the unclear spectral morphology of surface water in karst wetlands poses a significant challenge in remote sensing estimation of non-optically active water quality parameters (NAWQPs). This study proposed a novel multi-scale spectral morphology feature extraction (MSFE) method to insight to spectral characteristics in surface water of karst wetlands, and further screen the sensitive features of NAWQPs. Then we constructed three remote sensing inversion strategies for NAWQPs (TN, TP, NH₃_N, DO), including direct estimation, indirect estimation, and auxiliary estimation. Finally, we constructed a novel pH-based hierarchical analysis framework (pH_HA) to thoroughly explore the influence of alkalinity-biased characteristics of karst water on the spectral domain of NAWQPs and its estimation accuracy using in-situ hyperspectral data, respectively. We found that the spectral characteristics of karst waters at the first reflectance peak (580 nm) differed significantly from other water body types. The MSFE successfully captured the sensitive spectral domains for NAWQPs, and focused on between 500 and 600 nm and 900-960 nm. The sensitive features captured by MSFE improved estimation accuracy of NAWQPs (R² >0.9). Direct estimation presented more stable performance compared to the auxiliary estimation (average RMSE of 0.366 mg/L), and the auxiliary estimation model further improved the retrieval accuracy of TN compared to direct estimation model (R² increasing from 0.43 to 0.56). The novel hierarchical framework clearly revealed the notable changes in the sensitive spectral domains of NAWQPs under different pH values, and enabled more precise determination of spectral subdomains of NAWQPs, and identified the optimal spectral features. The pH_HA framework effectively improved the estimation accuracy of NAWQPs (R² increased from 0.514 to over 0.9), and the estimation accuracies (R²) of four NAWQPs were all more than 0.9 when the pH value was over 8.5. Our works provide an effective approach for monitoring water quality in karst wetlands.

Keywords: Hierarchical inversion; Hyperspectral data; Karst wetlands; Machine learning; Non-optically active water quality parameters; Spectral properties.