Classification-based inversion of suspended sediment concentration using in-situ hyperspectral data in Qingshui River Basin of Huanghe River

Zhiru JIN; Xinyue LIU; Qianguo XING; Weichen ZHANG; Xiao LIU; Xing MING

doi:10.1007/s00343-026-5442-8

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Classification-based inversion of suspended sediment concentration using in-situ hyperspectral data in Qingshui River Basin of Huanghe River

Updated：2026-08-04

- Classification-based inversion of suspended sediment concentration using in-situ hyperspectral data in Qingshui River Basin of Huanghe River
- Journal of Oceanology and Limnology Pages: 1-17(2026)
- Affiliations：
  
  1.College of Physical Science and Technology, Hebei University, Baoding 071002, China
  2.CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
  3.Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China
  4.University of Chinese Academy of Sciences, Beijing 100049, China
- Author bio：
  
  mingxing@hbu.edu.cn
- Funds：
- DOI：10.1007/s00343-026-5442-8
  CLC：
- Received：11 November 2025，
  
  Online First：04 August 2026，
- Accepted：
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JIN Zhiru,LIU Xinyue,XING Qianguo,et al.Classification-based inversion of suspended sediment concentration using in-situ hyperspectral data in Qingshui River Basin of Huanghe River[J].Journal of Oceanology and Limnology,
DOI：

JIN Zhiru,LIU Xinyue,XING Qianguo,et al.Classification-based inversion of suspended sediment concentration using in-situ hyperspectral data in Qingshui River Basin of Huanghe River[J].Journal of Oceanology and Limnology, DOI：.

摘要

Abstract

The Qingshui River Basin is the largest tributary entering the Huanghe (Yellow) River within Ningxia of China

contributing about 49% of the sediment load from this section. In this basin

the suspended sediment concentration (SSC) exhibits a short-term variability with values rising by orders of magnitude within hours. This phenomenon is primarily driven by the precipitation-induced runoff

which induces the surface soil erosion

sediment transport and the local bed erosion. Such rapid fluctuations of SSC are difficult to be captured using manual sampling or satellite remote sensing methods. To address this issue

a classification-based inversion model was developed in this paper using the ground-based in-situ hyperspectral data from the Wangtuan station beside Qingshui River between March and September 2024

covering both normal and flood periods. At the beginning

we utilized the K-means clustering algorithm to classify these water spectral characteristics into three types. These water types exhibited different spectral characteristics in the position and number of their reflectance peaks. Subsequently we built empirical and machine learning models for each water type to retrieve SSC. By comparison to Global models

the classification-based inversion models substantially improved inversion performance: a linear empirical model performed the best for low SSC water type (

=0.92)

while support vector regression and random forest achieved a

of 0.92 under medium and high SSC conditions

respectively. Therefore

this study we conducted in this paper achieved a high inversion accuracy of SSC especially for medium and high SSC water types. Our results revealed that the ‘classify first

then model’ method is reliable for estimating SSC of high-sediment water bodies in the Qingshui River Basin

and enhances the feasibility of ground-based in-situ hyperspectral monitoring technology for refined Huanghe River watershed management.

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