Search

Menu
Search
in Ecology

A hybrid empirical and semi analytical inversion approach for remote sensing estimation of SPM in Ebinur Lake China

131 Views


Abstract

The suspended particulate matter (SPM) is a key parameter influencing the quality of aquatic habitats. SPM directly impacts the habitat of the “soft gold” (Artemia) in Ebinur Lake and is a major contributor to saline dust within its watershed. However, significant variations among inversion models present a challenge for SPM monitoring via remote sensing. Using Ebinur Lake as a case study, this study identify an optimal strategy for SPM monitoring. The findings are as follows: (1) Ebinur Lake water shows complete absorption at 695 nm. (2) ESTARFM achieved a Red-band spatiotemporal fusion R2 ≥0.79, leading to an SPM inversion R2 of 0.75. (3) Compared to the empirical model (R2 ≥ 0.72, RMSE ≤ 78.95 mg/L), the QAA (Quasi-Analytical Algorithm) demonstrated superior adaptability (R2 ≥ 0.74, RMSE ≤ 61.12 mg/L). (4) The red-band reflectance consistency among Landsat sensors reaches R2 ≥ 0.76, with a cross-platform correlation of R2= 0.73 between Landsat 8 and Sentinel 2. (5) The QAA_655(665) model is effective for Landsat 8 and Sentinel 2, whereas the EXPM (the exponential model) is more suitable for Landsat 5/7 and fusion images. This study primarily addresses the challenge of spatiotemporal inadaptability in the long-term SPM inversion process for Ebinur Lake. It can serve as a reference for water color parameter retrieval via remote sensing in globally similar environmental contexts.

Similar content being viewed by others

Exploration of the utilization of a new land degradation index in Lake Ebinur Basin in China

Article
Open access
30 July 2024

High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023

Article
Open access
04 April 2025

Satellite-ground synchronous in-situ dataset of water optical parameters and surface temperature for typical lakes in China

Article
Open access
14 August 2024

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

References

  1. Niu, L. et al. Role of suspended particulate matter for the transport and risks of organic micropollutant mixtures in rivers: A comparison between baseflow and high discharge conditions. Environ. Sci. Technol. 59 (10), 4857–4867 (2025).

    Google Scholar 

  2. Teng, W. et al. High spatial-resolution satellite mapping of suspended particulate matter in global coastal waters using particle composition-adaptive algorithms. Remote Sens. Environ. 323, 114745 (2025a).

    Google Scholar 

  3. Liu, X. & Wang, M. Global daily gap-free ocean color products from multi-satellite measurements. Int. J. Appl. Earth Obs. Geoinf. 108, 102714 (2022).

    Google Scholar 

  4. Feng, Z. et al. Quantitative inversion of suspended particulate matter in ebinur lake, an arid region: Evidence based on radiative transfer mechanism model. J. Hydrology: Reg. Stud. 61, 102714 (2025).

    Google Scholar 

  5. Cao, Z. et al. MODIS observations reveal decrease in lake suspended particulate matter across China over the past two decades. Remote Sens. Environ. 295, 113724 (2023).

    Google Scholar 

  6. Liu, X. et al. Confounding effects of seasonality and anthropogenic river regulation on suspended particulate matter-driven mercury transport to coastal seas. J. Hazard. Mater. 469, 133979 (2024).

    Google Scholar 

  7. Wen, Z. et al. Remote estimates of suspended particulate matter in global lakes using machine learning models. Int. Soil. Water Conserv. Res. 12 (1), 200–216 (2024).

    Google Scholar 

  8. Ma, H. et al. Satellite canopy water content from Sentinel-2, Landsat-8 and MODIS: Principle, algorithm and assessment. Remote Sens. Environ. 326, 114801 (2025a).

    Google Scholar 

  9. Sagan, V. et al. Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth Sci. Rev. 205, 103187 (2020).

    Google Scholar 

  10. Teng, X. et al. Photochemical transformation and interaction of octachlorodibenzofuran (OCDF) with microplastics in suspended particulate matter-water system. Water Res. https://doi.org/10.1016/j.watres.2025.123766 (2025).

    Google Scholar 

  11. Shi, K. et al. Long-term remote monitoring of total suspended matter concentration in lake Taihu using 250 m MODIS-aqua data. Remote Sens. Environ. 164, 43–56 (2015).

    Google Scholar 

  12. Mohammadpour, G. & Pirasteh, S. Interference of CDOM in remote sensing of suspended particulate matter (SPM) based on MODIS in the Persian Gulf and Oman sea. Mar. Pollut. Bull. 173, 113104 (2021).

    Google Scholar 

  13. Liu, C. et al. Controlled and driving mechanism of the SPM variation of shallow brackish lakes in arid regions. Sci. Total Environ. 878, 163127 (2023a).

    Google Scholar 

  14. Li, Z. et al. High spatial resolution inversion of chromophoric dissolved organic matter (CDOM) concentrations in Ebinur Lake of arid Xinjiang, China: Implications for surface water quality monitoring. Int. J. Appl. Earth Obs. Geoinf. 132, 104022 (2024).

    Google Scholar 

  15. Peterson, K. T., Sagan, V. & Sloan, J. J. Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GISci. Remote Sens. 57(4), 510–525 (2020).

    Google Scholar 

  16. Qiu, Z. et al. Improving the observations of suspended sediment concentrations in rivers from Landsat to Sentinel-2 imagery. Int. J. Appl. Earth Obs. Geoinf. 134, 104209 (2024).

    Google Scholar 

  17. Moradi, M., Arabi, B., Hommersom, A., van der Molen, J. & Samimi, C. Quality control tests for automated above-water hyperspectral measurements: Radiative transfer assessment. ISPRS J. Photogramm. Remote Sens. 215, 292–312 (2024).

    Google Scholar 

  18. Harringmeyer, J. P. et al. A hyperspectral view of the nearshore Mississippi River Delta: Characterizing suspended particles in coastal wetlands using imaging spectroscopy. Remote Sens. Environ. 301, 113943 (2024).

    Google Scholar 

  19. Cui, M. et al. Summer patterns of global lake total suspended solids under climate-hydrology-topography forcing. Water Res. 291, 125274 (2026).

    Google Scholar 

  20. Noori, A., Mohajeri, M. H., Mehraein, M. & Sharafati, A. Lake total suspended matter retrieval by wind speed: A machine learning model trained by time-series satellite imagery. Ecol. Inform. 81, 102565 (2024).

    Google Scholar 

  21. Tavora, J. et al. Recipes for the derivation of water quality parameters using the high-spatial-resolution data from sensors on board sentinel-2A, sentinel-2B, Landsat-5, Landsat-7, Landsat-8, and Landsat-9 satellites. J. Remote Sens. 3, 0049 (2023).

    Google Scholar 

  22. Duan, P. et al. High-resolution planetscope imagery and machine learning for estimating suspended particulate matter in the Ebinur Lake, Xinjiang, China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 1019–1032 (2022).

    Google Scholar 

  23. Cao, N. et al. Estimation of dissolved organic carbon using Sentinel-2 in the eutrophic Ebinur Lake, China. Remote Sens. 16(2), 252 (2024).

    Google Scholar 

  24. Renosh, P. R. et al. Construction of multi-year time-series profiles of suspended particulate inorganic matter concentrations using machine learning approach. Remote Sensing 9(12), 1320 (2017).

    Google Scholar 

  25. Yan, N., Qiu, Z., Zhang, C., Liu, J. & Liu, D. Observing water turbidity in Chinese rivers using Landsat series data over the past 40 years. J. Clean. Prod. 494, 145001 (2025).

    Google Scholar 

  26. Li, J., Chen, X., Tian, L., Huang, J. & Feng, L. Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and spatial considerations. ISPRS J. Photogramm. Remote Sens. 106, 145–156 (2015).

    Google Scholar 

  27. Petit, T. et al. Inherent optical properties and optical characteristics of dissolved organic and particulate matter in an Arctic fjord (Storfjorden, Svalbard) in early summer. Ocean Sci. Discuss. 2021, 1–28 (2021).

    Google Scholar 

  28. Liu, C. et al. Feasibility of the spatiotemporal fusion model in monitoring Ebinur Lake’s suspended particulate matter under the missing-data scenario. Remote Sensing 13(19), 3952 (2021).

    Google Scholar 

  29. Liu, C. et al. High spatiotemporal resolution reconstruction of suspended particulate matter concentration in arid Brackish lake, China. J. Clean. Prod. 414, 137673 (2023).

    Google Scholar 

  30. Wang, J. et al. Satellite observations of suspended particulate matter concentration in Lake Gaoyou in the past four decades. Water Res. 254, 121442 (2024).

    Google Scholar 

  31. Feng, Z. et al. Quantitative inversion of suspended particulate matter in Ebinur lake, an arid region: Evidence based on radiative transfer mechanism model. J. Hydrol. Reg. Stud. 61, 102714 (2025).

    Google Scholar 

  32. Li, X., He, X. & Pan, X. Application of gaofen-6 images in the downscaling of land surface temperatures. Remote Sens. 14(10), 2307 (2022).

    Google Scholar 

  33. Qing, L. I. N. & Wenqiang, X. U. Analysis of the effect of the quantity of inflow into Ebinur Lake on its ecological security. Environ. Res. 266, 120517 (2025).

    Google Scholar 

  34. Zhao, G. et al. Decoupling of surface water storage from precipitation in global drylands due to anthropogenic activity. Nat. Water 3(1), 80–88 (2025).

    Google Scholar 

  35. Zhong, R., Liu, S., Chen, S., Zhao, L. & Yang, D. Satellite observations reveal anthropogenic pressure significantly affects the suspended particulate matter concentrations in coastal waters of Hainan Island. J. Environ. Manage. 365, 121617 (2024).

    Google Scholar 

  36. Gackstetter, D., Yu, K. & Krner, M. Self-attention and frequency-augmentation for unsupervised domain adaptation in satellite image-based time series classification. ISPRS J. Photogramm. Remote Sens. 224, 113–132 (2025).

    Google Scholar 

  37. Liu, C., Zhang, F., Johnson, V. C., Duan, P. & Kung, H. T. Spatio-temporal variation of oasis landscape pattern in arid area: Human or natural driving?. Ecol. Indic. 125, 107495 (2021).

    Google Scholar 

  38. Cao, N. et al. Spatio-temporal analysis of colored dissolved organic matter over Ebinur Lake in Xinjiang, China. Ecol. Inform. 78, 102339 (2023).

    Google Scholar 

  39. Ju, J. et al. The harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset. Remote Sens. Environ. 324, 114723 (2025).

    Google Scholar 

  40. Ma, J. et al. Projected response of algal blooms in global lakes to future climatic and land use changes: Machine learning approaches. Water Res. 271, 122889 (2025).

    Google Scholar 

  41. Zhu, X., Chen, J., Gao, F., Chen, X. & Masek, J. G. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ. 114(11), 2610–2623 (2010).

    Google Scholar 

  42. Zhang, Y., Qin, B. & Yang, L. Spectral absorption coefficients of particulate matter and chromophoric dissolved organic matter in Meiliang Bay of Lake Taihu. Acta Ecol. Sin. 12, 3969–3979 (2006) ((in chinese)).

    Google Scholar 

  43. Cleveland, J. S. & Weidemann, A. D. Quantifying absorption by aquatic particles: A multiple scattering correction for glass‐fiber filters. Limnol. Oceanogr. 38(6), 1321–1327 (1993).

    Google Scholar 

  44. Liu, Y., Song, Y., Liu, P., Lai, L. & Zhang, Y. Concerns about the temporal matching windows in satellite-ground synchronization for lacustrine environment mapping. Water Res. 286, 124208 (2025).

    Google Scholar 

  45. Pitarch, J. & Vanhellemont, Q. The QAA-RGB: A universal three-band absorption and backscattering retrieval algorithm for high resolution satellite sensors. Development and implementation in ACOLITE. Remote Sens. Environ. 265, 112667 (2021).

    Google Scholar 

  46. Yin, Z. et al. Water clarity changes in Lake Taihu over 36 years based on Landsat TM and OLI observations. Int. J. Appl. Earth Obs. Geoinf. 102, 102457 (2021).

    Google Scholar 

  47. Di Polito, C. et al. On the potential of robust satellite techniques approach for SPM monitoring in coastal waters: Implementation and application over the Basilicata Ionian Coastal Waters using MODIS‐Aqua. Remote Sens. 8(11), 922 (2016).

    Google Scholar 

  48. Liu, D. et al. Observations of water transparency in China’s lakes from space. Int. J. Appl. Earth Obs. Geoinf. 92, 102187 (2020).

    Google Scholar 

  49. Li, J., Chen, X., Tian, L., Huang, J. & Feng, L. Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and Spatial considerations. ISPRS J. Photogramm. Remote Sens.. 106, 145–156 (2015).

    Google Scholar 

  50. Gernez, P. et al. Toward Sentinel-2 high resolution remote sensing of suspended particulate matter in very turbid waters: SPOT4 (Take5) experiment in the Loire and Gironde estuaries. Remote Sens. 7 (8), 9507–9528 (2015).

    Google Scholar 

  51. Odermatt, D., Gitelson, A., Brando, V. E. & Schaepman, M. Review of constituent retrieval in optically deep and complex waters from satellite imagery. Remote Sens. Environ. 118, 116–126 (2012).

    Google Scholar 

  52. Matsushita, B. et al. A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic States in Asian inland waters. ISPRS J. Photogram. Remote Sens. 102, 28–37 (2015).

    Google Scholar 

  53. Cao, Z. et al. Seamless observations of chlorophyll-a from OLCI and VIIRS measurements in inland lakes. Water Res. 270, 122825 (2025).

    Google Scholar 

  54. Coffer, M. M. et al. Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple Spatial scales. Ecol. Ind. 128, 107822 (2021).

    Google Scholar 

  55. He, H. et al. A novel quad-modality deep neural network for estimating chlorophyll-a concentrations in Lianyungang’s lakes and reservoirs using Sentinel-2 MSI data. Water Res. https://doi.org/10.1016/j.watres.2025.124246 (2025).

    Google Scholar 

  56. Wei, J. et al. Global estimation of suspended particulate matter from satellite ocean color imagery. J. Geophys. Res. Oceans 126(8), e2021JC017303 (2021).

    Google Scholar 

  57. Nechad, B., Ruddick, K. G. & Park, Y. Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. Remote Sens. Environ. 4 (114), 854–866 (2010).

    Google Scholar 

  58. Mamun, M., Hasan, M. & An, K. G. Advancing reservoirs water quality parameters Estimation using Sentinel-2 and Landsat-8 satellite data with machine learning approaches. Ecol. Inf. 81, 102608 (2024).

    Google Scholar 

  59. Yan, Y. et al. A two-step random forest algorithm for deriving dissolved inorganic carbon in lakes from Landsat satellite data. Environ. Res. 271, 121068 (2025).

    Google Scholar 

Download references

Funding

We appreciate the anonymous reviewers and editors for appraising our manuscript and offering instructive comments. This research was carried out with support from the Open Fund of Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation, Ministry of Natural Resources (TICIARSN-2023-05), the Radiation transfer simulation of lakes and reservoirs on the northern slope of Tianshan Mountains, time series reconstruction of water color parameters (2024D01A86) and the National Natural Science Foundation of China (42561063), the Estimation of water storage and evaporation of typical lakes in Xinjiang and its impact on regional precipitation (IDM2024002), the Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements (2024KFKT014), and the “Tianchi Talent” Introduction Program of the Xinjiang Uygur Autonomous Region.

Author information

Authors and Affiliations

  1. College of Geographic Sciences and Tourism, Xinjiang Normal University, Urumqi, 830054, China

    Changjiang Liu, Xingbin Xu, Yingxiu Wu & Wenming Zhang

  2. Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation, Ministry of Natural Resources, Nanjing, 210044, China

    Changjiang Liu

  3. Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Urumqi, 830054, China

    Changjiang Liu & Xingbin Xu

  4. College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China

    Fei Zhang

  5. GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, USM, 11800, Penang, Malaysia

    Mou Leong Tan

  6. School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China

    Ye Yuan

  7. Department of Mathematics and Statistics, Chonnam National University, Gwangju, 61186, South Korea

    Ye Yuan

Authors

  1. Changjiang Liu
    View author publications

    Search author on:PubMed Google Scholar

  2. Xingbin Xu
    View author publications

    Search author on:PubMed Google Scholar

  3. Yingxiu Wu
    View author publications

    Search author on:PubMed Google Scholar

  4. Fei Zhang
    View author publications

    Search author on:PubMed Google Scholar

  5. Wenming Zhang
    View author publications

    Search author on:PubMed Google Scholar

  6. Mou Leong Tan
    View author publications

    Search author on:PubMed Google Scholar

  7. Ye Yuan
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Liu. C conceptualized the study, developed the methodology, performed the formal analysis, and wrote the original draft, Xu. X, Zhang. F, and Yuan. Y supervised the research, Wu. Y and Zhang. W conducted the investigation and data curation, Tan M. L. contributed to the investigation and reviewed the manuscript. All authors reviewed and approved the final version of the manuscript.

Corresponding authors

Correspondence to
Xingbin Xu or Ye Yuan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Liu, C., Xu, X., Wu, Y. et al. A hybrid empirical and semi analytical inversion approach for remote sensing estimation of SPM in Ebinur Lake China.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-40250-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-026-40250-x

Keywords

  • Lake
  • High-turbidity water
  • Model selection strategy
  • Data fusion
  • Atmospheric correction

Source: Ecology - nature.com

Author Correction: Mummified cave cheetahs inform rewilding actions in Saudi Arabia

Homogenization and differentiation of urban tree assemblages globally

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close