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Zheng Duan

Biträdande universitetslektor

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A novel multi-source data fusion method based on Bayesian inference for accurate estimation of chlorophyll-a concentration over eutrophic lakes

Författare

  • Cheng Chen
  • Qiuwen Chen
  • Gang Li
  • Mengnan He
  • Jianwei Dong
  • Hanlu Yan
  • Zhiyuan Wang
  • Zheng Duan

Summary, in English

A novel multi-source data fusion method based on Bayesian inference (BIF) was proposed in this study to blend the advantages of in-situ observations and remote sensing estimations for obtaining accurate chlorophyll-a (Chla) concentration in Lake Taihu (China). Two error models (additive and multiplicative) were adopted to construct the likelihood function in BIF; the BIF method was also compared with three commonly used data fusion algorithms, including linear and nonlinear regression data fusion (LRF and NLRF) and cumulative distribution function matching data fusion (CDFF). The results showed the multiplicative error model had small normalized residual errors and was a more suitable choice. The BIF method largely outperformed the data fusion algorithms of CDFF, NLRF and LRF, with the largest correlation coefficients and smallest root mean square error. Moreover, the BIF results can capture the high Chla concentrations in the northwest and the low Chla concentrations in the east of Lake Taihu.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • MERGE: ModElling the Regional and Global Earth system

Publiceringsår

2021-07

Språk

Engelska

Publikation/Tidskrift/Serie

Environmental Modelling and Software

Volym

141

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Other Earth and Related Environmental Sciences

Nyckelord

  • Bayesian inference
  • Chlorophyll-a
  • Eutrophic lake
  • Lake taihu
  • Multi-source data fusion
  • Multiplicative error model

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1364-8152