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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

Chen, Cheng ; Chen, Qiuwen ; Li, Gang ; He, Mengnan ; Dong, Jianwei ; Yan, Hanlu ; Wang, Zhiyuan and Duan, Zheng LU (2021) In Environmental Modelling and Software 141.
Abstract

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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bayesian inference, Chlorophyll-a, Eutrophic lake, Lake taihu, Multi-source data fusion, Multiplicative error model
in
Environmental Modelling and Software
volume
141
article number
105057
publisher
Elsevier
external identifiers
  • scopus:85107142930
ISSN
1364-8152
DOI
10.1016/j.envsoft.2021.105057
language
English
LU publication?
yes
id
37799796-b507-47a0-bf53-1f2a49484de0
date added to LUP
2021-06-24 12:51:51
date last changed
2023-02-21 10:19:20
@article{37799796-b507-47a0-bf53-1f2a49484de0,
  abstract     = {{<p>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.</p>}},
  author       = {{Chen, Cheng and Chen, Qiuwen and Li, Gang and He, Mengnan and Dong, Jianwei and Yan, Hanlu and Wang, Zhiyuan and Duan, Zheng}},
  issn         = {{1364-8152}},
  keywords     = {{Bayesian inference; Chlorophyll-a; Eutrophic lake; Lake taihu; Multi-source data fusion; Multiplicative error model}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Environmental Modelling and Software}},
  title        = {{A novel multi-source data fusion method based on Bayesian inference for accurate estimation of chlorophyll-a concentration over eutrophic lakes}},
  url          = {{http://dx.doi.org/10.1016/j.envsoft.2021.105057}},
  doi          = {{10.1016/j.envsoft.2021.105057}},
  volume       = {{141}},
  year         = {{2021}},
}