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A novel multi-sourc...
A novel multi-source data fusion method based on Bayesian inference for accurate estimation of chlorophyll-a concentration over eutrophic lakes
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- Chen, Cheng (författare)
- Nanjing Hydraulic Research Institute,Hohai University
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- Chen, Qiuwen (författare)
- Nanjing Hydraulic Research Institute
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- Li, Gang (författare)
- Nanjing Hydraulic Research Institute
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- He, Mengnan (författare)
- Nanjing Hydraulic Research Institute
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- Dong, Jianwei (författare)
- Nanjing Hydraulic Research Institute
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- Yan, Hanlu (författare)
- Nanjing Hydraulic Research Institute
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- Wang, Zhiyuan (författare)
- Nanjing Hydraulic Research Institute
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- Duan, Zheng (författare)
- Lund University,Lunds universitet,MERGE: ModElling the Regional and Global Earth system,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Institutionen för naturgeografi och ekosystemvetenskap,Centre for Environmental and Climate Science (CEC),Faculty of Science,Dept of Physical Geography and Ecosystem Science
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(creator_code:org_t)
- Elsevier BV, 2021
- 2021
- Engelska.
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Ingår i: Environmental Modelling and Software. - : Elsevier BV. - 1364-8152. ; 141
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Annan geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Other Earth and Related Environmental Sciences (hsv//eng)
Nyckelord
- Bayesian inference
- Chlorophyll-a
- Eutrophic lake
- Lake taihu
- Multi-source data fusion
- Multiplicative error model
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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