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Triple collocation-based merging of multi-source gridded evapotranspiration data in the Nordic Region

Li, Xueying LU orcid ; Zhang, Wenxin LU orcid ; Vermeulen, Alex LU orcid ; Dong, Jianzhi and Duan, Zheng LU (2023) In Agricultural and Forest Meteorology 335.
Abstract
Accurate evapotranspiration (ET) data are required for many hydro-meteorological applications. Compared with the traditional evaluation that requires in-situ measurements, the triple collocation (TC) technique estimates geophysical product errors without the need for ground truth, which is especially suitable over large areas lacking a dense in-situ network. However, violations of the zero-error cross-correlation (ECC) assumption are found to be the dominant sources of impairing the TC robustness. This study presents the first application of a TC-based merging framework that optimally considers ECC to merge multi-source gridded ET products in the Nordic Region during 2003–2018. The ECC estimates of each ET dataset pair calculated by the... (More)
Accurate evapotranspiration (ET) data are required for many hydro-meteorological applications. Compared with the traditional evaluation that requires in-situ measurements, the triple collocation (TC) technique estimates geophysical product errors without the need for ground truth, which is especially suitable over large areas lacking a dense in-situ network. However, violations of the zero-error cross-correlation (ECC) assumption are found to be the dominant sources of impairing the TC robustness. This study presents the first application of a TC-based merging framework that optimally considers ECC to merge multi-source gridded ET products in the Nordic Region during 2003–2018. The ECC estimates of each ET dataset pair calculated by the quadruple collocation approach are used to select the qualified triplets from four products, including FLUXCOM, Global Land Surface Satellite (GLASS), Global Land Evaporation and Amsterdam Model (GLEAM), and Penman-Monteith-Leuning Version 2 (PML-V2). Then the ET merged datasets are generated by weighting TC-based rescaled error variances of the parent datasets through least square merging. Finally, the accuracy of both the parent and the merged datasets are assessed with the Integrated Carbon Observation System (ICOS) flux data in the Nordic Region based on multiple statistical metrics. Results demonstrate that the ECC values provide intuitive evidence for filtering unqualified TC triplets. Both the absolute and relative error variances (signal-to-noise ratio) are considered for ET dataset evaluation. Overall PML-V2 has the best performance among the evaluated four products. Two merged ET datasets with the reference climatology of FLUXCOM outperform all parent products with the lowest errors by using ICOS data as reference among all sites – indicating the feasibility of TC technique for improving ET accuracy in the Nordic Region. This study also analyses the impacts of reference climatology selection on the TC merged results. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Triple collocation, Quadruple collocation, ICOS, Evapotranspiration, Satellite remote sensing
in
Agricultural and Forest Meteorology
volume
335
article number
109451
publisher
Elsevier
external identifiers
  • scopus:85151684217
ISSN
1873-2240
DOI
10.1016/j.agrformet.2023.109451
language
English
LU publication?
yes
id
61188742-36a8-4559-afc5-229a11f08e59
date added to LUP
2023-04-21 09:41:33
date last changed
2024-06-11 11:22:24
@article{61188742-36a8-4559-afc5-229a11f08e59,
  abstract     = {{Accurate evapotranspiration (ET) data are required for many hydro-meteorological applications. Compared with the traditional evaluation that requires in-situ measurements, the triple collocation (TC) technique estimates geophysical product errors without the need for ground truth, which is especially suitable over large areas lacking a dense in-situ network. However, violations of the zero-error cross-correlation (ECC) assumption are found to be the dominant sources of impairing the TC robustness. This study presents the first application of a TC-based merging framework that optimally considers ECC to merge multi-source gridded ET products in the Nordic Region during 2003–2018. The ECC estimates of each ET dataset pair calculated by the quadruple collocation approach are used to select the qualified triplets from four products, including FLUXCOM, Global Land Surface Satellite (GLASS), Global Land Evaporation and Amsterdam Model (GLEAM), and Penman-Monteith-Leuning Version 2 (PML-V2). Then the ET merged datasets are generated by weighting TC-based rescaled error variances of the parent datasets through least square merging. Finally, the accuracy of both the parent and the merged datasets are assessed with the Integrated Carbon Observation System (ICOS) flux data in the Nordic Region based on multiple statistical metrics. Results demonstrate that the ECC values provide intuitive evidence for filtering unqualified TC triplets. Both the absolute and relative error variances (signal-to-noise ratio) are considered for ET dataset evaluation. Overall PML-V2 has the best performance among the evaluated four products. Two merged ET datasets with the reference climatology of FLUXCOM outperform all parent products with the lowest errors by using ICOS data as reference among all sites – indicating the feasibility of TC technique for improving ET accuracy in the Nordic Region. This study also analyses the impacts of reference climatology selection on the TC merged results.}},
  author       = {{Li, Xueying and Zhang, Wenxin and Vermeulen, Alex and Dong, Jianzhi and Duan, Zheng}},
  issn         = {{1873-2240}},
  keywords     = {{Triple collocation; Quadruple collocation; ICOS; Evapotranspiration; Satellite remote sensing}},
  language     = {{eng}},
  month        = {{05}},
  publisher    = {{Elsevier}},
  series       = {{Agricultural and Forest Meteorology}},
  title        = {{Triple collocation-based merging of multi-source gridded evapotranspiration data in the Nordic Region}},
  url          = {{http://dx.doi.org/10.1016/j.agrformet.2023.109451}},
  doi          = {{10.1016/j.agrformet.2023.109451}},
  volume       = {{335}},
  year         = {{2023}},
}