Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

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dc.contributor.authorShi, Haiyang
dc.contributor.authorLou, Geping
dc.contributor.authorHellwich, Olaf
dc.contributor.authorXie, Mingjuan
dc.contributor.authorZhang, Chen
dc.contributor.authorZhang, Yu
dc.contributor.authorWang, Yuangang
dc.contributor.authorYuan, Xiuliang
dc.contributor.authorMa, Xiaofei
dc.contributor.authorZhang, Wenqiang
dc.contributor.authorKurban, Alishir
dc.contributor.authorMaeyer, Philippe De
dc.contributor.authorVoorde, Tim Van de
dc.date.accessioned2023-01-30T11:50:38Z
dc.date.available2023-01-30T11:50:38Z
dc.date.issued2022-08-16
dc.description.abstractNet ecosystem exchange (NEE) is an important indicator of carbon cycling in terrestrial ecosystems. Many previous studies have combined flux observations and meteorological, biophysical, and ancillary predictors using machine learning to simulate the site-scale NEE. However, systematic evaluation of the performance of such models is limited. Therefore, we performed a meta-analysis of these NEE simulations. A total of 40 such studies and 178 model records were included. The impacts of various features throughout the modeling process on the accuracy of the model were evaluated. Random forests and support vector machines performed better than other algorithms. Models with larger timescales have lower average R2 values, especially when the timescale exceeds the monthly scale. Half-hourly models (average R2 = 0.73) were significantly more accurate than daily models (average R2 = 0.5). There are significant differences in the predictors used and their impacts on model accuracy for different plant functional types (PFTs). Studies at continental and global scales (average R2 = 0.37) with multiple PFTs, more sites, and a large span of years correspond to lower R2 values than studies at local (average R2 = 0.69) and regional (average R2 = 0.7) scales. Also, the site-scale NEE predictions need more focus on the internal heterogeneity of the NEE dataset and the matching of the training set and validation set.en
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlinen
dc.identifier.eissn1726-4189
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18115
dc.identifier.urihttps://doi.org/10.14279/depositonce-16908
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550 Geowissenschaftende
dc.subject.otherNet ecosystem exchangeen
dc.subject.otherNEEen
dc.subject.othercarbon cycling in terrestrial ecosystemsen
dc.subject.othermeta-analysis of NEE simulationsen
dc.subject.otherNEE dataseten
dc.titleVariability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluationen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.doi10.5194/bg-19-3739-2022
dcterms.bibliographicCitation.journaltitleBiogeosciences (BG)
dcterms.bibliographicCitation.originalpublishernameCopernicus
dcterms.bibliographicCitation.originalpublisherplaceKatlenburg-Lindau
dcterms.bibliographicCitation.pageend3756
dcterms.bibliographicCitation.pagestart3739
dcterms.bibliographicCitation.volume19
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree*
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Computer Vision & Remote Sensing
tub.publisher.universityorinstitutionTechnische Universität Berlin

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