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dc.contributor.authorRäsänen, Aleksi
dc.contributor.authorTolvanen, Anne
dc.contributor.authorKareksela, Santtu
dc.date.accessioned2022-06-29T10:15:18Z
dc.date.available2022-06-29T10:15:18Z
dc.date.issued2022
dc.identifier.citationRäsänen, A., Tolvanen, A., & Kareksela, S. (2022). Monitoring peatland water table depth with optical and radar satellite imagery. <i>International Journal of Applied Earth Observation and Geoinformation</i>, <i>112</i>, Article 102866. <a href="https://doi.org/10.1016/j.jag.2022.102866" target="_blank">https://doi.org/10.1016/j.jag.2022.102866</a>
dc.identifier.otherCONVID_147342942
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/82094
dc.description.abstractPeatland water table depth (WTD) and wetness have widely been monitored with optical and synthetic aperture radar (SAR) remote sensing but there is a lack of studies that have used multi-sensor data, i.e., combination of optical and SAR data. We assessed how well WTD can be monitored with remote sensing data, whether multi-sensor approach boosts explanatory capacity and whether there are differences in regression performance between data and peatland types. Our data consisted of continuous multiannual WTD data from altogether 50 restored and undrained Finnish peatlands, and optical (Landsat 5–8, Sentinel-2) and Sentinel-1 C-band SAR data processed in Google Earth Engine. We calculated random forest regressions with dependent variable being WTD and independent variables consisting of 21 optical and 10 SAR metrics. The average regression performance was moderate in multi-sensor models (R2 43.1%, nRMSE 19.8%), almost as high in optical models (R2 42.4%, nRMSE 19.9%) but considerably lower in C-band SAR models (R2 21.8%, nRMSE 23.4%) trained separately for each site. When the models included data from several sites but were trained separately for six habitat type and management option combinations, the average R2 was 40.6% for the multi-sensor models, 36.6% for optical models and 33.7% for C-band SAR models. There was considerable site-specific variation in the model performance (R2 −3.3–88.8% in the multi-sensor models ran separately for each site) and whether multi-sensor, optical or C-band SAR model performed best. The average regression performance was higher for undrained than for restored peatlands, and higher for open and sparsely treed than for densely treed peatlands. The most important variables included SWIR-based optical metrics and VV SAR backscatter. Our results suggest that optical data works usually better than does C-band SAR data in peatland WTD monitoring and multi-sensor approach increases explanatory capacity moderately little.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesInternational Journal of Applied Earth Observation and Geoinformation
dc.rightsCC BY-NC-ND 4.0
dc.subject.otheroptical satellite imagery
dc.subject.otherpeatland
dc.subject.othersoil moisture
dc.subject.othersynthetic aperture radar
dc.subject.otherwetland
dc.subject.otherwetness
dc.titleMonitoring peatland water table depth with optical and radar satellite imagery
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202206293694
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.contributor.oppiaineEkologia ja evoluutiobiologiafi
dc.contributor.oppiaineResurssiviisausyhteisöfi
dc.contributor.oppiaineEcology and Evolutionary Biologyen
dc.contributor.oppiaineSchool of Resource Wisdomen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn1569-8432
dc.relation.volume112
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysokosteikot
dc.subject.ysosatelliittikuvat
dc.subject.ysokaukokartoitus
dc.subject.ysosuot
dc.subject.ysooptiset laitteet
dc.subject.ysomaaperä
dc.subject.ysokosteus
dc.subject.ysomittausmenetelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p16352
jyx.subject.urihttp://www.yso.fi/onto/yso/p29435
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
jyx.subject.urihttp://www.yso.fi/onto/yso/p10981
jyx.subject.urihttp://www.yso.fi/onto/yso/p3023
jyx.subject.urihttp://www.yso.fi/onto/yso/p1675
jyx.subject.urihttp://www.yso.fi/onto/yso/p6453
jyx.subject.urihttp://www.yso.fi/onto/yso/p20083
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.jag.2022.102866
jyx.fundinginformationThe research was funded by the Ministry of the Environment and Natural Resources Institute Finland and supported by Parks & Wildlife Finland (Metsähallitus) through collecting data.


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