Abstract
ContextSpatial patterns of CH4 fluxes can be modeled with remotely sensed data representing land cover, soil moisture and topography. Spatially extensive CH4 flux measurements conducted with portable analyzers have not been previously upscaled with remote sensing.ObjectivesHow well can the CH4 fluxes be predicted with plot-based vegetation measures and remote sensing? How does the predictive skill of the model change when using different combinations of predictor variables?MethodsWe measured CH4 fluxes in 279 plots in a 12.4 km2 peatland-forest-mosaic landscape in Pallas area, northern Finland in July 2019. We compared 20 different CH4 flux maps produced with vegetation field data and remote sensing data including Sentinel-1, Sentinel-2 and digital terrain model (DTM).ResultsThe landscape acted as a net source of CH4 (253–502 µg m−2 h−1) and the proportion of source areas varied considerably between maps (12–50%). The amount of explained variance was high in CH4 regressions (59–76%, nRMSE 8–10%). Regressions including remote sensing predictors had better performance than regressions with plot-based vegetation predictors. The most important remote sensing predictors included VH-polarized Sentinel-1 features together with topographic wetness index and other DTM features. Spatial patterns were most accurately predicted when the landscape was divided into sinks and sources with remote sensing-based classifications, and the fluxes were modeled for sinks and sources separately.ConclusionsCH4 fluxes can be predicted accurately with multi-source remote sensing in northern boreal peatland landscapes. High spatial resolution remote sensing-based maps constrain uncertainties related to CH4 fluxes and their spatial patterns.
Highlights
Methane (CH4) is the second most important greenhouse gas (IPCC 2013), and a large part of CH4 emissions originate from northern peatlands (Frolking et al 2011; Turetsky et al 2014; Abdalla et al 2016)
We compared 20 different CH4 flux maps produced with vegetation field data and remote sensing data including Sentinel1, Sentinel-2 and digital terrain model (DTM)
CH4 fluxes can be predicted with a range of different remote sensing datasets, and remote sensing works better than plot-based vegetation measures in explaining CH4 patterns (Table 4)
Summary
Methane (CH4) is the second most important greenhouse gas (IPCC 2013), and a large part of CH4 emissions originate from northern peatlands (Frolking et al 2011; Turetsky et al 2014; Abdalla et al 2016). CH4 is transported from soil to the atmosphere by molecular diffusion, in the form of gas bubbles (i.e. ebullition) and plant-mediated processes. Important factors controlling CH4 emissions are soil temperature, water table depth, plant community composition, and soil pH (Turetsky et al 2014; Abdalla et al 2016). These factors are spatially heterogenic in diverse scales in the landscape, and there are considerable uncertainties in the resulting net emission of CH4 by the peatlands and how the emissions are distributed spatially (Turetsky et al 2014). To constrain the different uncertainties, there have been attempts to assess and upscale CH4 flux patterns over different spatial extents ranging from a single mire (Lehmann et al 2016) to landscapes (Dinsmore et al 2017) and to synthesize CH4 controls globally (Turetsky et al 2014)
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