Predicting catchment-scale methane fluxes with multi-source remote sensing

Abstract

Context Spatial 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. Objectives How 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? Methods We 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). Results The 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. Conclusions CH4 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.

Subarctic catchment water storage and carbon cycling – Leading the way for future studies using integrated datasets at Pallas, Finland

“…While the strength and variability of CH 4 fluxes are known to be strongly related to hydrology (Knox et al, 2019; Räsänen et al, 2021; Zhang et al, 2020), the seasonal dynamics in CO 2 and CH 4 fluxes typically follow the patterns in vegetation activity and, both directly and indirectly (through phenology), the patterns in temperature. On the other hand, the non‐growing season fluxes can be more directly related to hydrological processes, particularly at sites dominated by active surface flow patterns, such as Lompolojänkkä mire.…”

“…At the landscape level, a variety of hydrological factors and flow paths control bio-physiochemical processes that substantially influence sources and sinks of GHG. For example, recent analyses of the controls on spatial variability in CH 4 fluxes at the mire site (Zhang et al, 2020) and in the larger Pallas catchment (Räsänen et al, 2021) suggest that hydrological pathways strongly controls the exchange rate and direction of CH 4 exchange. At the mire site, distance to the stream, which controls the area of oxic/ anoxic zone in the peat by bringing O 2 -rich water to the mire, is reported to be the most important factor explaining the spatial variability (Zhang et al, 2020).…”

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

“…The test area is Pallas in northern Finland, the corner co-ordinates being 67.9122 • N, 24.0586 • E and 68.0233 • N, 24.2539 • E (see Fig. 1) [34]. The terrain is partly hilly, the altitude varying in the range 260-610 m above sea level.…”

“…Biomass and LAI of Norway spruce (Picea abies), Scots pine (Pinus sylvestris) and Downy birch (Betula pubescens), and other deciduous species were available for 131 plots in the test area [34]. The LAI values were calculated using measured [34] tree height and diameter values [48], [49]. The total biomass and LAI values for coniferous and deciduous species and all trees were determined as well (see Table II).…”

“…Hence, normal spatial averaging or Lee filtering [8] to reduce speckle is not applicable. Previously, we have studied midsummer soil moisture patterns and related methane fluxes with a combination of SAR, optical, and topographic data for this region [34]. However, spatiotemporal changes in soil moisture were not assessed.…”