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.

中文翻译：

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利用多源遥感预测集水规模甲烷通量

语境

CH ₄通量的空间格局可以利用代表土地覆盖，土壤湿度和地形的遥感数据进行建模。使用便携式分析仪进行的空间范围广泛的CH ₄通量测量以前尚未通过遥感技术进行升级。

目标

通过基于样地的植被测量和遥感，可以预测到CH ₄通量有多好？当使用不同的预测变量组合时，模型的预测技巧将如何变化？

方法

我们于2019年7月在芬兰北部帕拉斯地区的12.4 km ^2的泥炭地-森林-马赛克景观中的279个样地中测量了CH ₄通量。我们将产生的20种不同的CH ₄通量图与植被场数据和遥感数据（包括Sentinel-1）进行了比较，Sentinel-2和数字地形模型（DTM）。

结果

景观作为CH ₄的净源（253–502 µg m ^-2  h ^-1），源图的比例在地图之间差异很大（12–50％）。在CH ₄回归中，解释的方差量很高（59-76％，nRMSE 8-10％）。包括遥感预报器在内的回归性能要优于基于样地的植被预报器。最重要的遥感预报器包括VH极化的Sentinel-1功能以及地形湿度指数和其他DTM功能。通过基于遥感的分类将景观分为汇和源时，可以最准确地预测空间格局，并分别为汇和源建模通量。

结论

在北部北方泥炭地景观中，通过多源遥感可以准确预测CH ₄通量。高空间分辨率基于遥感的地图限制了与CH ₄通量及其空间模式有关的不确定性。