A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland

Abstract

AbstractQuantification of methane (CH4) gas emission from peat is critical to understand CH4 budget from natural wetlands under a climate warming scenario. Previous studies have focused on prediction and mapping of CH4 emission flux using process‐based models, while application of statistical‐empirical models for upscaling spatially sparse in situ measurements is scarce. In this study, we developed an empirical remote sensing upscaling approach to estimate CH4 emission flux in the Everglades using limited in situ point‐based CH4 emission flux measurements and Landsat data during 2013–2018. We spatially and temporally linked in situ data with Landsat surface reflectance based on temporally composite data sets and developed an object‐based machine learning framework to model and map CH4 emission flux. An ensemble analysis of two machine learning models, k‐Nearest Neighbor (k‐NN) and Support Vector Machine (SVM), shows that the upscaling approach is promising for predicting CH4 emission flux with a R2 of 0.65 and 0.87 based on a fivefold cross‐validation for a dry season and wet season estimation, respectively. We generated emission flux map products that successfully revealed the spatial and temporal heterogeneity of CH4 emission within the dominant freshwater marsh ecosystem in the Everglades. We conclude that Landsat is promising for upscaling and monitoring CH4 emission flux and reducing the uncertainty in emission estimates from wetlands.