In recent decades, anthropogenic and natural sources of greenhouse gases (GHGs) have been broadly studied to quantify their impact on the earth’s changing climate. Natural sources constitute 40-50% of global methane (CH4) emissions with wetlands contributing upwards of 40% of those natural sources. Despite being a major source of natural CH4 emissions, researchers’ ability to predict emission rates from individual wetlands is poor. This knowledge gap exists largely due to the high cost of existing flux measurement systems as well as the variability of wetland CH4 emissions over space and time. These factors unite to prevent comprehensive quantification of CH4 emissions from wetlands. Given that CH4 emissions have a global warming potential (GWP) over 25 times that of carbon dioxide (CO2), quantifying environmental drivers and flux rates is crucial for filling the knowledge gap. To address this need, we have developed a low-cost CH4 flux chamber that can autonomously collect data in the field using a semiconductor gas sensor. While these sensors are sensitive to CH4, limits to manufacturing processes mean that each device must be calibrated individually to incorporate the influence of temperature and humidity on sensor outputs. We evaluated regression and machine learning approaches including generalized additive models (GAMs), random forests, and support vector machines to create calibration protocols and equations that will reduce the time and expertise required to calibrate each sensor. GAMs were the most accurate method for generating a reliable calibration model for this specific sensor, particularly when CH4 concentrations were high. We applied this flux protocol approach to examine the impact of environmental drivers on fine scale variation of depressional wetlands in Midwestern, USA. Specifically, we evaluated the impact of microtopography and freeze/thaw cycles, which are key factors related to wetland CH4 emissions. Additionally, we found high spatial variation related to microtopography and water depth. Saturated soils had the highest CH4 fluxes whereas dry soil had the lowest. Increased water depth slightly attenuated CH4 fluxes and increased the temporal variation of fluxes, likely due to ebullition. Rapid thawing events in the winter resulted in substantial CH4 flux events, especially saturated zones with ice cover.