With warming temperatures, Arctic ecosystems are changing from a net sink to a net sourceof carbon to the atmosphere, but the Arctic’s carbon balance remains highly uncertain. Landscapes are often assumed to be homogeneous when interpreting eddy covariance carbon fluxes, which can lead to biases when gap-filling and scaling-up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence carbon dioxide (CO₂) and methane (CH4) fluxes. In Chapter 2, I reported results from growing season CO₂ and CH₄ fluxes from an eddy covariance tower in the Yukon-Kuskokwim (YK) Delta in Alaska. I used flux footprint models and Bayesian Markov Chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent landcover fluxes based on high resolution landcover maps of the region. I compared three types of footprint models and used two landcover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. I used artificially created gaps of withheld observations to compare gap-filling performance using our derived landcover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. I also compared regional carbon budgets scaled up from observations using heterogeneous and homogeneous approaches. Gap-filling methods that accounted for heterogeneous landscapes were better at predicting artificially withheld gaps in CO₂ fluxes than traditional approaches, and there were only slight differences performance between footprint models and landcover maps. I identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). I resolved distinct seasonality in tundra growing season CO₂ fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO₂ sink by a factor of two and underestimated CH₄ emissions by a factor of two when compared to scaling with any method that accounts for landscape heterogeneity. I showed how Bayesian MCMC, analytical footprint models, and high resolution landcover maps can be leveraged to derive detailed landcover carbon fluxes from eddy covariance timeseries. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic. Climate change is causing an intensification in tundra fires across the Arctic, including the unprecedented 2015 fires in the YK Delta. The YK Delta contains extensive surface waters (approximately 33% cover) and significant quantities of organic carbon, much of which is stored in vulnerable permafrost. Inland aquatic ecosystems act as hot-spots for landscape CO₂ and CH₄ emissions and likely represent a significant component of the Arctic carbon balance, yet aquatic fluxes of CO₂ and CH₄ are also some of the most uncertain. In Chapter 3, I measured dissolved CO₂ and CH₄ concentrations (n = 364), in surface waters from different types of waterbodies during summers from 2016 to 2019. I used Sentinel-2 multispectral imagery to classify landcover types and area burned in contributing watersheds. I developed a model using machine learning to assess how waterbody properties (size, shape, and landscape properties), environmental conditions (O₂ concentration, temperature), and surface water chemistry (dissolved organic carbon composition, nutrient concentrations) help predict in situ observations of CO₂ and CH₄ concentrations across deltaic waterbodies. CO₂ concentrations were negatively related to waterbody size and positively related to waterbody edge effects. CH₄ concentrations were primarily related to organic matter quantity and composition. Waterbodies in burned watersheds appeared to be less carbon limited and had longer soil water residence times than in unburned watersheds. My results illustrated the importance of small lakes for regional carbon emissions and demonstrate the need for a mechanistic understanding of the drivers of greenhouse gasses in small waterbodies. In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. In Chapter 4, I demonstrated how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO₂ and CH₄ fluxes across the YK Delta of south-west AK. I found that waterbody size and contour were strong predictors for aquatic CO₂ emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km²) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km²) contributed the majority of carbon emissions from waterbodies. Waterbody CH₄ emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to my machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO₂ and CH₄ emissions by 26%–79% and 8%–53% respectively. This chapter demonstrated the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling carbon emissions in the arctic. In order to understand carbon feedbacks with the atmosphere and predict climate change, we need to develop methods to model and scale up carbon emissions. Gridded datasets of carbon fluxes are used to benchmark Earth system models, attribute changes in rates of atmospheric concentrations of greenhouse gases, and project future climate change. There are two main approaches to deriving gridded datasets of carbon fluxes and global or regional carbon budgets: bottom-up scaling, and top-down atmospheric inversions. There is often divergence between approaches, with carbon budgets calculated from bottom-up and top-down studies rarely overlapping. The resulting uncertainty in carbon budgets calculated from either approach is more pronounced in high-latitudes. One of the challenges with combining bottom-up models and comparing top-down models is the variable spatial resolutions used in each approach. In Chapter 5, I applied flux scaling models from earlier chapters to create bottom-up carbon budgets at very high resolution (10 m) for the entire YK Delta domain. I used ERA5 land reanalysis data to extend the flux models to 2012-2015 and 2017 growing seasons to coincide with airborne observations of atmospheric CO₂ and CH₄ concentrations from NASA CARVE and Arctic-CAP campaigns. I progressively coarsened remote sensing imagery for the region to 30 m, 90 m, 250 m, and 1 km to create coarser landcover maps and corresponding bottom-up carbon budgets. The high resolution bottom-up models, when convolved with concentration footprints, produced simulated atmospheric enhancements that were similar to observed atmospheric enhancements. There was little change coarsening to 30 m and 90 m, but simulated atmospheric enhancements and especially carbon budgets were quite different at 250 m and 1 km spatial resolution. The changes with resolution were largely the result of an increase in area mapped as wetlands and shrub tundra, and less area mapped as small waterbodies and lichen tundra. Coarser resolution bottom-up scaling consistently overestimated CH4 budgets. By evaluating flux models against atmospheric observations, I was able to diagnose missing components such as inland water carbon emissions and times when the scaling models overestimated emissions to missing seasonal dynamics. This dissertation combined novel uses of statistical techniques with a high density of field observations to yield process-level understanding of carbon cycling that could be applied to scaling-up carbon emissions. By merging terrestrial and aquatic perspectives and concurrently mapping ecosystem landcovers and disturbances at high spatial resolution, I avoided common sources of uncertainty in carbon budgets such as double-counting of areas. I investigated how we represent the landscape in terms of both spatial resolution and the level of landscape heterogeneity, and determined the effects of these choices on carbon fluxes and budget estimates. By comparing to the atmosphere, I evaluated the validity of different approaches to modeling carbon fluxes in the Arctic. Together, the chapters in this dissertation provided a holistic study of carbon cycling in the Arctic.