## 5 Conclusions  
We successfully modeled WT depth and NEE from airborne hyperspectral and satellite multispectral imagery, respectively. To the best of our knowledge, this is the first time these parameters were retrieved from optical imagery for a peatland at such a fine spatial resolution (1 m for WT depth and 20-30 m for NEE). The narrow band index NDWI\({}_{1240}\) was shown to be sensitive to both liquid water content in the vegetation as well as phenology and, therefore, can be used as a proxy for estimating WT depth. For the retrieval of NEE, we propose separate models, one for historical estimations from Landsat TM5 and a separate model going forward with Landsat 8 OLI or Sentinel-2. We found it was not possible to combine data from sensors with very different spectral and radiometric properties into one model. Despite the successful retrieval of NEE from satellite imagery, the use of airborne HSI improved the relationship with eddy covariance tower-measured NEE. We believe the higher radiometric resolution of the airborne HSI and much finer spatial and spectral resolutions are the reasons for this difference. The anisotropic properties of the peatland should be considered for the collection of SWIR airborne HSI in the summer when the vascular plants are in full foliage, however, this is less of a concern in the spring prior to green-up. Multitemporal HSI should be collected with consistent illumination and viewing geometries in these types of studies, with the RAA in line with the heading as much as possible. For satellite-based estimations of peatland biogeochemical properties, care must be taken with regards to the detection and removal of high-altitude cirrus. Despite the transparent nature of these clouds, they impact the surface reflectance products, and scenes with cirrus contamination cannot be used to reliably retrieve NEE. Despite their importance, uncertainties about the total global peatland spatial extent persist, due in part to difficulties in mapping peatlands from coarse resolution remotely sensed data [4; 10]. Recent estimates of global peatland extent surpass 4.5 million km\({}^{2}\)[4], with over 1 million km\({}^{2}\) in both Europe [87] and Canada [3], respectively, based on compilations of national datasets. With the increasing global archives of Landsat and Sentinel-2 imagery, and planetary scale computing platforms, such as Google Earth Engine [42], the methodologies developed here for NEE could be applied to other northern peatlands without tree cover. Ground measurements of NEE from the eddy covariance method should be incorporated to validate the models if applied elsewhere. Global networks such as FLUXNET [88] collect multitemporal data of CO\({}_{2}\) exchange that could be used as model training and validation. Protocols being established for satellite-based Land Data Product Validation [89] will allow for greater intercomparability between sites. In the absence of airborne or UAV-based hyperspectral imagery, data from planned spaceborne hyperspectral missions could be investigated to determine their utility for predicting WT depth, provided sufficient _in situ_ data are available to validate the results, especially for WT depth outside the range examined here. Climate change affects peatlands directly [90; 91]. One of the most important drivers of change in the functioning of northern peatlands is the WT depth. Even modest decreases in water table depth coupled with increased air temperature have been shown to lead to profound changes in the proportional contributions of mosses and vascular plants to biomass production [91]. In addition, drought conditions with a lowering of the WT have recently been shown to result in an increase in higher ecosystem respiration, due in part to changes in vegetation composition where vascular plants replaced _Sphagnum_ mosses [90]. Remotely sensed data offer a reliable means to monitor such changes on an ongoing basis.