## 4 Discussion
Understanding the effects of the anisotropic properties of peatlands on remotely sensed data is important for biogeochemical modeling because transformations applied to reflectance (such as vegetation indices) are strongly affected by BRDF [66]. Goniometer measurements of moss BRDF have indicated that the infrared region showed a greater degree of variability with change in azimuth angle than the visible wavelengths [67]. For moss samples, there was no pronounced hot spot [67]. Instead, the BRDF effects constitute a higher reflectivity perpendicular to the illumination angle at low view angles (similar to illumination conditions found at high latitudes). The BRDF properties of vascular plant canopies are primarily characterized by a hotspot, a peak in reflectance when the sun is directly behind the sensor [61]. At the ecosystem scale, BRDF is a complex process, influenced not only by crown size, density, and spacing between crowns, but also the background soil BRDF, which for peatlands is the moss canopy. Vascular vegetation phenology has also been observed to strongly influence BRDF properties [63; 68]. As such, their influence on the overall surface reflectance of the peatland must also be considered. The broader implication of these observations relates to the mission planning of airborne imagery coincidental with satellite overpasses. At the latitude of Mer Bleue, coincidental image acquisition with Sentinel-2 and Landsat 8 OLI with mission planning aiming to minimize cross-track illumination effects results in suboptimal illumination conditions in the summer (i.e., vascular plants in full foliage) for retrieving biogeochemical properties of the peatland such as near surface water content [31] or WT position relying on SWIR wavelengths. While wavelengths in the VNIR range should be less impacted, allowing for the retrieval of other characteristics such as pigments contents [9], analyses requiring the SWIR region need additional considerations. Application of similar analyses to satellite based SWIR bands (e.g., Sentinel-2 bands 11 and 12) could face comparable challenges without a comprehensive BRDF correction [69]. Despite our utilization of the new Collection 1, Tier 1 surface reflectance imagery for the Landsat TM5 and OLI 8 multitemporal models predicting NEE, it was not possible to combine data from the two sensors under our current framework. The fundamental differences in spectral response (Figure 2), spatial response (i.e., line spread functions), radiometry (e.g., SNR, and radiometric resolution), and geometry (location on Earth contributing to the signal) [70] are likely the primary factors in the differences between the models derived from TM5 and OLI8 imagery. Several important changes have been made to the Landsat sensors over the years, resulting in improvements in data quality from Landsat 8 OLI in comparison to its predecessors as described in detail by [70]. However, aside from the obvious spectral resolution differences (Figure 2), we believe two other aspects influencing the specificity of the data acquired are important to consider in this context: instrument design (whiskbroom TM5 vs. pushbroom OLI 8) and radiometric resolution (8 bit TM5 vs. 12 bit OLI 8). The increased dynamic range (from the noise floor to the maximum radiance levels for each band) also leads to an improved noise and quantization performance [71]. The increased radiometric resolution and higher SNR are likely largely responsible for the greater range of MWI from Landsat 8 OLI over a similar range in observed NEE (Figure 8). Experimental degradation of the radiometric resolution of Landsat 8 OLI from 12 to 8 bits illustrated a loss of information at 9 bits, where the distribution of the original 12-bit data was no longer preserved (analysis not shown). The multiple focal plane array module (FPA) pushbroom design of OLI 8 (and Sentinel-2) replaces the mirror scanning mechanism from Landsat TM5 resulting in, among other characteristics, improved geometric accuracy.