Given the strong seasonality of Arctic vegetation (see Section 4.4), additional consideration of the timescale of measurement and underlying phenomena are critical to mapping efforts. Coupled observations across spatial scales that can be conducted with high observation frequency across seasons will help resolve this challenge (Table 2). Further, both seasonal and interannual evaluations of change in the Arctic tundra must consider the constraints of winter in terms of both sampling design and physiological effects. The rapid seasonal progression (as discussed in Section 4.4) imposes tremendous challenges for benchmarking the onset of seasonal photosynthetic activity and tissue growth, quantifying sensitivity to shoulder season stress, and detecting legacy effects on productivity in subsequent seasons. In particular, the strong seasonality of photoprotective pigments in evergreens (see Section 3.4 and 4.4), which complicate interpretation of spectral reflectance, requires further research across the Arctic tundra domain to improve parameterization of models of primary production. Additionally, deciduous shrub species exhibit strong autumn leaf coloration with photoprotective pigments present (and chlorophyll content declining) during leaf senescence that may facilitate remotely sensed quantification of species cover values. For example the red autumn leaves of birch continue to actively photosynthesize even though chlorophyll pigments may be less evident by traditional greeness-based remote sensing (Patankar et al., 2013). Spectroscopy is well suited to address these challenges, and could likely help disentangle the timing of vegetation responses among plant functional types.

The use of optical remote sensing information over large regions (i.e., across continents) and through time (i.e., multiple decades) has increased considerably in recent years (Ustin & Middleton, 2021). This includes IS data in the Arctic (e.g., (Langford et al., 2019)), given the increased availability of these datasets (Miller et al., 2019). However, new approaches for access, use, and analysis of large IS datasets will be needed given the growing volume of remote sensing observations across scales. For example, fusing high volume data from novel UAS and ground-based platforms and expanded use of datasets across scenes and locations will greatly increase the overall volume of data for any given project. Seasonal weather conditions and sun-sensor geometry changes in the Arctic mean that a considerable fraction of data may have variable data quality over scenes or across scenes in a study area. Similarly, current methods for retrieval of IS data require manual search, collection and combining of data across different locations by end-users. To ease and expand use of IS data for Arctic researchers, it is recommended that data systems provide analysis-ready (e.g., geo-rectified and consistent atmospheric correction) and cloud-optimized data storage formats (e.g., cloud-optimized GeoTIFF). In addition, files should be accessible on storage buckets (i.e., basic container that stores bulk data, usually used for organizing combinations of similar datasets, e.g., S3 or Google cloud bucket) through cloud-based tools to facilitate rapid search, filter, and extraction of data across specific locations, regions and scenes. Similarly, it is recommended that cloud-based tools facilitate basic analyses, data transformation, subsetting, and application of mapping algorithms without downloading large volumes of IS datasets but instead the final derived products or results of the cloud pre-processing. For example, this could be facilitated through the use of a cloud storage location within Google Earth Engine (GEE) or GEE within the Python or R environments. By moving IS data access to the cloud would also facilitate easy combination with other remote sensing data or even multiscale observations, including UAS data. This would also reduce the data latency from collection to community use and allow more users to facilitate discovery of novel and important patterns in phenomena in the Arctic biome.

We described important attributes of tundra ecosystems that impose challenges for conducting spectroscopy, including plant functional type and pixel-composition characteristics, intrinsic dimensionality, and capacity for land cover classification, change detection, time series observations, and characterizing biophysical properties. Future spectroscopy missions such as SBG would be well-advised to consider the challenges of complex biomes such as the Arctic tundra during mission development and especially for data product generation. To address these challenges, an optimized mixture of narrow and broad bands should be considered for SBG to accurately characterize Arctic vegetation.