Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

Abstract

Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including NASA's Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low-stature plants and very fine-scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long-term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi-scale spectroscopy, from lab studies to satellites that enable frequent and continuous long-term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

Enhanced trends in spectral greening and climate anomalies across Europe

“…The recent trends in spectral greening and browning have been linked for example to surface water body fraction, increased temperature, atmospheric carbon input, and soil moisture content (Forzieri et al, 2017;. However, satellite-based remote sensing observations mostly build on coarse-grained NDVI pixel datasets, which are mixed data composites that can only provide an estimate of the actual land cover, particularly in climate-sensitive areas such as shrubland or tundra (Herrmann & Tappan, 2013;Nelson et al, 2022). But open-source NDVI time series data are highly suitable for supraregional monitoring of spectral surface reflection variability and allow the linkage to environmental and climate driving factors, such as soil moisture content and water availability (Chen et al, 2019;Kloos et al, 2021;Peng et al, 2021)-important factors that amplify climateinduced extreme weather events.…”

Time-series maps reveal widespread change in plant functional type cover across Arctic and boreal Alaska and Yukon

“…We selected seven mutually exclusive PFT categories that collectively include all vascular plants within the study area as well as light macrolichens, a nonvascular class of high importance to caribou management (table 1). The PFTs, which largely follow Nelson et al (2022), are separated by growth form and leaf habit to optimize detectability in optical remote sensing and to characterize ecologically important distinctions related to vegetation dynamics and wildlife habitats. Dynamics and ecological feedback mechanisms for these PFTs have been extensively studied in recent decades.…”

Forests for forests: combining vegetation indices with solar-induced chlorophyll fluorescence in random forest models improves gross primary productivity prediction in the boreal forest

“…Arctic-boreal carbon cycle uncertainty can be attributed to unevenly distributed field observations and challenges in using remote sensing (e.g. high sun angles, snow cover, short growing seasons, persistent greenness) (Nelson et al 2022). Therefore, improvements must be made in how we connect remotely sensed measurements to GPP in arcticboreal ecosystems.…”