TY - JOUR
T1 - Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance
T2 - Opportunities and Challenges
AU - Nelson, Peter R.
AU - Maguire, Andrew J.
AU - Pierrat, Zoe
AU - Orcutt, Erica L.
AU - Yang, Dedi
AU - Serbin, Shawn
AU - Frost, Gerald V.
AU - Macander, Matthew J.
AU - Magney, Troy S.
AU - Thompson, David R.
AU - Wang, Jonathan A.
AU - Oberbauer, Steven F.
AU - Zesati, Sergio Vargas
AU - Davidson, Scott J.
AU - Epstein, Howard E.
AU - Unger, Steven
AU - Campbell, Petya K.E.
AU - Carmon, Nimrod
AU - Velez-Reyes, Miguel
AU - Huemmrich, K. Fred
N1 - Publisher Copyright:
© 2022 Jet Propulsion Laboratory, California Institute of Technology. Government sponsorship acknowledged.
PY - 2022/2
Y1 - 2022/2
N2 - 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 National Aeronautics and Space Administration’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.
AB - 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 National Aeronautics and Space Administration’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.
KW - Arctic tundra
KW - imaging spectroscopy
KW - intrinsic dimensionality
KW - surface biology and geology
KW - vegetation
UR - http://www.scopus.com/inward/record.url?scp=85125132745&partnerID=8YFLogxK
U2 - 10.1029/2021JG006697
DO - 10.1029/2021JG006697
M3 - Article
AN - SCOPUS:85125132745
SN - 2169-8953
VL - 127
JO - Journal of Geophysical Research: Biogeosciences
JF - Journal of Geophysical Research: Biogeosciences
IS - 2
M1 - e2021JG006697
ER -