References

Aalto, J., Scherrer, D., Lenoir, J., Guisan, A., & Luoto, M. (2018). Biogeophysical controls on
soil-atmosphere thermal differences: implications on warming Arctic ecosystems. Environmental Research Letters, 13(7), 074003. https://doi.org/10.1088/1748-9326/aac83e
Aartsma, P., Asplund, J., Odland, A., Reinhardt, S., & Renssen, H. (2021). Microclimatic
comparison of lichen heaths and shrubs: shrubification generates atmospheric heating but subsurface cooling during the growing season.Biogeosciences, 18(5), 1577–1599. https://doi.org/10.5194/bg-18-1577-2021
Adams, W. W., Zarter, C. R., Ebbert, V., & Demmig-Adams, B. (2004). Photoprotective
Strategies of Overwintering Evergreens. BioScience, 54(1), 41. https://doi.org/10.1641/0006-3568(2004)054[0041:PSOOE]2.0.CO;2
Anderson, J. E., Douglas, T. A., Barbato, R. A., Saari, S., Edwards, J. D., & Jones, R. M. (2019).
Linking vegetation cover and seasonal thaw depths in interior Alaska permafrost terrains
using remote sensing. Remote Sensing of Environment , 233 , 111363.
https://doi.org/10.1016/j.rse.2019.111363
Andresen, C. G., & Lougheed, V. L. (2021). Arctic aquatic graminoid tundra responses to
nutrient availability. Biogeosciences, 18(8), 2649–2662. https://doi.org/10.5194/bg-18-2649-2021
Asner, G. P., & Martin, R. E. (2008). Spectral and chemical analysis of tropical forests: Scaling
from leaf to canopy levels. Remote Sensing of Environment,112(10), 3958–3970. https://doi.org/10.1016/j.rse.2008.07.003
Asner, G. P., Martin, R. E., Anderson, C. B., & Knapp, D. E. (2015). Quantifying forest canopy
traits: Imaging spectroscopy versus field survey. Remote Sensing of Environment, 158, 15–27. https://doi.org/10.1016/j.rse.2014.11.011
Assmann, J. J., Myers-Smith, I. H., Kerby, J. T., Cunliffe, A. M., & Daskalova, G. N. (2020).
Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites. Environmental Research Letters,15(12), 125002. https://doi.org/10.1088/1748-9326/abbf7d
Bartsch, A., Höfler, A., Kroisleitner, C., & Trofaier, A. (2016). Land cover mapping in northern
high latitude permafrost regions with satellite data: Achievements and remaining challenges. Remote Sensing, 8(12), 979. https://doi.org/10.3390/rs8120979
Beamish, A., Raynolds, M. K., Epstein, H., Frost, G. V., Macander, M. J., Bergstedt, H., et al.
(2020). Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook. Remote Sensing of Environment, 246, 111872. https://doi.org/10.1016/j.rse.2020.111872
Beamish, A. L., Coops, N., Chabrillat, S., & Heim, B. (2017). A Phenological Approach to
Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope, Alaska. Remote Sensing, 9(11), 1200. https://doi.org/10.3390/rs9111200
Beckett, R. P., Minibayeva, F., Solhaug, K. A., & Roach, T. (2021). Photoprotection in lichens:
adaptations of photobionts to high light. The Lichenologist,53(1), 21–33. https://doi.org/10.1017/S0024282920000535
Berner, L. T., Massey, R., Jantz, P., Forbes, B. C., Macias-Fauria, M., Myers-Smith, I., et al.
(2020). Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nature Communications, 11(1), 4621. https://doi.org/10.1038/s41467-020-18479-5
Bhatt, U. S., Walker, D. A., Raynolds, M. K., Walsh, J. E., Bieniek, P. A., Cai, L., et al. (2021).
Climate drivers of Arctic tundra variability and change using an indicators framework, 16(5), 055019. https://doi.org/10.1088/1748-9326/abe676
Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Normand, S., Rüger, N., Beck, P. S. A.,
et al. (2018). Plant functional trait change across a warming tundra biome. Nature, 562(7725), 57–62. https://doi.org/10.1038/s41586-018-0563-7
Black, K. L., Wallace, C. A., & Baltzer, J. L. (2021). Seasonal thaw and landscape position
determine foliar functional traits and whole-plant water use in tall shrubs on the low arctic tundra. New Phytologist, 231(1), 94–107. https://doi.org/10.1111/nph.17375
Blok, D., Heijmans, M. M. P. D., Schaepman-Strub, G., van Ruijven, J., Parmentier, F. J. W.,
Maximov, T. C., & Berendse, F. (2011). The Cooling Capacity of Mosses: Controls on Water and Energy Fluxes in a Siberian Tundra Site.Ecosystems, 14(7), 1055–1065. https://doi.org/10.1007/s10021-011-9463-5
Boelman, N. T., Rocha, A. V., & Shaver, G. R. (2011). Understanding burn severity sensing in
Arctic tundra: exploring vegetation indices, suboptimal assessment timing and the impact of increasing pixel size. International Journal of Remote Sensing, 32(22), 7033–7056. https://doi.org/10.1080/01431161.2011.611187
Bokhorst, S., Tømmervik, H., Callaghan, T. V., Phoenix, G. K., & Bjerke, J. W. (2012).
Vegetation recovery following extreme winter warming events in the sub-Arctic
estimated using NDVI from remote sensing and handheld passive proximal sensors. Environmental and Experimental Botany, 81, 18–25. https://doi.org/10.1016/j.envexpbot.2012.02.011
Bratsch, S. N., Epstein, H. E., Buchhorn, M., & Walker, D. A. (2016). Differentiating among
Four Arctic Tundra Plant Communities at Ivotuk, Alaska Using Field Spectroscopy. Remote Sensing, 8(1), 51. https://doi.org/10.3390/rs8010051
Bubier, J. L., Rock, B. N., & Crill, P. M. (1997). Spectral reflectance measurements of boreal
wetland and forest mosses. Journal of Geophysical Research: Atmospheres, 102(D24), 29483–29494. https://doi.org/10.1029/97JD02316
Buchhorn, M., Raynolds, M. K., & Walker, D. A. (2016). Influence of BRDF on NDVI and
biomass estimations of Alaska Arctic tundra, 11(12), 125002. https://doi.org/10.1088/1748-9326/11/12/125002
CAVM Team. (2003). Circumpolar Arctic Vegetation Map. Anchorage, Alaska: U.S. Fish and
Wildlife Service. Retrieved from http://www.arcticatlas.org/maps/themes/cp/
Cawse-Nicholson, K., Townsend, P. A., Schimel, D., Assiri, A. M., Blake, P. L., Buongiorno, M.
F., et al. (2021). NASA’s surface biology and geology designated observable: A perspective on surface imaging algorithms. Remote Sensing of Environment, 257, 112349. https://doi.org/10.1016/j.rse.2021.112349
Chapin, F. S. (2003). Effects of Plant Traits on Ecosystem and Regional Processes: a Conceptual
Framework for Predicting the Consequences of Global Change. Annals of Botany, 91(4), 455–463. https://doi.org/10.1093/aob/mcg041
Chapman, J. W., Thompson, D. R., Helmlinger, M. C., Bue, B. D., Green, R. O., Eastwood, M.
L., et al. (2019). Spectral and Radiometric Calibration of the Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG).Remote Sensing, 11(18), 2129. https://doi.org/10.3390/rs11182129
Chasmer, L., Hopkinson, C., Veness, T., Quinton, W., & Baltzer, J. (2014). A decision-tree
classification for low-lying complex land cover types within the zone of discontinuous permafrost. Remote Sensing of Environment,143, 73–84.
Chen, W., Tape, K. D., Euskirchen, E. S., Liang, S., Matos, A., Greenberg, J., & Fraterrigo, J. M.
(2020). Impacts of Arctic Shrubs on Root Traits and Belowground Nutrient Cycles Across a Northern Alaskan Climate Gradient. Frontiers in Plant Science, 11, 1943. https://doi.org/10.3389/fpls.2020.588098
Cooper, E. J. (2014). Warmer Shorter Winters Disrupt Arctic Terrestrial Ecosystems. Annual
Review of Ecology, Evolution, and Systematics, 45(1), 271–295. https://doi.org/10.1146/annurev-ecolsys-120213-091620
Cornelissen, J. H. C., Lang, S. I., Soudzilovskaia, N. A., & During, H. J. (2007). Comparative
Cryptogam Ecology: A Review of Bryophyte and Lichen Traits that Drive Biogeochemistry. Annals of Botany, 99(5), 987–1001. https://doi.org/10.1093/aob/mcm030
Cunliffe, A. M., Anderson, K., Boschetti, F., Brazier, R. E., Graham, H. A., Myers‐Smith, I. H.,
et al. (2021). Global application of an unoccupied aerial vehicle photogrammetry
protocol for predicting aboveground biomass in non‐forest ecosystems.Remote Sensing in Ecology and Conservation, rse2.228. https://doi.org/10.1002/rse2.228
Curran, P. J., Kupiec, J. A., & Smith, G. M. (1997). Remote sensing the biochemical
composition of a slash pine canopy. IEEE Transactions on Geoscience and Remote Sensing, 35(2), 415–420. https://doi.org/10.1109/36.563280
Davidson, S., Santos, M., Sloan, V., Watts, J., Phoenix, G., Oechel, W., & Zona, D. (2016).
Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA. Remote Sensing, 8(12), 978. https://doi.org/10.3390/rs8120978
Demmig-Adams, B., Adams III, W. W., Barker, D. H., Logan, B. A., Bowling, D. R., &
Verhoeven, A. S. (1996). Using chlorophyll fluorescence to assess the fraction of absorbed light allocated to thermal dissipation of excess excitation. Physiologia Plantarum, 98(2), 253–264. https://doi.org/10.1034/j.1399-3054.1996.980206.x
Dietze, M. C., Serbin, S. P., Davidson, C., Desai, A. R., Feng, X., Kelly, R., et al. (2014). A
quantitative assessment of a terrestrial biosphere model’s data needs across North American biomes. Journal of Geophysical Research: Biogeosciences, 119(3), 286–300. https://doi.org/10.1002/2013JG002392
Drolet, G., Wade, T., Nichol, C. J., MacLellan, C., Levula, J., Porcar-Castell, A., et al. (2014). A
temperature-controlled spectrometer system for continuous and unattended measurements of canopy spectral radiance and reflectance.International Journal of Remote Sensing, 35(5), 1769–1785. https://doi.org/10.1080/01431161.2014.882035
Durán, S. M., Martin, R. E., Díaz, S., Maitner, B. S., Malhi, Y., Salinas, N., et al. (2019).
Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient. Science Advances, 5(12), eaaw8114. https://doi.org/10.1126/sciadv.aaw8114
Eitel, J. U. H., Maguire, A. J., Boelman, N., Vierling, L. A., Griffin, K. L., Jensen, J., et al.
(2019). Proximal remote sensing of tree physiology at northern treeline: Do late-season changes in the photochemical reflectance index (PRI) respond to climate or photoperiod? Remote Sensing of Environment,221, 340–350. https://doi.org/10.1016/j.rse.2018.11.022
Eitel, J. U. H., Griffin, K. L., Boelman, N. T., Maguire, A. J., Meddens, A. J. H., Jensen, J., et al.
(2020). Remote sensing tracks daily radial wood growth of evergreen needleleaf trees. Global Change Biology, 26(7), 4068–4078. https://doi.org/10.1111/gcb.15112
Epstein, H. E., Walker, D. A., Raynolds, M. K., Jia, G. J., & Kelley, A. M. (2008). Phytomass
patterns across a temperature gradient of the North American arctic tundra. Journal of Geophysical Research: Biogeosciences,113(G3). https://doi.org/10.1029/2007JG000555
Epstein, H. E., Walker, D. A., Frost, G. V., Raynolds, M. K., Bhatt, U., Daanen, R., et al. (2020).
Spatial patterns of arctic tundra vegetation properties on different soils along the Eurasia Arctic Transect, and insights for a changing Arctic, 16(1), 014008. https://doi.org/10.1088/1748-9326/abc9e3
Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., & Dietze, M. C. (2018).
Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation. Biogeosciences, 15(19), 5801–5830. https://doi.org/10.5194/bg-15-5801-2018
Féret, J.-B., François, C., Gitelson, A., Asner, G. P., Barry, K. M., Panigada, C., et al. (2011).
Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment, 115(10), 2742–2750. https://doi.org/10.1016/j.rse.2011.06.016
Fisher, J. B., Hayes, D. J., Schwalm, C. R., Huntzinger, D. N., Stofferahn, E., Schaefer, K., et al.
(2018). Missing pieces to modeling the Arctic-Boreal puzzle.Environmental Research Letters, 13(2), 020202. https://doi.org/10.1088/1748-9326/aa9d9a
French, N. H. F., Jenkins, L. K., Loboda, T. V., Flannigan, M., Jandt, R., Bourgeau-Chavez, L.
L., & Whitley, M. (2015). Fire in arctic tundra of Alaska: past fire activity, future fire potential, and significance for land management and ecology. International Journal of Wildland Fire,24(8), 1045–1061. https://doi.org/10.1071/WF14167
Gamon, J. A., Peñuelas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks
diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35–44. https://doi.org/10.1016/0034-4257(92)90059-S
Gamon, J. A., Serrano, L., & Surfus, J. S. (1997). The photochemical reflectance index: an
optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia,112(4), 492–501. https://doi.org/10.1007/s004420050337
Gamon, J. A., Somers, B., Malenovský, Z., Middleton, E. M., Rascher, U., & Schaepman, M. E.
(2019). Assessing Vegetation Function with Imaging Spectroscopy.