Remote Sensing (Mar 2024)

Environmentally Induced Snow Transmittance Variations in the Photosynthetic Spectral Domain: Photobiological Implications for Subnivean Vegetation under Climate Warming Conditions

  • Gladimir V. G. Baranoski,
  • Petri M. Varsa

DOI
https://doi.org/10.3390/rs16050927
Journal volume & issue
Vol. 16, no. 5
p. 927

Abstract

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Variations in the productivity of subnivean vegetation can substantially affect the ecology of regions more susceptible to increasing warming levels and lead to significant feedback effects on the global climate. Due to its importance, this topic is at the center of a broad scope of interdisciplinary studies supported by field and remote sensing observations. However, the current knowledge about environmental factors affecting the penetration of photosynthetically active radiation (PAR) through snow is still constrained by the paucity of transmittance data. In this work, we aim to further the understanding about these interconnected processes. We conduct a systematic investigation about the effects of independent and combined changes in key nivological characteristics, namely thickness, saturation, density and grain size, on snow transmittance in the photosynthetic spectral domain. Our investigation is carried out through controlled in silico (computational) experiments supported by measured radiometric data. Its outcomes unveil fundamental quantitative and qualitative trends related to the role played by these nivological characteristics on the spectral quality of transmitted PAR, which is quantified in terms of red to blue (R/B), red to far-red (R/FR) and blue to far-red (B/FR) ratios. These trends include increases in the R/B ratio as well as decreases in the R/FR and B/FR ratios following thickness reductions or grain size increases, with opposite variations in these ratios being observed for saturation or density increases. Accordingly, the pairing of our findings with in situ and remotely collected information contributes to cement the scientific foundation required for the effective assessment of cause-effect loops linking accentuated vegetation greening to accelerated rates of snow cover recession.

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