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Towards a remote-sensing-driven model of isoprene emissions from Alpine tundra

Westergaard-Nielsen, Andreas ; Maigaard, R S ; Davie-Martin, Cleo L. ; Seco, Roger ; Holst, Thomas LU ; Pirk, Norbert ; Laursen, S N and Rinnan, Riikka (2025) In Environmental Research Letters 20(10).
Abstract
This study investigates isoprene emissions in a high-latitude Alpine tundra ecosystem, focusing on using near-field remote sensing of surface temperatures, the photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI), and meteorological measurements to model these emissions. Isoprene is a key biogenic volatile organic compound (BVOC) emitted by select plants, which can impact atmospheric chemistry and climate. Increased temperatures, particularly in high latitudes, may enhance isoprene emissions due to extended growing seasons and heightened plant stress. The research was conducted in Finse, Norway, where isoprene and CO2 fluxes were measured with eddy covariance alongside spectral and... (More)
This study investigates isoprene emissions in a high-latitude Alpine tundra ecosystem, focusing on using near-field remote sensing of surface temperatures, the photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI), and meteorological measurements to model these emissions. Isoprene is a key biogenic volatile organic compound (BVOC) emitted by select plants, which can impact atmospheric chemistry and climate. Increased temperatures, particularly in high latitudes, may enhance isoprene emissions due to extended growing seasons and heightened plant stress. The research was conducted in Finse, Norway, where isoprene and CO2 fluxes were measured with eddy covariance alongside spectral and meteorological data, and surface temperature. A random forest (RF) model was developed to predict isoprene fluxes, considering the variable importance of different environmental factors. The results showed that surface temperature and CO2 flux were consistently important predictors, across three differential temporal data aggregations (hourly, daily, weekly), while the PRI demonstrated low predictive power, possibly due to the heterogeneous vegetation and variable light conditions. The NDVI was more effective than anticipated, likely linked to phenological changes in vegetation. Model performance varied with temporal resolution, with weekly data achieving the highest predictive accuracy (R2 up to 0.76). The RF model accurately reflected seasonal emission patterns but underestimated short-term peaks, suggesting the potential to combine machine learning with process-based modelling. This research highlights the promise of proxy data from remote sensing for scaling BVOC emission models to regional levels, essential for understanding climate impacts in Arctic ecosystems. (Less)
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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Environmental Research Letters
volume
20
issue
10
article number
104060
publisher
IOP Publishing
ISSN
1748-9326
DOI
10.1088/1748-9326/ae02a9
language
English
LU publication?
yes
id
5f055aed-77ee-4067-a7f7-2a76e9984ef3
date added to LUP
2025-09-24 11:17:19
date last changed
2025-09-24 13:36:50
@article{5f055aed-77ee-4067-a7f7-2a76e9984ef3,
  abstract     = {{This study investigates isoprene emissions in a high-latitude Alpine tundra ecosystem, focusing on using near-field remote sensing of surface temperatures, the photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI), and meteorological measurements to model these emissions. Isoprene is a key biogenic volatile organic compound (BVOC) emitted by select plants, which can impact atmospheric chemistry and climate. Increased temperatures, particularly in high latitudes, may enhance isoprene emissions due to extended growing seasons and heightened plant stress. The research was conducted in Finse, Norway, where isoprene and CO<sub>2</sub> fluxes were measured with eddy covariance alongside spectral and meteorological data, and surface temperature. A random forest (RF) model was developed to predict isoprene fluxes, considering the variable importance of different environmental factors. The results showed that surface temperature and CO<sub>2</sub> flux were consistently important predictors, across three differential temporal data aggregations (hourly, daily, weekly), while the PRI demonstrated low predictive power, possibly due to the heterogeneous vegetation and variable light conditions. The NDVI was more effective than anticipated, likely linked to phenological changes in vegetation. Model performance varied with temporal resolution, with weekly data achieving the highest predictive accuracy (R<sup>2</sup> up to 0.76). The RF model accurately reflected seasonal emission patterns but underestimated short-term peaks, suggesting the potential to combine machine learning with process-based modelling. This research highlights the promise of proxy data from remote sensing for scaling BVOC emission models to regional levels, essential for understanding climate impacts in Arctic ecosystems.}},
  author       = {{Westergaard-Nielsen, Andreas and Maigaard, R S and Davie-Martin, Cleo L. and Seco, Roger and Holst, Thomas and Pirk, Norbert and Laursen, S N and Rinnan, Riikka}},
  issn         = {{1748-9326}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{10}},
  publisher    = {{IOP Publishing}},
  series       = {{Environmental Research Letters}},
  title        = {{Towards a remote-sensing-driven model of isoprene emissions from Alpine tundra}},
  url          = {{http://dx.doi.org/10.1088/1748-9326/ae02a9}},
  doi          = {{10.1088/1748-9326/ae02a9}},
  volume       = {{20}},
  year         = {{2025}},
}