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Towards a remote-sensing-driven model of isoprene emissions from Alpine tundra
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 2 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 2 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 2 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.
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The main goal for the “Towards better exploitation of Satellite data for monitoring Air Quality in Norway using
downscaling techniques” (Sat4AQN) project was to evaluate the potential of spatially downscaling satellite data using a
high-resolution Chemical Transport Model (CTM) to spatial scales that are more relevant for monitoring air quality in
urban areas and regional background sites in Norway. For this demonstration project, we focused on satellite aerosol
optical density (AOD) and particulate matter (PM) estimates.
NILU
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Towards crowd-sourced air quality and physical activity monitoring by a low-cost mobile platform. Lecture Notes in Computer Science, 9677
2016
Towards operational satellite based atmospheric monitoring in Norway SatMoNAir. NILU OR
SatMoNAir prosjektet [NSC kontrakt nr. JOP.12.12.2] bygger på et tidligere NRS følgemiddelprosjekt kalt 'Roadmap towards EarthCARE and Sentinel 5 precursors', der NILU og met.no utviklet en strategi for å være forberedt på framtidige satellittobservasjoner knyttet til nasjonal klimaovervåkning, værvarsel og forskning. Det har blitt fokusert på tre områder: a.
Aerosol¿ klimaeffekter i Skandinavia og polare strøk, b. Bruk av satellitter i nasjonal overvåkning av ozonlaget, c. Satellittbaserte målinger av luftkvalitet til bruk i EMEP rapportering. Resultater fra dette arbeidet blir beskrevet i denne rapporten. Prosjektet har vært en viktig støtte for NILUs nasjonale overvåkning av klimagasser og aerosoler (Myhre et al., 2012) og atmosfærens ozonlag (Svendby et al., 2012). Resultater har også blitt rapportert til EMEP.
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