Estimation of carbon fluxes from eddy covariance data and satellite-derived vegetation indices in a karst grassland (Podgorski Kras, Slovenia)

Noumonvi, Koffi Dodji (avtor), Ferlan, Mitja (avtor), Eler, Klemen (avtor), Alberti, Giorgio (avtor), Peressotti, Alessandro (avtor), Cerasoli, Sofia (avtor)

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R$^2$), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

• Jezik: Angleški jezik
• Ključne besede: eddy covariance, carbon flux, GPP, NEE, vegetation indices, remote sensing, satellite data, GPP map
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: BF - Biotehniška fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2019
• Št. strani: 21 str.
• Številčenje: Vol. 11, iss. 6, art. 649
• PID: 20.500.12556/RUL-131932
• UDK: 630*58
• ISSN pri članku: 2072-4292
• DOI: 10.3390/rs11060649
• COBISS.SI-ID: 5360038
• Datum objave v RUL: 06.10.2021

Gradivo je del revije

• Naslov: Remote sensing
• Skrajšan naslov: Remote sens.
• Založnik: MDPI
• ISSN: 2072-4292
• COBISS.SI-ID: 32345133

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 16.03.2019

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: mikrometeorološke metode, metoda kovariance vrtincev, tok ogljika, GPP, NEE, modeliranje, vegetacijski indeksi, daljinsko zaznavanje