Towards operational remote sensing of forest carbon balance across Northern Europe

Abstract

Abstract.Monthly averages of ecosystem respiration (ER), gross primary production (GPP) and net ecosystem exchange (NEE) over Scandinavian forest sites were estimated using regression models driven by air temperature (AT), absorbed photosynthetically active radiation (APAR) and vegetation indices. The models were constructed and evaluated using satellite data from Terra/MODIS and measured data collected at seven flux tower sites in northern Europe. Data used for model construction was excluded from the evaluation. Relationships between ground measured variables and the independent variables were investigated.It was found that the enhanced vegetation index (EVI) at 250 m resolution was highly noisy for the coniferous sites, and hence, 1 km EVI was used for the analysis. Linear relationships between EVI and the biophysical variables were found: correlation coefficients between EVI and GPP, NEE, and AT ranged from 0.90 to 0.79 for the deciduous data, and from 0.85 to 0.67 for the coniferous data. Due to saturation, there were no linear relationships between normalized difference vegetation index (NDVI) and the ground measured parameters found at any site. APAR correlated better with the parameters in question than the vegetation indices. Modeled GPP and ER were in good agreement with measured values, with more than 90% of the variation in measured GPP and ER being explained by the coniferous models. The site-specific respiration rate at 10 • C (R 10 ) was needed Correspondence to: P. Olofsson (olofsson@bu.edu) for describing the ER variation between sites. Even though monthly NEE was modeled with less accuracy than GPP, 61% and 75% (dec. and con., respectively) of the variation in the measured time series was explained by the model. These results are important for moving towards operational remote sensing of forest carbon balance across Northern Europe.

High-latitude tree growth and satellite vegetation indices: Correlations and trends in Russia and Canada (1982–2008)

“…Recent efforts to link NDVI with ground‐based measurements of vegetation productivity have met with mixed success. In Scandinavia, mean monthly MODIS NDVI and flux tower GPP showed moderate correlations (r = 0.7–0.79) at seven forested sites; however, NDVI saturation during periods of high productivity (NDVI > 0.9) was a noticeable issue [ Olofsson et al , 2007]. At three flux tower sites located in Southeast Asian tropical forests, Huete et al [2008] found that the relationship between NDVI and gross ecosystem production varied considerably with forest type (r 2 = 0.00–0.53).…”

“…At three flux tower sites located in Southeast Asian tropical forests, Huete et al [2008] found that the relationship between NDVI and gross ecosystem production varied considerably with forest type (r 2 = 0.00–0.53). Satellite vegetation indices, both NDVI and the enhanced vegetation index (EVI), show stronger associations with tower measurements in forests with seasonal, rather than evergreen, canopy cover [ Olofsson et al , 2007; Huete et al , 2008]. NDVI and EVI are related to productivity via light absorption, though differences in canopy phenology will affect the degree to which light absorption is biochemically decoupled from utilization for carbon assimilation [ Goetz and Prince , 1996].…”

Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data

“…Remote sensing variables, particularly vegetation indices, do not directly represent carbon fluxes processes (Jung et al, 2008), but as shown previously, they are statistically related to ecosystem fluxes (Olofsson et al, 2008;Rahman, Sims, Cordova, & El-Masri, 2005). Vegetation indices are calculated using measured reflectances in specific spectral bands that are related to some chemical and physical properties of the vegetation.…”

“…Vegetation indices are calculated using measured reflectances in specific spectral bands that are related to some chemical and physical properties of the vegetation. For example, greenness indices such as the Normalised Difference Vegetation Index (NDVI) or the Enhanced difference Vegetation Index (EVI) (Olofsson et al, 2008;Sims et al, 2008) are related to the amount of green biomass (e.g., leaf area index, LAI), whereas water indices such as the Normalised Difference Water Index (NDWI) (Gao, 1996) provide information on the canopy water content. Remote sensing data are also used as the basis to derive the land cover maps that are used in modelling exercises when the model parameterisation is specific for a Plant Functional Type (PFT).…”

Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data

“…The first type of studies proposed new models (different for LUEbased) to estimate GPP from remote sensing and showed how well the models estimated GPP (Olofsson et al, 2008;Sims et al, 2008;Schubert et al, 2012). Some of the models are simpler than the LUE approach and have been justified based on the claim that one or more parametrizations used in the LUE approach are not required (Gitelson et al, 2006;Sims et al, 2008;Jung et al, 2008;Jahan and Gan, 2009;Ueyama et al, 2010;Wu et al, 2011;Sjöström et al, 2011;Sakamoto et al, 2011;Hashimoto et al, 2012).…”