Abstract
Quantification of net ecosystem carbon exchange (NEE) between the atmosphere and vegetation is of great importance for regional and global studies of carbon balance. The eddy covariance technique can quantify carbon budgets and the effects of environmental controls for many forest types across the continent but it only provides integrated CO2 flux measurements within tower footprints and need to be scaled up to large areas in combination with remote sensing observations. In this study we compare a multiple-linear regression (MR) model which relates enhanced vegetation index and land surface temperature derived from the moderate resolution imaging spectroradiometer (MODIS), and photosynthetically active radiation with the site-level NEE, for estimating carbon flux exchange between the ecosystem and the environment at the deciduous-dominated Harvard Forest to three other methods proposed in the literature. Six years (2001–2006) of eddy covariance and MODIS data are used and results show that the MR model has the best performance for both training (2001–2004, R2 = 0.84, RMSE = 1.33 g Cm−2 day−1) and validation (2005–2006, R2 = 0.76, RMSE = 1.54 g Cm−2 day−1) datasets comparing to the other ones. It provides the potential to estimate carbon flux exchange across different ecosystems at various time intervals for scaling up plot-level NEE of CO2 to large spatial areas.
Highlights
Quantification of the net carbon exchange between atmosphere and terrestrial ecosystem in global carbon cycle is becoming important with future potential sequestration influenced by increased atmospheric CO2 and changing climate (Nemani et al 2003)
The temperature and greenness (TG) model developed by Sims et al (2008) that based on the moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI) and land surface temperature (LST) product is validated in a wide diversity of natural vegetation including both deciduous and evergreen forests across North America
These studies demonstrate that greenness indices like enhanced vegetation indices (EVI) and land surface water index (LSWI), land surface temperature (LST), photosynthetically active radiation (PAR) are reliable proxies indicating plant phenological stages, canopy stresses and environmental conditions in estimation of carbon uptake by terrestrial ecosystems referred to as gross ecosystem exchange (GEE), but the ability of these biophysical indices in capturing the net carbon uptake by forest ecosystems namely net ecosystem carbon exchange (NEE) is less well known
Summary
Quantification of the net carbon exchange between atmosphere and terrestrial ecosystem in global carbon cycle is becoming important with future potential sequestration influenced by increased atmospheric CO2 and changing climate (Nemani et al 2003). The temperature and greenness (TG) model developed by Sims et al (2008) that based on the MODIS EVI and land surface temperature (LST) product is validated in a wide diversity of natural vegetation including both deciduous and evergreen forests across North America These studies demonstrate that greenness indices like enhanced vegetation indices (EVI) and land surface water index (LSWI), land surface temperature (LST), photosynthetically active radiation (PAR) are reliable proxies indicating plant phenological stages, canopy stresses (air temperature, soil moisture, vapor pressure deficit) and environmental conditions (incoming solar radiation) in estimation of carbon uptake by terrestrial ecosystems referred to as gross ecosystem exchange (GEE), but the ability of these biophysical indices in capturing the net carbon uptake by forest ecosystems namely NEE is less well known. This study will explore the implication and ability of eddy covariance and remote sensing observations for quantifying net carbon exchange between the atmosphere and forest ecosystems
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