Abstract
Accurate quantification of gross primary production (GPP) at regional and global scales is essential for carbon budgets and climate change studies. Five models, the vegetation photosynthesis model (VPM), the temperature and greenness model (TG), the alpine vegetation model (AVM), the greenness and radiation model (GR), and the MOD17 algorithm, were tested and calibrated at eight sites in China during 2003–2005. Results indicate that the first four models provide more reliable GPP estimation than MOD17 products/algorithm, although MODIS GPP products show better performance in grasslands, croplands, and mixed forest (MF). VPM and AVM produce better estimates in forest sites (R2 = 0.68 and 0.67, respectively); AVM and TG models show satisfactory GPP estimates for grasslands (R2 = 0.91 and 0.9, respectively). In general, the VPM model is the most suitable model for GPP estimation for all kinds of land cover types in China, with R2 higher than 0.34 and root mean square error (RMSE) lower than 48.79%. The relationships between eddy CO2 flux and model parameters (Enhanced Vegetation Index (EVI), photosynthetically active radiation (PAR), land surface temperature (LST), air temperature, and Land Surface Water Index (LSWI)) are further analyzed to investigate the model’s application to various land cover types, which will be of great importance for studying the effects of climatic factors on ecosystem performances.
Highlights
Gross primary production (GPP), defined as the total amount of carbon dioxide fixed by plants in photosynthesis, is the first step in the input of atmospheric CO2 to terrestrial ecosystems [1,2,3]
MOD17 for croplands and mixed forest (MF) show relative high correlations with the measured fluxes (R2 are 0.8 and 0.76, respectively); low correlations have been found for ENF and EBF (R2 are 0.44 and 0.21, respectively)
These differences may be due to the fact that the MOD17 algorithm generally has a good performance when the measured GPP value is relatively small (GPP_EC < 40gC/m2/eight-day or lower)
Summary
Gross primary production (GPP), defined as the total amount of carbon dioxide fixed by plants in photosynthesis, is the first step in the input of atmospheric CO2 to terrestrial ecosystems [1,2,3]. A number of remote sensing-based GPP models have been proposed, including the MOD17 algorithm [6], the vegetation photosynthesis model (VPM) [7], the temperature and greenness (TG) model [8], the physiological principles for predicting growth (3-PG) [9] model, the eddy covariance light use efficiency model (EC-LUE) [10], the vegetation index (VI) model [11], the alpine vegetation model (AVM) [12], and the greenness and radiation model (GR) [13,14], etc. It is essential to analyze the dependence of GPP models on climate variables and compare the model performances in different eco-regions with diverse canopy structures and climate characteristics
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