{'fraction_non_alphanumeric': 0.03007518796992481, 'fraction_numerical': 0.006766917293233083, 'mean_word_length': 5.219626168224299, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Estimating gross primary production of a semi-arid grassland ecosystem with a satellite-driven model, VPM

Estimation of gross primary production (GPP) from remote sensing data is an important approach to study regional or global carbon cycle. In this study, a satellite-driven model, vegetation photosynthesis model (VPM) was introduced to estimate gross primary production (GPP) of the semi-arid grassland ecosystem for the growing season (2006) in North China. Meanwhile, observed GPP derived from eddy covariance flux data were used to critically evaluate the performance of the model. As defined by the input variables of VPM, two improved vegetation indices (enhanced vegetation index (EVI) and land surface water index (LSWI)) derived from the standard data product MOD09A1 of Moderate Resolution Imaging Spectroradimeter (MODIS), air temperature and photosynthetic active radiation at the flux site, were included for the model calculating. The seasonal dynamics of GPP predicted by the VPM model agreed well with estimated GPP from the eddy flux tower, and simulation with time step of 8-day was better than with 1-day time step. Results of this study demonstrate that the satellite-driven VPM has potential for estimating site-level or regional grassland GPP, and might be an effective tool for scaling-up carbon fluxes.