| Since the industrial revolution,the use of fossil energy such as coal and oil has gradually increased,and the emission concentration of greenhouse gases represented by carbon dioxide has also become higher and higher,which has led to an increasingly obvious trend of global warming,and the global carbon cycle has also changed with climate change..Net primary productivity（NPP）on land is the most important component flux in the global carbon cycle and plays a crucial role in connecting the global carbon and water cycles and energy balance between the atmosphere,biosphere,hydrosphere,and lithosphere.Although various methods and models have been developed to estimate land NPP at different scales,there is still significant variability and uncertainty in long-term global NPP simulations.Therefore,to better understand and accurately estimate the spatial and temporal patterns of land NPP,we incorporated leaf maintenance respiration,fine root maintenance respiration,live wood maintenance respiration,and growth respiration modules into the MODIS MOD17A2/A3 algorithm-based global Gross Primary Productivity（GPP）that we have developed,and ultimately simulated global net primary productivity（NPP）.We optimized the growth respiration parameters by comparing the results with flux station data and NPP data from other models.Furthermore,we optimized the Q10 parameter for the land regions of China based on actual sampling data,resulting in higher accuracy when simulating the region with the model.The main research conclusions are as follows:（1）After changing the parameter for autotrophic respiration in MODIS from Rg=25%NPP to Rg=30%NPP,the model’s simulation results are closer to the measured values from flux towers.From 2001to 2020,this study used LAI data and climate data from GLDAS 2.1 to simulate global land NPP at a spatial resolution of 0.25°and a temporal resolution of 3 hours using a process-based integrated model.During the study period from 2001 to 2020,the annual variation range of global land NPP was between59.48 Pg C/yr and 65.64 Pg C/yr,with an average of 62.75 Pg C/yr,which falls within the currently recognized range of annual average NPP of 50-80 Pg C/yr.Over these twenty years,it exhibited a significant increasing trend（p<0.01）,with a global average growth rate of 0.32 Pg C/yr.（2）During the study period from 2001 to 2020,the maximum annual average NPP value globally was 3209 g C/m2/yr,located in the Amazon rainforest region.The majority of higher annual NPP values were around 2000 g C/m2/yr and were mainly distributed along the equator,primarily in tropical rainforest areas such as the Amazon rainforest,Congo Basin,and Southeast Asian tropical rainforests.The lowest NPP values were predominantly found in cold or arid regions.Most regions globally showed an increasing trend in NPP over the years,and these trends were statistically significant at a 95%confidence level.These significant growth trends were mainly observed in the temperate and boreal zones as well as high-altitude areas.Conversely,regions with significant declining trends were scattered within tropical rainforests such as the Congo Basin,Amazon rainforest,and Southeast Asia.（3）During the period from 2001 to 2020,the average annual NPP of different vegetation types changed.The average annual NPP of each vegetation type from high to low is as follows:evergreen broadleaf forest（1.65 Kg C/m2/yr）,deciduous broadleaf forest（0.95 Kg C/m2/yr）,mixed forest（0.79 Kg C/m2/yr）,multi-tree tropical grassland（0.74 Kg C/m2/yr）,farmland（0.64 Kg C/m2/yr）,tropical sparse tree savanna（0.54 Kg C/m2/yr）,deciduous coniferous forest（0.53 Kg C/m2/yr）,evergreen coniferous forest（0.48 Kg C/m2/yr）,grassland（0.20 Kg C/m2/yr）,sparse shrubland（0.11 Kg C/m2/yr）,and dense shrubland（0.06 Kg C/m2/yr）.During the 20-year period,except for evergreen broadleaf forests,the annual NPP of other vegetation types showed a significant increasing trend,from highest to lowest:sparse shrubland（50.5%）,evergreen coniferous forest（30.1%）,deciduous coniferous forest（27.8%）,grassland（22.5%）,mixed forest（19.8%）,dense shrubland（18.3%）,multi-tree tropical grassland（15.3%）,tropical sparse tree savanna（13.7%）,farmland（10.9%）,deciduous broadleaf forest（6%）,and finally,evergreen broadleaf forest（-3%）.（4）From 2019 to 2021,large-scale field sampling was conducted on terrestrial ecosystems in China,collecting 469 species and 2698 leaves from 23 cities.The selection of sampling sites included different latitudes,longitudes,elevations,and involved various types of land vegetation such as evergreen broadleaf forest,evergreen needleleaf forest,deciduous broadleaf forest,grassland,sparse shrubland,and dense shrubland.The LI-6400 portable photosynthesis system was used to measure the respiration rate of leaves,and the Q10values in the temperature range of 10-45℃for each sample were calculated based on three Q10models:exponential model,Arrhenius model,and thermodynamic model.The results showed that the temperature correction formula of Q10in MODIS:Q10=3.22-0.046*Tavg was not applicable in China and did not match the actual values.Assuming that the Q10 value for the same type of land vegetation is the same across the country and setting it as the average value of the measured data for that type of land vegetation,it was shown to be reasonable in our model by comparing with flux station data.（5）The average Q10 values of different land cover types obtained from field measurements are used as fixed parameters for each type,and they are incorporated into the model to calculate China’s annual NPP.During the study period from 2001 to 2020,the annual variation range of China’s terrestrial NPP was3.11 Pg C/yr to 3.59 Pg C/yr,with an average of 3.43 Pg C/yr.Over the twenty-year period,it showed a significant increasing trend（p<0.01）,with a national average growth rate of 0.0243 Pg C/yr. |
