Contrasting Performance of the Remotely-Derived GPP Products over Different Climate Zones across China

Precise quantification of terrestrial gross primary production (GPP) has been recognized as one of the most important components in understanding the carbon balance between the biosphere and the atmosphere. In recent years, although many large-scale GPP estimates from satellite data and ecosystem models have been generated, few attempts have been made to compare the different GPP products at national scales, particularly for various climate zones. In this study, two of the most widely-used GPP datasets were systematically compared over the eight climate zones across China’s terrestrial ecosystems from 2001 to 2015, which included the moderate resolution imaging spectroradiometer (MODIS) GPP and the breathing Earth system simulator (BESS) GPP products. Additionally, the coarse (0.05o) GPP estimates from the vegetation photosynthesis model (VPM) at the same time scale were used for auxiliary analysis with the two products. Both MODIS and BESS products exhibited a decreasing trend from the southeast region to the northwest inland. The largest GPP was found in the tropical humid region with 5.49 g C m−2 d−1 and 5.07 g C m−2 d−1 for MODIS and BESS, respectively, while the lowest GPP was distributed in the warm temperate arid region, midtemperate semiarid region and plateau zone. Meanwhile, the work confirmed that all these GPP products showed apparent seasonality with the peaks in the summertime. However, large differences were found in the interannual variations across the three GPP products over different climate regions. Generally, the BESS GPP agreed better than the MODIS GPP when compared to the seasonal and interannual variations of VPM GPP. Furthermore, the spatial correlation analysis between terrestrial GPP and the climatic factors, including temperature and precipitation, indicated that natural rainfall dominated the variability in GPP of Northern China, such as the midtemperate semiarid region, while temperature was a key controlling factor in the Southern China and the Tibet Plateau area.

[1]  Philippe Ciais,et al.  Contribution of climate change and rising CO2 to terrestrial carbon balance in East Asia: A multi-model analysis , 2011 .

[2]  Philippe Ciais,et al.  Dominant role of plant physiology in trend and variability of gross primary productivity in North America , 2017, Scientific Reports.

[3]  Rebekka R. E. Artz,et al.  Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review. , 2018, The Science of the total environment.

[4]  Sun Jian-qi,et al.  Drought Response to Air Temperature Change over China on the Centennial Scale , 2015 .

[5]  Yujie Wang,et al.  Scientific Impact of MODIS C5 Calibration Degradation and C6+ Improvements , 2014 .

[6]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): overview and key results , 1999 .

[7]  D. O. Hall,et al.  The global carbon sink: a grassland perspective , 1998 .

[8]  Hideki Kobayashi,et al.  Integration of MODIS land and atmosphere products with a coupled‐process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales , 2011 .

[9]  Xiaohua Pan,et al.  Trends in temperature extremes during 1951–1999 in China , 2003 .

[10]  Xiangming Xiao,et al.  Modeling gross primary production of maize cropland and degraded grassland in northeastern China , 2010 .

[11]  Atul K. Jain,et al.  Climate‐driven uncertainties in modeling terrestrial gross primary production: a site level to global‐scale analysis , 2014, Global change biology.

[12]  F. Woodward,et al.  Dynamic responses of terrestrial ecosystem carbon cycling to global climate change , 1998, Nature.

[13]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[14]  Shaohong Wu,et al.  Vegetation distribution on Tibetan Plateau under climate change scenario , 2011 .

[15]  A. Arneth,et al.  Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa , 2013 .

[16]  N. He,et al.  Regional variation in carbon sequestration potential of forest ecosystems in China , 2017, Chinese Geographical Science.

[17]  Youngryel Ryu,et al.  Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS) , 2016 .

[18]  Stefan Erasmi,et al.  Effects of canopy photosynthesis saturation on the estimation of gross primary productivity from MODIS data in a tropical forest , 2012 .

[19]  Lunche Wang,et al.  Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data , 2014, Remote. Sens..

[20]  Andrew E. Suyker,et al.  Uncertainty in simulating gross primary production of cropland ecosystem from satellite-based models , 2015 .

[21]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[22]  S. Running,et al.  Numerical Terradynamic Simulation Group 1-2017 Decreasing net primary production due to drought and slight decreases in solar radiation in China from 2000 to 2012 , 2018 .

[23]  Guihua Liu,et al.  Tracking Ecosystem Water Use Efficiency of Cropland by Exclusive Use of MODIS EVI Data , 2015, Remote. Sens..

[24]  Markus Reichstein,et al.  Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms , 2016 .

[25]  M. Grubb,et al.  A review of Chinese CO2 emission projections to 2030: the role of economic structure and policy , 2015 .

[26]  Xuguang Tang,et al.  Potential of the remotely-derived products in monitoring ecosystem water use efficiency across grasslands in Northern China , 2019, International Journal of Remote Sensing.

[27]  Shaohong Wu,et al.  Responses of vegetation distribution to climate change in China , 2014, Theoretical and Applied Climatology.

[28]  K. Pilegaard,et al.  How is water-use efficiency of terrestrial ecosystems distributed and changing on Earth? , 2014, Scientific Reports.

[29]  R. Myneni,et al.  Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982–2011 by using PDSI indices and agriculture drought survey data , 2016 .

[30]  Liang Zhao,et al.  Spatial patterns and climate drivers of carbon fluxes in terrestrial ecosystems of China , 2013, Global change biology.

[31]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[32]  M. S. Moran,et al.  Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence , 2014, Proceedings of the National Academy of Sciences.

[33]  Andrew E. Suyker,et al.  Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data , 2008, Agricultural and Forest Meteorology.

[34]  P. Ciais,et al.  Elevated atmospheric CO2 negatively impacts photosynthesis through radiative forcing and physiology‐mediated climate feedback , 2017 .

[35]  Y. Zhang,et al.  Extreme Climate in China: Facts, Simulation and Projection , 2012 .

[36]  Yiqi Luo,et al.  Global patterns of extreme drought-induced loss in land primary production: Identifying ecological extremes from rain-use efficiency. , 2018, The Science of the total environment.

[37]  Changsheng Li,et al.  Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data , 2002 .

[38]  Jinwei Dong,et al.  A global moderate resolution dataset of gross primary production of vegetation for 2000–2016 , 2017, Scientific Data.

[39]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[40]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[41]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[42]  D. Hollinger,et al.  MODELING GROSS PRIMARY PRODUCTION OF AN EVERGREEN NEEDLELEAF FOREST USING MODIS AND CLIMATE DATA , 2005 .

[43]  D. Pury,et al.  Simple scaling of photosynthesis from leaves to canopies without the errors of big‐leaf models , 1997 .

[44]  Maosheng Zhao,et al.  Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 , 2010, Science.

[45]  Yoshiko Kosugi,et al.  Estimation of light-use efficiency through a combinational use of the photochemical reflectance index and vapor pressure deficit in an evergreen tropical rainforest at Pasoh, Peninsular Malaysia , 2014 .

[46]  J. Monteith SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[47]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[48]  Sandra A. Brown Measuring carbon in forests: current status and future challenges. , 2002, Environmental pollution.

[49]  Yingnian Li,et al.  Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China , 2011 .

[50]  U. KyawThaPaw Mathematical analysis of the operative temperature and energy budget , 1987 .

[51]  W. Dong,et al.  Response of the starting dates and the lengths of seasons in Mainland China to global warming , 2010 .

[52]  Jiyuan Liu,et al.  The Performances of MODIS-GPP and -ET Products in China and Their Sensitivity to Input Data (FPAR/LAI) , 2014, Remote. Sens..

[53]  Y. Guirui,et al.  The Changes of Net Primary Productivity in Chinese Terrestrial Ecosystem: Based on Process and Parameter Models , 2012 .

[54]  Jinwei Dong,et al.  Canopy and climate controls of gross primary production of Mediterranean-type deciduous and evergreen oak savannas , 2016 .

[55]  Mingguo Ma,et al.  Estimation of gross primary production over the terrestrial ecosystems in China , 2013 .

[56]  Tao Wang,et al.  Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years , 2018, Global change biology.

[57]  W. Oechel,et al.  Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants , 2015 .

[58]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[59]  R. Lu,et al.  A review of recent studies on extreme heat in China , 2016 .

[60]  U. KyawThaPaw,et al.  Applications of solutions to non-linear energy budget equations , 1988 .

[61]  Xiangming Xiao,et al.  Modeling gross primary productivity for winter wheat―maize double cropping system using MODIS time series and CO2 eddy flux tower data , 2009 .

[62]  Shiping Chen,et al.  Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems , 2019, Remote. Sens..

[63]  L. Guanter,et al.  Consistency Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production of Vegetation in North America , 2016 .

[64]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[65]  Jing Xie,et al.  A comprehensive assessment of MODIS-derived GPP for forest ecosystems using the site-level FLUXNET database , 2015, Environmental Earth Sciences.

[66]  W. Ju,et al.  Changes of net primary productivity in China during recent 11 years detected using an ecological model driven by MODIS data , 2013, Frontiers of Earth Science.

[67]  T. A. Black,et al.  Can a satellite-derived estimate of the fraction of PAR absorbed by chlorophyll (FAPARchl) improve predictions of light-use efficiency and ecosystem photosynthesis for a boreal aspen forest? , 2009 .

[68]  F. Gao,et al.  Estimation of Crop Gross Primary Production (GPP): Fapar(sub Chl) Versus MOD15A2 FPAR , 2014 .

[69]  M. Lomas,et al.  Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends , 2013, Global change biology.