Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution

Abstract Accurate estimation of gross primary productivity (GPP) is essential for understanding ecosystem function and global carbon cycling. However, there is still substantial uncertainty in the magnitude, spatial distribution, and temporal dynamics of GPP. Using light use efficiency (LUE) models, we conducted a comprehensive analysis of the uncertainty in GPP estimation resulting from various sources: model structure, model parameters, input data, and spatial resolution. We first evaluated the influences of model structures, namely the fraction of absorbed photosynthetically active radiation (FPAR), water scalar (WS), and temperature scalar (TS), on site-level GPP estimates. We then used the Sobol’ sensitivity analysis to quantify the relative contributions of model input variables to the uncertainty in GPP. In addition, we used different land cover and meteorological datasets to examine the effects of input data and spatial resolution on the magnitude and spatiotemporal patterns of GPP. We found that the model structures affected not only model performance but also model parameters in a manner that differed with vegetation type and region. Thus, proper model structures and rigorous model parameterization and calibration should be adopted in GPP modeling. The Sobol’ sensitivity analysis showed that the meteorological drivers including photosynthetically active radiation (PAR) and daily minimum temperature (TMIN) had larger contribution to the uncertainty in simulated GPP than did the surface reflectance-based indices including enhanced vegetation index (EVI) and normalized difference water index (NDWI). At the regional scale, different land cover datasets had the largest impacts on GPP simulations, especially in heterogeneous areas, followed by the scale effects from different spatial resolutions; changing meteorological datasets had the smallest effects. Therefore, more accurate and finer-resolution land cover maps and meteorological datasets are essential for more accurate GPP estimates. Our findings have implications for improving our understanding of the full uncertainty in carbon flux estimates and reducing the uncertainty in carbon cycle simulations.

[1]  Huadong Guo,et al.  Net ecosystem productivity of temperate grasslands in northern China: An upscaling study , 2014 .

[2]  T. Vesala,et al.  On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm , 2005 .

[3]  P. Cox,et al.  Impact of changes in diffuse radiation on the global land carbon sink , 2009, Nature.

[4]  Wenbo Xu,et al.  Modelling gross primary production in the Heihe river basin and uncertainty analysis , 2012 .

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

[6]  Zhuguo Ma,et al.  Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling , 2010 .

[7]  Mathias Disney,et al.  Impact of land cover uncertainties on estimates of biospheric carbon fluxes , 2008 .

[8]  Liu Liang-Yu.  Simulation and correction of spatialscaling effects for leaf area index , 2014 .

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

[10]  Quantifying evapotranspiration and biophysical regulations of a poplar plantation assessed by eddy covariance and sap-flow methods. , 2009 .

[11]  D. Sims,et al.  An improved approach for remotely sensing water stress impacts on forest C uptake , 2014, Global change biology.

[12]  J. Townshend,et al.  A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies , 2013 .

[13]  Hirofumi Hashimoto,et al.  Modeling the interannual variability and trends in gross and net primary productivity of tropical forests from 1982 to 1999 , 2005 .

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

[15]  Huang Huiping.  China Land Cover 2000 Using SPOT VGT S10 Data , 2005 .

[16]  E. Rastetter,et al.  Potential Net Primary Productivity in South America: Application of a Global Model. , 1991, Ecological applications : a publication of the Ecological Society of America.

[17]  Nathan M. Urban,et al.  Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates , 2011 .

[18]  Jindi Wang,et al.  Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Jie He,et al.  On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau , 2010 .

[20]  Conghe Song,et al.  Understanding moisture stress on light use efficiency across terrestrial ecosystems based on global flux and remote‐sensing data , 2015 .

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

[22]  Erxue Chen,et al.  A long-term simulation of forest carbon fluxes over the Qilian Mountains , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Jie He,et al.  Improving land surface temperature modeling for dry land of China , 2011 .

[24]  Jiquan Chen,et al.  Energy balance and partition in Inner Mongolia steppe ecosystems with different land use types , 2009 .

[25]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[26]  Piero Toscano,et al.  Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set , 2013 .

[27]  W. Cohen,et al.  Evaluation of MODIS NPP and GPP products across multiple biomes. , 2006 .

[28]  Ranga B. Myneni,et al.  The impact of gridding artifacts on the local spatial properties of MODIS data : Implications for validation, compositing, and band-to-band registration across resolutions , 2006 .

[29]  Mingguo Ma,et al.  Validation of MODIS-GPP product at 10 flux sites in northern China , 2013 .

[30]  K. Davis,et al.  Uncertainty in model parameters and regional carbon fluxes: A model-data fusion approach , 2014 .

[31]  Jinwei Dong,et al.  Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model , 2015 .

[32]  Shuqing Zhao,et al.  Scale criticality in estimating ecosystem carbon dynamics , 2014, Global change biology.

[33]  Ramakrishna R. Nemani,et al.  Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[34]  K. Davis,et al.  Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data , 2010 .

[35]  Min Liu,et al.  Large‐scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands , 2014 .

[36]  Li Zhang,et al.  A Comparison of Satellite-Derived Vegetation Indices for Approximating Gross Primary Productivity of Grasslands , 2014 .

[37]  Dailiang Peng,et al.  Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy , 2017 .

[38]  Steven W. Running,et al.  User's Guide Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm , 2015 .

[39]  Bo Jiang,et al.  Retrieval of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from VIIRS Time-Series Data , 2016, Remote. Sens..

[40]  Qiang Liu,et al.  The Global Land Surface Satellite (GLASS) Remote Sensing Data Processing System and Products , 2013, Remote. Sens..

[41]  Michele Meroni,et al.  Remote sensing-based estimation of gross primary production in a subalpine grassland , 2012 .

[42]  Hans Peter Schmid,et al.  Footprint modeling for vegetation atmosphere exchange studies: a review and perspective , 2002 .

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

[44]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[45]  B. Wylie,et al.  Correction to “Upscaling carbon fluxes over the Great Plains grasslands: Sinks and sources” , 2011 .

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

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

[48]  Dan Liu,et al.  Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database , 2014 .

[49]  Stefano Tarantola,et al.  Sensitivity analysis of spatial models , 2009, Int. J. Geogr. Inf. Sci..

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

[51]  S. Liang,et al.  Improved estimations of gross primary production using satellite‐derived photosynthetically active radiation , 2014 .

[52]  Dailiang Peng,et al.  Monitoring the distribution of C3 and C4 grasses in a temperate grassland in northern China using moderate resolution imaging spectroradiometer normalized difference vegetation index trajectories , 2012 .

[53]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[54]  Ronggao Liu,et al.  Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties , 2013 .

[55]  Zhongmin Hu,et al.  Comparison of four light use efficiency models for estimating terrestrial gross primary production , 2015 .

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

[57]  M. Heimann,et al.  Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes , 2007 .

[58]  Maosheng Zhao,et al.  Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses , 2006 .

[59]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[60]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[61]  S. Ollinger,et al.  Generalization and evaluation of the process‐based forest ecosystem model PnET‐CN for other biomes , 2015 .

[62]  Aaron Moody,et al.  Twentieth-Century Droughts and Their Impacts on Terrestrial Carbon Cycling in China , 2009 .

[63]  T. Vesala,et al.  Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes , 2007 .

[64]  Guangsheng Zhou,et al.  Light Use Efficiency over Two Temperate Steppes in Inner Mongolia, China , 2012, PloS one.

[65]  Markus Reichstein,et al.  Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models , 2007 .

[66]  Jonas Ardö,et al.  Evaluation of MODIS gross primary productivity for Africa using eddy covariance data , 2013 .

[67]  Prasanna H. Gowda,et al.  Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI , 2016 .

[68]  Yi Zheng,et al.  Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation , 2017, Remote. Sens..

[69]  T. Vesala,et al.  Advantages of diffuse radiation for terrestrial ecosystem productivity , 2002 .

[70]  C. Frantzidis,et al.  Response to Reviewers Reviewer #1 , 2010 .

[71]  H. Fang,et al.  Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground‐based estimates over six FLUXNET sites , 2015 .

[72]  T. Arkebauer,et al.  Gross primary production and ecosystem respiration of irrigated maize and irrigated soybean during a growing season , 2005 .

[73]  M. B. Beck,et al.  Water quality modeling: A review of the analysis of uncertainty , 1987 .

[74]  Martin Wild,et al.  Solar radiation budgets in atmospheric model intercomparisons from a surface perspective , 2005 .

[75]  K. Davis,et al.  A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes , 2006 .

[76]  Gavin C. Cawley,et al.  Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers , 2003, Pattern Recognit..

[77]  Mekonnen Gebremichael,et al.  Evaluation of MODIS Gross Primary Productivity (GPP) in tropical monsoon regions , 2006 .

[78]  Yu Xia,et al.  Evaluation of MODIS Gross Primary Production across Multiple Biomes in China Using Eddy Covariance Flux Data , 2016, Remote. Sens..

[79]  J. Monteith.  Climate and the efficiency of crop production in Britain , 1977 .

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

[81]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[82]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .