Modelling gross primary production in the Heihe river basin and uncertainty analysis

References

• Cao , M. , Prince , S.D. , Small , J. and Goetz , S.J. 2004 . Remotely sensed interannual variations and trends in terrestrial net primary productivity 1981–2000 . Ecosystems , 7 : 233 – 242 .
   Web of Science ®Google Scholar
• Collatz , G.J. , Ball , J.T. , Grivet , C. and Berry , J.A. 1991 . Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer . Agricultural and Forest Meteorology , 54 : 107 – 136 .
   Web of Science ®Google Scholar
• Collins , D.C. and Avissar , R. 1994 . An evaluation with the Fourier amplitude sensitivity test (FAST) of which land-surface parameters are of greatest importance in atmospheric modeling . Journal of Climate , 7 : 681 – 703 .
   Web of Science ®Google Scholar
• Crosetto , M. and Tarantola , S. 2001 . Uncertainty and sensitivity analysis: tools for GIS-based model implementation . International Journal of Geographical Information Science , 15 : 415 – 437 .
   Web of Science ®Google Scholar
• Dye , D.G. and Shibasaki , R. 1995 . Intercomparison of global PAR data sets . Geophysical Research Letters , 22 : 2013 – 2016 .
   Web of Science ®Google Scholar
• Eck , T.F. and Dye , D.G. 1991 . Satellite estimation of incident photosynthetically active radiation using ultraviolet reflectance . Remote Sensing of Environment , 38 : 135 – 146 .
   Web of Science ®Google Scholar
• Gower , S.T. , Kucharik , C.J. and Norman , J.M. 1999 . Direct and indirect estimation of leaf area index, fAPAR and net primary production of terrestrial ecosystems . Remote Sensing of the Environment , 70 : 29 – 51 .
   Web of Science ®Google Scholar
• Helton , J.C. , Davis , F.J. and Johnson , J.D. 2005 . A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling . Reliability Engineering and System Safety , 89 : 305 – 330 .
   Web of Science ®Google Scholar
• Kremer , J.N. 1983 . Ecological implications of parameter uncertainty in stochastic simulation . Ecological Modelling , 18 : 187 – 207 .
   Web of Science ®Google Scholar
• Larocque , G.R. , Bhatti , J.S. , Boutin , R. and Chertov , O. 2008 . Uncertainty analysis in carbon cycle models of forest ecosystems: research needs and development of a theoretical framework to estimate error propagation . Ecological Modelling , 219 : 400 – 412 .
   Web of Science ®Google Scholar
• Li , S. , Niu , Z. , Yan , H. and Xu , W. 2008 . Modeling gross primary production in Jiangxi Province using MODIS images . International Conference on Earth Observation Data Processing and Analysis (ICEODPA). Proceedings of the SPIE , 7285 : 72854G
   Google Scholar
• Li , S. , Yan , H. , Niu , Z. , Xu , W. and Yuan , J. 2007a . Satellite estimation of photosynthetically active radiation in Jiangxi Province . MIPPR2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications. Proceedings of the SPIE , 6790 : 67902D
   Google Scholar
• Li , Z. , Yu , G. , Xiao , X. , Li , Y. , Zhao , X. , Ren , C. , Zhang , L. and Fu , Y. 2007b . Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data . Remote Sensing of Environment , 107 : 510 – 519 .
   Web of Science ®Google Scholar
• Marino , S. , Hogue , I.B. , Ray , C.J. and Kirschner , D.E. 2008 . A methodology for performing global uncertainty and sensitivity analysis in systems biology . Journal of Theoretical Biology , 254 : 178 – 196 .
   PubMed Web of Science ®Google Scholar
• Muleta , M.K. and Nicklow , J.W. 2005 . Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model . Journal of Hydrology , 306 : 127 – 145 .
   Web of Science ®Google Scholar
• Post , J. , Hattermann , F.F. , Krysanova , V. and Suckow , F. 2008 . Parameter and input data uncertainty estimation for the assessment of long-term soil organic carbon dynamics . Environmental Modelling and Software , 23 : 125 – 138 .
   Web of Science ®Google Scholar
• Potter , C.S. , Randerson , J.T. , Field , C.B. , Matson , P.A. , Vitousek , P.M. , Mooney , H.A. and Klooster , S.A. 1993 . Terrestrial ecosystem production: a process model based on global satellite and surface data . Global Biogeochemical Cycle , 7 : 811 – 842 .
   Web of Science ®Google Scholar
• Prince , S.D. and Goward , S.N. 1995 . Global primary production: a remote sensing approach . Journal of Biogeography , 22 : 815 – 835 .
   Web of Science ®Google Scholar
• Qi , S. and Cai , Y. 2007 . Mapping and assessment of degraded land in the Heihe river basin, arid north-western China . Sensors , 7 : 2565 – 2578 .
   PubMed Web of Science ®Google Scholar
• Raich , J.W. , Rastetter , E.B. , Melillo , J.M. , Kicklighter , D.W. , Steudler , P.A. , Peterson , B.J. , Grace , A.L. , Moore , III, B. and Vorosmarty , C.J. 1991 . Potential net primary productivity in South America: application of a global model . Ecological Applications , 1 : 399 – 429 .
   PubMed Web of Science ®Google Scholar
• Roy , J. and Saugier , B. 2001 . “ Terrestrial primary productivity: definitions and milestones ” . In Terrestrial Global Productivity , Edited by: Roy , J. , Saugier , B. and Mooney , H.A. 1 – 6 . San Diego, CA : Academic Press .
   Google Scholar
• Ruimy , A. , Kergoat , L. and Bondeau , A. 1999 . Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light-use efficiency . Global Change Biology , 5 : 56 – 64 .
   Web of Science ®Google Scholar
• Running , S.W. , Thornton , P.E. , Nemani , R. and Glassy , J.M. 2000 . “ Global terrestrial gross and net primary productivity from the Earth Observing System ” . In Methods in ecosystem science , Edited by: Sala , O.E. , Jackson , R.B. and Mooney , H.A. 44 – 57 . New York : Springer-Verlag .
   Google Scholar
• Saltelli , A. and Bolado , R. 1998 . An alternative way to compute Fourier amplitude sensitivity test (FAST) . Computational Statistics and Data Analysis , 26 : 445 – 460 .
   Web of Science ®Google Scholar
• Saltelli , A. , Chan , A. and Scott , N.A. 2000a . Sensitivity Analysis , Weinheim : John Wiley and Sons .
   Google Scholar
• Saltelli , A. , Tarantola , S. and Campolongo , F. 2000b . Sensitivity analysis as an ingredient of modeling . Statistical Science , 15 : 377 – 395 .
   Web of Science ®Google Scholar
• Saltelli , A. , Tarantola , S. , Campolongo , F. and Ratto , M. 2004 . Sensitivity analysis in practice: a guide to assessing scientific models , 232 Hoboken , NJ : John Wiley and Sons .
   Google Scholar
• Saltelli , A. , Tarantola , S. and Chan , K.P.S. 1999 . A quantitative model-independent method for global sensitivity analysis of model output . Technometrics , 41 : 39 – 56 .
   Web of Science ®Google Scholar
• Smith , J.E. and Heath , L.S. 2001 . Identifying influences on model uncertainty: an application using a forest carbon budget model . Environmental Management , 27 : 253 – 267 .
   PubMed Web of Science ®Google Scholar
• Turner , D.P. , Ritts , W.D. , Zhao , M. , Kurc , S.A. , Dunn , A.L. , Wofsy , S.C. , Small , E.E. and Running , S.W. 2006 . Assessing interannual variation in MODIS-based estimates of gross primary production . IEEE Transactions on Geoscience and Remote Sensing , 44 : 1899 – 1907 .
   Web of Science ®Google Scholar
• Waring , R.H. , Law , B.E. , Goulden , M.L. , Bassow , S.L. , Mccreight , R.W. , Wofsy , S.C. and Bazzaz , F.A. 1995 . Scaling gross ecosystem production at Harvard Forest with remote sensing: a comparison of estimates from a constrained quantum-use efficiency model and eddy correlation . Plant, Cell and Environment , 18 : 1201 – 1213 .
   Web of Science ®Google Scholar
• Xiao , X. , Hollinger , D. , Aber , J. , Goltz , M. , Davidson , E.A. , Zhang , Q. and Moore , III, B. 2004a . Satellite-based modeling of gross primary production in an evergreen needleleaf forest . Remote Sensing of Environment , 89 : 519 – 534 .
   Web of Science ®Google Scholar
• Xiao , X. , Zhang , Q. , Braswell , B. , Urbanski , S. , Boles , S. , Wofsy , S. , Moore , III, B. and Ojima , D. 2004b . Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data . Remote Sensing of Environment , 91 : 256 – 270 .
   Web of Science ®Google Scholar
• Xu , C. , He , H.S. , Hu , Y. , Chang , Y. , Li , X. and Bu , R. 2005 . Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation . Ecological Modelling , 185 : 255 – 269 .
   Web of Science ®Google Scholar
• Yuan , W. , Liu , S. , Zhou , G. , Zhou , G. , Tieszen , L.L. , Baldocchi , D. , Bernhofer , C. , Gholz , H. , Goldstein , A.H. , Goulden , M.L. , Hollinger , D.Y. , Hu , Y. , Law , B.E. , Stoy , P.C. , Vesala , T. and Wofsy , S.C. 2007 . Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes . Agricultural and Forest Meteorology , 143 : 189 – 207 .
   Web of Science ®Google Scholar