Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach
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A-Xing Zhu | Hirofumi Hashimoto | Ramakrishna R. Nemani | Kazuhito Ichii | Alfredo Huete | Steven W. Running | Petr Votava | Andrew Michaelis | Feihua Yang | A. Zhu | S. Running | A. Huete | R. Nemani | H. Hashimoto | A. Michaelis | P. Votava | M. A. White | K. Ichii | Feihua Yang | Michael A. White
[1] Emilio A. Laca,et al. Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem , 2003 .
[2] D. Baldocchi,et al. Upscaling fluxes from tower to landscape: Overlaying flux footprints on high-resolution (IKONOS) images of vegetation cover , 2004 .
[3] S. T. Gower,et al. Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems , 1999 .
[4] D. Sims,et al. Potential of MODIS EVI and surface temperature for directly estimating per‐pixel ecosystem C fluxes , 2005 .
[5] C. Kanzow. Levenberg-Marquardt methods for constrained nonlinear equations with strong local convergence properties , 2004 .
[6] R. Colwell. Remote sensing of the environment , 1980, Nature.
[7] Ramakrishna R. Nemani,et al. Simulating terrestrial carbon fluxes using the new biosphere model “biosphere model integrating eco‐physiological and mechanistic approaches using satellite data” (BEAMS) , 2005 .
[8] A. Huete,et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .
[9] Peter E. Thornton,et al. Generating surfaces of daily meteorological variables over large regions of complex terrain , 1997 .
[10] W. Cohen,et al. Site‐level evaluation of satellite‐based global terrestrial gross primary production and net primary production monitoring , 2005 .
[11] T. Vesala,et al. Advantages of diffuse radiation for terrestrial ecosystem productivity , 2002 .
[12] A. Escudero,et al. Decline in photosynthetic nitrogen use efficiency with leaf age and nitrogen resorption as determinants of leaf life span , 2003 .
[13] C. Field,et al. A reanalysis using improved leaf models and a new canopy integration scheme , 1992 .
[14] J. William Munger,et al. Exchange of Carbon Dioxide by a Deciduous Forest: Response to Interannual Climate Variability , 1996, Science.
[15] Ping Shi,et al. Retrieval of oceanic chlorophyll concentration using support vector machines , 2003, IEEE Trans. Geosci. Remote. Sens..
[16] 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.
[17] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[18] A-Xing Zhu,et al. Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[19] Rachel T. Pinker,et al. Geostationary satellite parameters for surface energy balance , 2002 .
[20] C. Tucker,et al. Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999 , 2003, Science.
[21] A-Xing Zhu,et al. Effects of spatial detail of soil information on watershed modeling , 2001 .
[22] Zhao-Liang Li,et al. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data , 2002 .
[23] Peter E. Thornton,et al. Regional ecosystem simulation: Combining surface- and satellite-based observations to study linkages between terrestrial energy and mass budgets , 1998 .
[24] William W. Hargrove,et al. New analysis reveals representativeness of the AmeriFlux network , 2003 .
[25] Dennis D. Baldocchi,et al. Objective threshold determination for nighttime eddy flux filtering , 2005 .
[26] Z. Wan,et al. Quality assessment and validation of the MODIS global land surface temperature , 2004 .
[27] A. Goldstein,et al. Influences of canopy photosynthesis and summer rain pulses on root dynamics and soil respiration in a young ponderosa pine forest. , 2006, Tree physiology.
[28] Andrew Brennan,et al. Necessary and Sufficient Conditions , 2018, Logic in Wonderland.
[29] Alan H. Strahler,et al. Global land cover mapping from MODIS: algorithms and early results , 2002 .
[30] S. Hyakin,et al. Neural Networks: A Comprehensive Foundation , 1994 .
[31] Peter E. Thornton,et al. Parameterization and Sensitivity Analysis of the BIOME–BGC Terrestrial Ecosystem Model: Net Primary Production Controls , 2000 .
[32] R. Nemani,et al. MODIS DAILY PHOTOSYNTHESIS (PSN) AND ANNUAL NET PRIMARY PRODUCTION (NPP) PRODUCT (MOD17) Algorithm Theoretical Basis Document , 1999 .
[33] Maosheng Zhao,et al. Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .
[34] J. Monteith. SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .
[35] J. Paruelo,et al. ANPP ESTIMATES FROM NDVI FOR THE CENTRAL GRASSLAND REGION OF THE UNITED STATES , 1997 .
[36] G. Carmichael,et al. Airborne tunable diode laser measurements of formaldehyde during TRACE-P: Distributions and box model comparisons , 2003 .
[37] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[38] W. Oechel,et al. FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .
[39] T. A. Black,et al. A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest , 2005 .
[40] D. Baldocchi. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future , 2003 .
[41] Ramakrishna R. Nemani,et al. A flexible, integrated system for generating meteorological surfaces derived from point sources across multiple geographic scales , 2005, Environ. Model. Softw..
[42] Dan Watt,et al. Quality Assessment , 2009, Encyclopedia of Database Systems.
[43] Andrew E. Suyker,et al. Gap filling strategies for long term energy flux data sets , 2001 .
[44] Xiang Gao,et al. Multisensor comparisons and validation of MODIS vegetation indices at the semiarid Jornada experimental range , 2003, IEEE Trans. Geosci. Remote. Sens..
[45] D. Hodáňová. An introduction to environmental biophysics , 1979, Biologia Plantarum.
[46] W. Cohen,et al. Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation , 2003 .
[47] D. Hollinger,et al. MODELING GROSS PRIMARY PRODUCTION OF AN EVERGREEN NEEDLELEAF FOREST USING MODIS AND CLIMATE DATA , 2005 .
[48] Maosheng Zhao,et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .
[49] S. Running,et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .
[50] W. Cohen,et al. Evaluation of MODIS NPP and GPP products across multiple biomes. , 2006 .
[51] J. D. Tarpley,et al. Surface radiation budgets in support of the GEWEX Continental‐Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project , 2003 .
[52] F. Chapin,et al. Principles of Terrestrial Ecosystem Ecology , 2002, Springer New York.
[53] Bruce K. Wylie,et al. Integration of CO 2 flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation , 2005 .