Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
暂无分享,去创建一个
Markus Reichstein | Kazuhito Ichii | Gustau Camps-Valls | Dario Papale | Christopher R. Schwalm | Gianluca Tramontana | Sven Sickert | Gerard Kiely | Alessandro Cescatti | Martin Jung | Sebastian Wolf | Lutz Merbold | M. Altaf Arain | G. Kiely | L. Merbold | G. Camps‐Valls | A. Cescatti | M. Reichstein | M. Jung | C. Schwalm | D. Papale | M. A. Arain | K. Ichii | S. Wolf | G. Tramontana | P. Serrano‐Ortiz | Botond Ráduly | B. Raduly | Penélope Serrano-Ortiz | S. Sickert | P. Serrano-Ortiz | Gustau Camps-Valls
[1] Dario Papale,et al. Eddy Covariance: A Practical Guide to Measurement and Data Analysis , 2012 .
[2] Zhao-Liang Li,et al. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data , 2002 .
[3] 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.
[4] M. Lomas,et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends , 2013, Global change biology.
[5] K. Ichii,et al. Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO2 exchanges: GOSAT CO2 inversion and empirical eddy flux upscaling , 2015 .
[6] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[7] J. Denzler,et al. Large-scale Gaussian process classification using random decision forests , 2012, Pattern Recognition and Image Analysis.
[8] B. Gao. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .
[9] J. Freidman,et al. Multivariate adaptive regression splines , 1991 .
[10] N. Gobron,et al. Diagnostic assessment of European gross primary production , 2008 .
[11] M. Heimann,et al. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes , 2007 .
[12] P. Cox,et al. Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models , 2013 .
[13] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[14] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[15] D. C. Uprety,et al. Carbon dioxide , 2017, Reactions Weekly.
[16] Nuno Carvalhais,et al. Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks , 2015 .
[17] N. C. Strugnell,et al. First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .
[18] Jiyuan Liu,et al. Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data , 2002 .
[19] D. Hollinger,et al. Uncertainty in eddy covariance measurements and its application to physiological models. , 2005, Tree physiology.
[20] Dario Papale,et al. A full greenhouse gases budget of Africa: synthesis, uncertainties, and vulnerabilities , 2014 .
[21] W. Oechel,et al. A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS , 2008 .
[22] P. J. García Nieto,et al. Forecasting the cyanotoxins presence in fresh waters: A new model based on genetic algorithms combined with the MARS technique , 2013 .
[23] A-Xing Zhu,et al. Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach , 2007 .
[24] V. Arora,et al. The effect of driving climate data on the simulated terrestrial carbon pools and fluxes over North America , 2014 .
[25] Vipin Kumar,et al. Similarity Measures for Categorical Data: A Comparative Evaluation , 2008, SDM.
[26] D. Roy,et al. An overview of MODIS Land data processing and product status , 2002 .
[27] K. Davis,et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data , 2010 .
[28] Costanza Calzolari,et al. Development of pedotransfer functions using a group method of data handling for the soil of the Pianura Padano-Veneta region of North Italy: water retention properties , 2005 .
[29] Peter Troch,et al. Observed timescales of evapotranspiration response to soil moisture , 2006 .
[30] A. Huete,et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .
[31] Jan G. P. W. Clevers,et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .
[32] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2004 .
[33] D. Baldocchi. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .
[34] Hoshin Vijai Gupta,et al. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .
[35] J. Nash,et al. River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .
[36] Markus Reichstein,et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts , 2015, Global change biology.
[37] A. Arneth,et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .
[38] C. Priestley,et al. On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .
[39] J. Paruelo,et al. Temporal and spatial patterns of ecosystem functioning in protected arid areas in southeastern Spain , 2005 .
[40] Markus Reichstein,et al. Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data , 2011 .
[41] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[42] Kenneth L. Clark,et al. Ecosystem carbon dioxide fluxes after disturbance in forests of North America , 2010 .
[43] S. Seneviratne,et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.
[44] P. Reich. The Carbon Dioxide Exchange , 2010, Science.
[45] W. Oechel,et al. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data , 2010, Remote Sensing of Environment.
[46] A. Arneth,et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation , 2010 .
[47] 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.
[48] Ray Leuning,et al. Global vegetation gross primary production estimation using satellite-derived light-use efficiency and canopy conductance. , 2015 .
[49] Chandra Giri,et al. A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .
[50] Peter E. Thornton,et al. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests , 2002 .
[51] A. Bondeau,et al. Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model , 2009 .
[52] D. Baldocchi,et al. Measuring fluxes of trace gases and energy between ecosystems and the atmosphere – the state and future of the eddy covariance method , 2014, Global change biology.
[53] R. Valentini,et al. A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization , 2003 .
[54] Lorenzo Bruzzone,et al. Kernel methods for remote sensing data analysis , 2009 .
[55] A. Arneth,et al. Assimilation exceeds respiration sensitivity to drought: A FLUXNET synthesis , 2010 .
[56] T. Vesala,et al. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation , 2006 .
[57] Joachim Denzler,et al. Large-scale gaussian process multi-class classification for semantic segmentation and facade recognition , 2013, Machine Vision and Applications.
[58] S. Running,et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .
[59] J. Thepaut,et al. The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .
[60] Martin Jung,et al. A Guided Hybrid Genetic Algorithm for Feature Selection with Expensive Cost Functions , 2013, ICCS.
[61] R. Nemani,et al. Refinement of rooting depths using satellite-based evapotranspiration seasonality for ecosystem modeling in California , 2009 .
[62] P. Ciais,et al. How errors on meteorological variables impact simulated ecosystem fluxes: a case study for six French sites , 2011 .
[63] Gustau Camps-Valls,et al. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data , 2015 .
[64] T. A. Black,et al. Reduction in carbon uptake during turn of the century drought in western North America , 2012 .
[65] Reza Shirmohammadi,et al. Optimization of mixed refrigerant systems in low temperature applications by means of group method of data handling (GMDH) , 2015 .
[66] T. Vesala,et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm , 2005 .
[67] F. Woodward,et al. Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.
[68] Olaf Menzer,et al. Carbon dioxide exchange over multiple temporal scales in an arid shrub ecosystem near La Paz, Baja California Sur, Mexico , 2012 .