MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms
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Jia Xu | Shaohua Zhao | Xiaotong Zhang | Xiaowei Chen | Kun Jia | Yunjun Yao | Lilin Zhang | Yuhu Zhang | Xuanyu Wang | Xiaotong Zhang | K. Jia | Yunjun Yao | Yuhu Zhang | Shaohua Zhao | Jia Xu | Lilin Zhang | Xiaowei Chen | Xuanyu Wang
[1] S. Liang,et al. Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter , 2011 .
[2] Jiemin Wang,et al. Intercomparison of surface energy flux measurement systems used during the HiWATER‐MUSOEXE , 2013 .
[3] Xiaotong Zhang,et al. Review on Estimation of Land Surface Radiation and Energy Budgets From Ground Measurement, Remote Sensing and Model Simulations , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[4] Peter S. Sephton,et al. Forecasting recessions: can we do better on MARS? , 2001 .
[5] 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.
[6] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[7] Ahmed El-Shafie,et al. Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure , 2012, Stochastic Environmental Research and Risk Assessment.
[8] C. Tucker,et al. A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data , 2000 .
[9] Muhammad Adnan,et al. Estimating Evapotranspiration using Machine Learning Techniques , 2017 .
[10] C. Priestley,et al. On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .
[11] S. Running,et al. Regional evaporation estimates from flux tower and MODIS satellite data , 2007 .
[12] S. Seneviratne,et al. Global intercomparison of 12 land surface heat flux estimates , 2011 .
[13] Shaomin Liu,et al. Validation of remotely sensed evapotranspiration over the Hai River Basin, China , 2012 .
[14] J. Freidman,et al. Multivariate adaptive regression splines , 1991 .
[15] S. Shukla,et al. Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment , 2015 .
[16] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[17] A. Huete,et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .
[18] Qiming Qin,et al. Simple method to determine the Priestley–Taylor parameter for evapotranspiration estimation using Albedo-VI triangular space from MODIS data , 2011 .
[19] Eric F. Wood,et al. Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches , 2011 .
[20] Donald F. Specht,et al. A general regression neural network , 1991, IEEE Trans. Neural Networks.
[21] Maosheng Zhao,et al. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses , 2006 .
[22] Fei Zhang,et al. Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data , 2015, Remote. Sens..
[23] S. Seneviratne,et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.
[24] E. Wood,et al. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations , 2008 .
[25] J. Adamowski,et al. Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada , 2012 .
[26] R. W. McClendon,et al. Estimating daily pan evaporation with artificial neural networks , 2000 .
[27] Richard D. De Veaux,et al. Multicollinearity: A tale of two nonparametric regressions , 1994 .
[28] S. Liang,et al. Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into the Common Land Model , 2011 .
[29] Hossein Tabari,et al. Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression , 2010, Irrigation Science.
[30] Yi Lin,et al. Differences in estimating terrestrial water flux from three satellite-based Priestley-Taylor algorithms , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[31] D. Baldocchi. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .
[32] J. Norman,et al. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature , 1995 .
[33] A. Huete,et al. Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance , 2013 .
[34] L. S. Pereira,et al. Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .
[35] Eric F. Wood,et al. Multi‐model, multi‐sensor estimates of global evapotranspiration: climatology, uncertainties and trends , 2011 .
[36] Alan H. Strahler,et al. Global land cover mapping from MODIS: algorithms and early results , 2002 .
[37] Seung Oh Lee,et al. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia , 2012 .
[38] Maurice R Puyau,et al. Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water. , 2010, The Journal of nutrition.
[39] Shaohua Zhao,et al. onitoring global land surface drought based on a hybrid vapotranspiration model , 2011 .
[40] Maosheng Zhao,et al. Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .
[41] P. Ciais,et al. Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model , 2007 .
[42] J. Monteith. Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.
[43] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[44] Maosheng Zhao,et al. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .
[45] Zhanqing Li,et al. A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature , 2007 .
[46] Shaomin Liu,et al. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .
[47] Maosheng Zhao,et al. Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .
[48] S. Liang,et al. Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. , 2017 .
[49] R. Deo,et al. Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models , 2016, Stochastic Environmental Research and Risk Assessment.
[50] J. Randerson,et al. Continental-scale net radiation and evapotranspiration estimated using MODIS satellite observations , 2011 .
[51] J. Norman,et al. Correcting eddy-covariance flux underestimates over a grassland , 2000 .
[52] R. Dickinson,et al. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability , 2011 .
[53] M. Taillefer,et al. Highly efficient and mild copper-catalyzed N- and C-arylations with aryl bromides and iodides. , 2004, Chemistry.
[54] Xiaoliang Lu,et al. Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data , 2010 .
[55] Dimitri P. Solomatine,et al. Model Induction with Support Vector Machines: Introduction and Applications , 2001 .
[56] D. Baldocchi,et al. Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites , 2008 .
[57] Narendra Singh Raghuwanshi,et al. Estimating Evapotranspiration using Artificial Neural Network , 2002 .
[58] Jia Xu,et al. Satellite-Derived Spatiotemporal Variations in Evapotranspiration over Northeast China during 1982-2010 , 2017, Remote. Sens..
[59] William P. Kustas,et al. Use of remote sensing for evapotranspiration monitoring over land surfaces , 1996 .