Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences
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[1] Robin K. S. Hankin,et al. Towards the probability of rapid climate change , 2006 .
[2] Sonja Kuhnt,et al. Design and analysis of computer experiments , 2010 .
[3] Matthias Drusch,et al. Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .
[4] Timothy W. Simpson,et al. Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.
[5] K. Moffett,et al. Remote Sens , 2015 .
[6] Luis Alonso,et al. Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[7] Adrian Doicu,et al. Multi-core-CPU and GPU-accelerated radiative transfer models based on the discrete ordinate method , 2014, Comput. Phys. Commun..
[8] O. Hagolle,et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .
[9] Yuri Knyazikhin,et al. Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .
[10] W. Verhoef,et al. Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data , 2015 .
[11] Peter Jan,et al. Particle Filtering in Geophysical Systems , 2009 .
[12] Mark J. Schervish,et al. Nonstationary Covariance Functions for Gaussian Process Regression , 2003, NIPS.
[13] Luis Alonso,et al. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 , 2012 .
[14] J. Hill,et al. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics , 2005 .
[15] W. Verhoef,et al. PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .
[16] Albert Tarantola,et al. Inverse problem theory - and methods for model parameter estimation , 2004 .
[17] A. O'Hagan,et al. Gaussian process emulation of dynamic computer codes , 2009 .
[18] Shenfeng Fei,et al. Ecological forecasting and data assimilation in a data-rich era. , 2011, Ecological applications : a publication of the Ecological Society of America.
[19] Roberto Furfaro,et al. A Statistical Framework for the Sensitivity Analysis of Radiative Transfer Models , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[20] J. Rougier. Efficient Emulators for Multivariate Deterministic Functions , 2008 .
[21] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[22] G. Gary Wang,et al. Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.
[23] W. Dierckx,et al. PROBA-V mission for global vegetation monitoring: standard products and image quality , 2014 .
[24] A. O'Hagan,et al. Quantifying uncertainty in the biospheric carbon flux for England and Wales , 2007 .
[25] Philip Lewis,et al. Temporal Constraints on Linear BRDF Model Parameters , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[26] Philip Lewis,et al. gp_emulator: Release of Remote Sensing paper code , 2016 .
[27] R. T. Wilson,et al. Py6S: A Python interface to the 6S radiative transfer model , 2013, Comput. Geosci..
[28] Bernard Pinty,et al. Do we (need to) care about canopy radiation schemes in DGVMs? Caveats and potential impacts , 2014 .
[29] Hong Jiang,et al. Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach. , 2011, Ecology letters.
[30] David Higdon,et al. Non-Stationary Spatial Modeling , 2022, 2212.08043.
[31] Hermann Kaufmann,et al. On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing , 2009 .
[32] D. Roya,et al. Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data , 2005 .
[33] Jindi Wang,et al. A Framework for Consistent Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo from MODIS Time-Series Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[34] Jianxi Huang,et al. Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield , 2013, Math. Comput. Model..
[35] Jan G. P. W. Clevers,et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .
[36] A. O'Hagan,et al. Curve Fitting and Optimal Design for Prediction , 1978 .
[37] N. Gobron,et al. A semidiscrete model for the scattering of light by vegetation , 1997 .
[38] Nadine Gobron,et al. Exploiting the MODIS albedos with the Two-stream Inversion Package (JRC-TIP): 1. Effective leaf area index, vegetation, and soil properties , 2011 .
[39] Roberta E. Martin,et al. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .
[40] Nadine Gobron,et al. An Earth Observation Land Data Assimilation System (EO-LDAS) , 2012 .
[41] Clive D Rodgers,et al. Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .
[42] Michael Dowd,et al. Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo , 2007 .
[43] M. Dowd. A sequential Monte Carlo approach for marine ecological prediction , 2006 .
[44] A. Strahler,et al. On the derivation of kernels for kernel‐driven models of bidirectional reflectance , 1995 .
[45] Jonathan Rougier,et al. Analyzing the Climate Sensitivity of the HadSM3 Climate Model Using Ensembles from Different but Related Experiments , 2009 .
[46] A. O'Hagan,et al. Predicting the output from a complex computer code when fast approximations are available , 2000 .
[47] Rasmus Fensholt,et al. MODIS leaf area index products: from validation to algorithm improvement , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[48] Wout Verhoef,et al. Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study , 2011 .
[49] C. Donlon,et al. The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission , 2012 .
[50] Farid Melgani,et al. Gaussian Process Regression for Estimating Chlorophyll Concentration in Subsurface Waters From Remote Sensing Data , 2010, IEEE Geoscience and Remote Sensing Letters.
[51] M. Kennedy,et al. Constraining the Sheffield dynamic global vegetation model using stream‐flow measurements in the United Kingdom , 2005, Global change biology.
[52] C. F. Sirmans,et al. Nonstationary multivariate process modeling through spatially varying coregionalization , 2004 .
[53] Nicholas C. Coops,et al. Virtual constellations for global terrestrial monitoring , 2015 .
[54] Michael T. Manry,et al. Attributes of neural networks for extracting continuous vegetation variables from optical and radar , 1998 .
[55] Geir Evensen,et al. The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .
[56] F. L. Dimet,et al. Multitemporal-patch ensemble inversion of coupled surface-atmosphere radiative transfer models for land surface characterization , 2008 .
[57] Anthony O'Hagan,et al. Diagnostics for Gaussian Process Emulators , 2009, Technometrics.
[58] Thomas Kaminski,et al. Recipes for adjoint code construction , 1998, TOMS.
[59] A.H. Haddad,et al. Applied optimal estimation , 1976, Proceedings of the IEEE.
[60] Branko Ristic,et al. Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .
[61] A. Strahler,et al. Monitoring vegetation phenology using MODIS , 2003 .
[62] W. Verhoef. Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .
[63] David Mackay,et al. Gaussian Processes - A Replacement for Supervised Neural Networks? , 1997 .
[64] José F. Moreno,et al. Toward a Semiautomatic Machine Learning Retrieval of Biophysical Parameters , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[65] D. Zupanski. A General Weak Constraint Applicable to Operational 4DVAR Data Assimilation Systems , 1997 .
[66] Thomas J. Santner,et al. The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.
[67] Andrew Gelman,et al. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..
[68] W. Verhoef,et al. Estimating forest variables from top-of-atmosphere radiance satellite measurements using coupled radiative transfer models , 2011 .
[69] Zhiqiang Xiao,et al. Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling , 2011 .
[70] Philip Lewis,et al. Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter , 2008 .
[71] S. Running,et al. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .
[72] D. Roy,et al. Continental-scale Validation of MODIS-based and LEDAPS Landsat ETM+ Atmospheric Correction Methods , 2012 .
[73] Arthur Gelb,et al. Applied Optimal Estimation , 1974 .
[74] P. Deschamps,et al. Atmospheric modeling for space measurements of ground reflectances, including bidirectional properties. , 1979, Applied optics.
[75] Arnaud Doucet,et al. On Particle Methods for Parameter Estimation in State-Space Models , 2014, 1412.8695.
[76] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[77] Didier Tanré,et al. Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..