Assimilation of SVM-based estimates of land surface temperature for the retrieval of surface energy balance components

Data-assimilation methods play a crucial role for exploiting remote sensing in dynamic physical models for the prediction of hydrological-process evolution. Here, a novel method is proposed to assimilate land-surface temperature estimates, derived by applying support-vector regression to infrared satellite data, into a variational technique for mass and energy exchange estimation at the soil surface. Recent techniques to fully automate support vector regression and to estimate the pixelwise statistics of the regression error are incorporated in the proposed method.

[1]  Giorgio Boni,et al.  Estimation of large‐scale evaporation fields based on assimilation of remotely sensed land temperature , 2008 .

[2]  H. Fischer,et al.  Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends , 2002 .

[3]  Gabriele Moser,et al.  Modelling the Error Statistics in Support Vector Regression of Surface Temperature from Infrared Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Jeff Dozier,et al.  A generalized split-window algorithm for retrieving land-surface temperature from space , 1996, IEEE Trans. Geosci. Remote. Sens..

[5]  Ming-Wei Chang,et al.  Leave-One-Out Bounds for Support Vector Regression Model Selection , 2005, Neural Computation.

[6]  Robert E. Dickinson,et al.  The Force–Restore Model for Surface Temperatures and Its Generalizations , 1988 .

[7]  Gabriele Moser,et al.  Land and Sea Surface Temperature Estimation by Support Vector Regression , 2009 .

[8]  Shafiqul Islam,et al.  Prediction of Ground Surface Temperature and Soil Moisture Content by the Force‐Restore Method , 1995 .

[9]  Giorgio Boni,et al.  Sampling strategies and assimilation of ground temperature for the estimation of surface energy balance components , 2001, IEEE Trans. Geosci. Remote. Sens..

[10]  William H. Press,et al.  Numerical recipes in C , 2002 .

[11]  Gabriele Moser,et al.  Automatic Parameter Optimization for Support Vector Regression for Land and Sea Surface Temperature Estimation From Remote Sensing Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Fabio Castelli,et al.  Mapping of Land-Atmosphere Heat Fluxes and Surface Parameters with Remote Sensing Data , 2003 .

[13]  Lorenzo Bruzzone,et al.  Kernel methods for remote sensing data analysis , 2009 .

[14]  Gabriele Moser,et al.  Modeling the Error Statistics in Support Vector Regression of Surface Temperature From Infrared Data , 2009, IEEE Geoscience and Remote Sensing Letters.

[15]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[16]  Fabio Castelli,et al.  Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery , 2004 .

[17]  Junbin Gao,et al.  A Probabilistic Framework for SVM Regression and Error Bar Estimation , 2002, Machine Learning.

[18]  William E. Nichols,et al.  Evaluation of the evaporative fraction for parameterization of the surface energy balance , 1993 .

[19]  K. Saxton,et al.  ANTECEDENT RETENTION INDEXES PREDICT SOIL MOISTURE , 1967 .

[20]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.