A Novel Method to Estimate Subpixel Temperature by Fusing Solar-Reflective and Thermal-Infrared Remote-Sensing Data With an Artificial Neural Network

Among the multisource data fusing methods, the potential advantages of remote sensing of solar-reflective visible and near-Infrared [(VNIR); 400-900 nm] data and thermal-infrared (TIR) data have not been fully mined. Usually, a linear unmixed method is used for the purpose, which results in low estimation accuracy of subpixel land-surface temperature (LST). In this paper, we propose a novel method to estimate subpixel LST. This approach uses the characteristics of high spatial-resolution advanced spaceborne thermal emission and reflection radiometer (ASTER) VNIR data and the low spatial-resolution TIR data simulated from ASTER temperature product to generate the high spatial-resolution temperature data at a subpixel scale. First, the land-surface parameters (e.g., leaf area index, normalized difference vegetation index (NDVI), soil water content index, and reflectance) were extracted from VNIR data and field measurements. Then, the extracted high resolution of land-surface parameters and the LST were simulated into coarse resolutions. Second, the genetic algorithm and self-organizing feature map artificial neural network (ANN) was utilized to create relationships between land-surface parameters and the corresponding LSTs separately for different land-cover types at coarse spatial-resolution scales. Finally, the ANN-trained relationships were applied in the estimation of subpixel temperatures (at high spatial resolution) from high spatial-resolution land-surface parameters. The two sets of data with different spatial resolutions were simulated using an aggregate resampling algorithm. Experimental results indicate that the accuracy with our method to estimate land-surface subpixel temperature is significantly higher than that with a traditional method that uses the NDVI as an input parameter, and the average error of subpixel temperature is decreased by 2-3 K with our method. This method is a simple and convenient approach to estimate subpixel LST from high spatial-temporal resolution data quickly and effectively.

[1]  R. Lucas,et al.  Non-linear mixture modelling without end-members using an artificial neural network , 1997 .

[2]  Anthony Gar-On Yeh,et al.  Neural-network-based cellular automata for simulating multiple land use changes using GIS , 2002, Int. J. Geogr. Inf. Sci..

[3]  Liu Qinhuo,et al.  Using CUPID to Simulate Wheat Canopy Component Temperatures Distribution:Sensitivity Analysis and Evaluation , 2007 .

[4]  Martha C. Anderson,et al.  Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans , 2004 .

[5]  Sameer Singh,et al.  Approaches to Multisensor Data Fusion in Target Tracking: A Survey , 2006, IEEE Transactions on Knowledge and Data Engineering.

[6]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature , 1995 .

[7]  Yao Min Clustering analysis based on SOFM network , 2006 .

[8]  Martha C. Anderson,et al.  Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship , 2003 .

[9]  E. Lynn Usery,et al.  Geospatial data resampling and resolution effects on watershed modeling: A case study using the agricultural non-point source pollution model , 2004, J. Geogr. Syst..

[10]  Niu Zheng,et al.  Evaluating Soil Moisture Status in China Using the Temperature/Vegetation Dryness Index(TVDI) , 2003, National Remote Sensing Bulletin.

[11]  Anthony J. Ratkowski,et al.  MODTRAN4: radiative transfer modeling for remote sensing , 1999, Remote Sensing.

[12]  Simon J. Hook,et al.  Validation of a New Parametric Model for Atmospheric Correction of Thermal Infrared Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Ma Jianwen Li Qiqing Hasi Bagan STUDY ON ASTER DATA CLASSIFICATION USING SELF-ORGANIZING NEURAL NETWORK METHOD , 2003 .

[14]  Kamal Sarabandi,et al.  Estimation of Sahelian-Grassland Parameters Using a Coherent Scattering Model and a Genetic Algorithm , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[15]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[16]  Weiguo Liu,et al.  Comparison of non-linear mixture models: sub-pixel classification , 2005 .

[17]  Han Ling,et al.  Application of multi-homed remote sensing image data fusion method in geology , 2005 .

[18]  Qiang Liu,et al.  Modeling Directional Brightness Temperature of the Winter Wheat Canopy at the Ear Stage , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[20]  T. Carlson,et al.  A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .

[21]  Samuel N. Goward,et al.  Evapotranspiration from combined reflected solar and emitted terrestrial radiation: Preliminary FIFE results from AVHRR data , 1989 .

[22]  J. Mahfouf,et al.  The ISBA land surface parameterisation scheme , 1996 .

[23]  Qiang Liu,et al.  A field measurement method of spectral emissivity and research on the feature of soil thermal infrared emissivity , 2003 .

[24]  J. Hogg Quantitative remote sensing of land surfaces , 2004 .

[25]  J. Norman,et al.  Algorithms for extracting information from remote thermal-IR observations of the Earth's surface , 1995 .