Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Land-surface temperature (LST) is of great significance for the estimation of radiation and energy budgets associated with land-surface processes. However, the available satellite LST products have either low spatial resolution or low temporal resolution, which constrains their potential applications. This paper proposes a spatiotemporal fusion method for retrieving LST at high spatial and temporal resolutions. One important characteristic of the proposed method is the consideration of the sensor observation differences between different land-cover types. The other main contribution is that the spatial correlations between different pixels are effectively considered by the use of a variation-based model. The method was tested and assessed quantitatively using the different sensors of Landsat TM/ETM+, moderate resolution imaging spectroradiometer and the geostationary operational environmental satellite imager. The validation results indicate that the proposed multisensor fusion method is accurate to about 2.5 K.

[1]  Ji Zhou,et al.  Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats , 2013 .

[2]  Martha C. Anderson,et al.  Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources , 2012 .

[3]  Olivier Lavialle,et al.  Multifocus image fusion and denoising: A variational approach , 2012, Pattern Recognit. Lett..

[4]  Altan Mesut,et al.  A comparative analysis of image fusion methods , 2012, 2012 20th Signal Processing and Communications Applications Conference (SIU).

[5]  Ming Chen,et al.  Validation of GOES-R Satellite Land Surface Temperature Algorithm Using SURFRAD Ground Measurements and Statistical Estimates of Error Properties , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[6]  K. Beurs,et al.  Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology , 2012 .

[7]  Qihao Weng,et al.  Enhancing temporal resolution of satellite imagery for public health studies: A case study of West Nile Virus outbreak in Los Angeles in 2007 , 2012 .

[8]  S. Liang,et al.  Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter , 2011 .

[9]  Devendra Singh,et al.  Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[10]  Thomas Hilker,et al.  Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery , 2011 .

[11]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[12]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery , 2010 .

[13]  Joanne C. White,et al.  Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. , 2009 .

[14]  Shunlin Liang,et al.  Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites , 2009 .

[15]  Liangpei Zhang,et al.  A MAP-Based Algorithm for Destriping and Inpainting of Remotely Sensed Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Mitchell D. Goldberg,et al.  Developing Algorithm for Operational GOES-R Land Surface Temperature Product , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[17]  A. French,et al.  Land surface temperature retrieval at high spatial and temporal resolutions over the southwestern United States , 2008 .

[18]  Jocelyn Chanussot,et al.  Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Robert E. Wolfe,et al.  A Landsat surface reflectance dataset for North America, 1990-2000 , 2006, IEEE Geoscience and Remote Sensing Letters.

[21]  R. T. Pinker,et al.  Implementation of GOES‐based land surface temperature diurnal cycle to AVHRR , 2005 .

[22]  Qingquan Li,et al.  A comparative analysis of image fusion methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[23]  R. Dickinson,et al.  The Footprint of Urban Areas on Global Climate as Characterized by MODIS , 2005 .

[24]  Russell C. Hardie,et al.  Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Rachel T. Pinker,et al.  Land Surface Temperature Estimation from the Next Generation of Geostationary Operational Environmental Satellites: GOES M¿Q , 2004 .

[26]  Z. Wan,et al.  Quality assessment and validation of the MODIS global land surface temperature , 2004 .

[27]  J. Sobrino,et al.  A generalized single‐channel method for retrieving land surface temperature from remote sensing data , 2003 .

[28]  Donglian Sun,et al.  Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES‐8) , 2003 .

[29]  Ren C. Luo,et al.  Multisensor fusion and integration: approaches, applications, and future research directions , 2002 .

[30]  Yuehua Wu,et al.  M-estimation in exponential signal models , 2001, IEEE Trans. Signal Process..

[31]  F. Lindsay,et al.  Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations , 2000 .

[32]  Zhao-Liang Li,et al.  A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data , 1997, IEEE Trans. Geosci. Remote. Sens..

[33]  H. Mooney,et al.  Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere , 1997, Science.

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

[35]  Bin Li,et al.  The impact of spatial variability of land-surface characteristics on land-surface heat fluxes , 1994 .

[36]  Manfred Ehlers,et al.  Multisensor image fusion techniques in remote sensing , 1991 .

[37]  J. D. Tarpley Estimating Incident Solar Radiation at the Surface from Geostationary Satellite Data , 1979 .

[38]  Vipin Chandra,et al.  MULTISENSOR FUSION AND INTEGRATION , 2013 .

[39]  K. Sundara Kumar,et al.  ESTIMATION OF LAND SURFACE TEMPERATURE TO STUDY URBAN HEAT ISLAND EFFECT USING LANDSAT ETM+ IMAGE , 2012 .

[40]  S. Bora,et al.  MULTISENSOR FUSION AND INTEGRATION , 2012 .

[41]  Michael K. Ng,et al.  Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images , 2009, Comput. J..

[42]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[43]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[44]  Frederick R. Forst,et al.  On robust estimation of the location parameter , 1980 .