Enhancement of Spatial Resolution of Thermal Bands Using Vegetation and Impervious Surface Indices

Document Type : علمی - پژوهشی

Authors

1 Assistant Prof. of Remote Sensing Division, Surveying and Geomatics Engineering Dep., College of Engineering, University of Zabol, Zabol

2 Assistant Prof., Dep. of Geomatics, Faculty of Civil and Transportation Engineering, University of Isfahan, Isfahan

Abstract

In recent years, the issue of improving the spatial resolution of thermal images in urban areas has been introduced as a new challenge. The purpose of this study is to use the impervious surfaces indices and vegetation indices to improve the spatial resolution of Landsat ETM + thermal band over Tehran as a part of the study area. After the initial pre-processing, images obtained using the mean filter was simulated at spatial resolutions 120, 240, 480, 720 and 960 m. The relationships between these simulated imaged with the image simulated at the resolution of 960 m were calculated by the use of regression models.These derived models, containing vegetation and impervious surface indices, were then used to simulation of surface temperatures in different pixel sizes. The accuracy of each output, has been evaluated using the thermal images of ETM + and MODIS sensors.The results showed that by increasing the spatial resolution, the errors increases while the gradient of error is not fixed. So that in all indices, there are more increasing in gradient of error when the pixel size goes to smaller than 240 meters.Moreover, the best performance was obtained by combination of impervious surfaces indices and vegetation indices due to the enhancement of spatial resolution of thermal images in Tehran city.Using the combination of these indices, the spatial resolution of the MODIS sensor can be reached to about 240 meters, while the absolute error value is less than 1 K Kelvin. 

Keywords


  1. Agam, N. & Kustas, W.P., 2007, A Vegetation Index Based Technique for Spatial Sharpening of Thermal Imagery, Remote Sensing of Environment, 107(4), PP. 545–558.
  2. Bastiaanssen, W.G.M. & Pelgrum, H., 1998, A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL): Part 2: Validation, Journal of Hydrology, 212–229.
  3. Essa, W. & Verbeiren, B., 2012, Evaluation of the DisTrad Thermal Sharpening Methodology for Urban Areas, International Journal of Applied Earth Observation and Geoformation, 19(0), 163–172.
  4. Inamdar, A.K. & French, A., 2008, Land Surface Temperature Retrieval at High Spatial and Temporal Resolutions over the Southwestern United States, Journal of Geophysical Research: Atmospheres, 113(D7), P. D07107.
  5. Kallel, A. & Ottle, C., 2013, Surface Temperature Downscaling From Multiresolution Instruments Based on Markov Models, IEEE Transactions on Geoscience and Remote Sensing, 51(3), 1588–1612.
  6. Kustas, W.P. & Norman, J.M., 2003, Estimating Subpixel Surface Temperatures and Energy Fluxes from the Vegetation Index Radiometric Temperature Relationship, Remote Sensing of Environment, 85(4), 429–440.
  7. Leblon, B., 2013, Soil and Vegetation Optical Properties, University of New Brunswick, Fredericton (NB), Canada.
  8. Moradizadeh, M. & Saradjian, M.R., 2016, Vegetation Effects Modeling in Soil Moisture Retrieval Using MSVI, Photogrammetric Engineering and Remote Sensing, 82(10), PP. 803–810.
  9. Moradizadeh, M. & Saradjian, M.R., 2017, Estimation of Improved Resolution Soil Moisture in Vegetated Areas Using Passive AMSR-E Data, Journal of Earth System Science, In Press.
  10. Liu, D. & Pu, R., 2008, Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval, Sensors, 8(4), PP. 2695–2706.
  11. Merlin, O. & Duchemin, B., 2010, Disaggregation of MODIS Surface Temperature over an Agricultural Area Using a Time Series of Formosat-2 Images, Remote Sensing of Environment, 114(11), PP. 2500–2512.
  12. Roerink, G.J. & Su, Z., 2000, S-SEBI: A Simple Remote Sensing Algorithm to Estimate the Surface Energy Balance, Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25(2), PP. 147–157.
  13. Stathopoulou, M. & Cartalis, C., 2009, Downscaling AVHRR Land Surface Temperatures for Improved Surface Urban Heat Island Intensity Estimation, Remote Sensing of Environment, 113(12), PP. 2592–2605.
  14. Weng, Q. & Lu, D., 2004, Estimation of Land Surface Temperature-Vegetation Abun-dance Relationship for Urban Heat Island Studies, Remote Sensing of Environment, 89(4), PP. 467–483.
  15. Yuan, F. & Bauer, M.E., 2007, Comparison of Impervious Surface Area and Normalized Difference Vegetation Index as Indicators of Surface Urban Heat Island Effects in Landsat Imagery, Remote Sensing of Environment, 106(3), PP. 375–386.
  16. Zhan, W. & Chen, Y., 2013, Disaggregation of Remotely Sensed Land Surface Temperature: Literature Survey, Taxonomy, Issues, and Caveats, Remote Sensing of Environment, 131(0), PP. 119–139.