Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city, Eastern Algeria

Abstract In this study, we presented a mono-window (MW) algorithm for land surface temperature retrieval from Landsat 8 TIRS. MW needs spectral radiance and emissivity of thermal infrared bands as input for deriving LST. The spectral radiance was estimated using band 10, and the surface emissivity value was derived with the help of NDVI and vegetation proportion parameters for which OLI bands 5 and 4 were used. The results in comparison with MODIS (MOD11A1) products indicated that the proposed algorithm is capable of retrieving accurate LST values, with a correlation coefficient of 0.850. The industrial area, public facilities and military area show higher surface temperature (more than 37 °C) in comparison with adjoining areas, while the green spaces in urban areas (34 °C) and forests (29 °C) were the cooler part of the city. These successful results obtained in the study could be used as an efficient method for the environmental impact assessment.

[1]  Zhao-Liang Li,et al.  MODIS Land Surface Temperature and Emissivity , 2010 .

[2]  Ingegärd Eliasson,et al.  The use of climate knowledge in urban planning , 2000 .

[3]  José A. Sobrino,et al.  Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data , 2006 .

[4]  Zhao-Liang Li,et al.  Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data , 2008, Sensors.

[5]  Hassan Rhinane,et al.  Contribution of Landsat TM Data for the Detection ofUrban Heat Islands Areas Case of Casablanca , 2012 .

[6]  J. Monteith,et al.  The Micrometeorology of the Urban Forest [and Discussion] , 1989 .

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

[8]  G. Roberts,et al.  Thermal remote sensing of active vegetation fires and biomass burning events [Chapter 18] , 2013 .

[9]  B. Dousseta,et al.  Satellite multi-sensor data analysis of urban surface temperatures and landcover , 2003 .

[10]  Stephan Pauleit,et al.  Benefits and uses of urban forests and trees , 2005 .

[11]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[12]  A. Mokhnache,et al.  Estimating of total atmospheric water vapor content from MSG1-SEVIRI observations , 2015 .

[13]  Stefan Dech,et al.  Thermal Infrared Remote Sensing:Sensors, Methods, Applications , 2015 .

[14]  José A. Sobrino,et al.  Satellite-derived land surface temperature: Current status and perspectives , 2013 .

[15]  Monika J. Hajto,et al.  Land Surface Temperature Patterns in the Urban Agglomeration of Krakow (Poland) Derived from Landsat-7/ETM+ Data , 2014, Pure and Applied Geophysics.

[16]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[17]  Christopher O. Justice,et al.  Land remote sensing and global environmental change : NASA's earth observing system and the science of ASTER and MODIS , 2011 .

[18]  Hannes Taubenböck,et al.  Analysis of Surface Thermal Patterns in Relation to Urban Structure Types: A Case Study for the City of Munich , 2013 .

[19]  Cecil C. Konijnendijk,et al.  Urban Forests and Trees , 2005 .

[20]  Fei Wang,et al.  An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data , 2015, Remote. Sens..

[21]  Xiuchun Yang,et al.  Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China , 2014, Journal of Arid Land.

[22]  J. Cristóbal,et al.  Modeling air temperature through a combination of remote sensing and GIS data , 2008 .

[23]  A. Karnieli,et al.  A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region , 2001 .