International Journal of Agricultural Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Land Surface Temperature Estimation Using Split Window Approach over US Nagar District of Uttarakhand State, India


Affiliations
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
2 Department of Soil and Water Conservation Engineering, Gobind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
     

   Subscribe/Renew Journal


To estimate land surface temperature (LST) has an important role for agriculture as well as global change of climate, growth of vegetation and glacier melting. It combines the results of all surface atmosphere interactions and energy fluxes between the surface and the atmosphere. Now-a-days, estimation of temperature of land surface is being calculated with the help of satellite images containing thermal infrared band. Though land surface temperature derived from satellite, could be a beneficial complement to conventional land surface temperature data sources. This research, proposed a methodology for determining land surface temperature through using a structured mathematical algorithm viz., split window (SW) algorithm. Split window algorithm has been used on LANDSAT 8 with operational land imager i.e. OLI sensor and thermal infrared sensor i.e. TIRS dataset of Udham Singh Nagar district. TIRS shows two thermal bands i.e. band 10 and band 11. SW approach requires brightness temperature value of both band 10 and band 11 as well as land surface emissivity which is calculated from OLI bands i.e. NIR and Red, for the estimation of land surface temperature. The spectral radiance was determined using thermal infrared bands i.e. band 10 and band 11. Emissivity was calculated by using normalized difference vegetation index i.e. NDVI threshold technique for which OLI bands 2, 3, 4 and 5 were utilized. SW approach uses brightness temperature of two bands of thermal infrared, mean and difference in land surface emissivity for estimating land surface temperature. In this paper, 6 Dec. 2015 date was selected as an example to show the approach of using SW technique to estimate the LST of Udham Singh Nagar district of Uttarakhand state in India.

Keywords

Split Window Approach, Fractional Vegetation Cover, Land Surface Emissivity, Land Surface Temperature.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Cheung, C. and Hart, M. (2014). Climate change and thermal comfort in Hong Kong. Internat. J. Biometeorol., 58(2): 137–148.

  • Cleveland, C. (2007). Heat Island Encyclopedia of Earth.

  • Ema, K., Nengah, S. J. and Widiatmaka (2016). Satellite-based land surface temperature estimation of Bogor municipality, Indonesia. Indonesian J. Electrical Engg. & Computer Sci., DOI: 10.11591/ijeecs.v2.i1, 2 (1): 221-228.

  • Gago, E.J., Roldan, J., Pacheco-Torres, R. and Ordonez, J. (2013). The city and urban heat islands: A review of strategies to mitigate adverse effects. Renewable & Sustain. Energy Reviews, 25(0): 749–758.

  • Gosling, S., Lowe, J., McGregor, G., Pelling, M. and Malamud, B. (2009). Associations between elevated atmospheric temperature and human mortality: a critical review of the literature. Climatic Change, 92(3-4) : 299-341.

  • Kleerekoper, L., Van-Esch, M. and Salcedo, T.B. (2012). How to make a city climate- proof, addressing the urban heat is land effect. Resour., Cons. & Recyc., 64(0): 30–38.

  • Kolokotroni, M., Zhang, Y. and Watkins, R. (2007). The London Heat Island and building cooling design. Solar Energy, 81(1): 102–110.

  • Kolokotroni, M., Ren, X., Davies, M. and Mavrogianni, A. (2012). London’s urban heat island: Impact on current and future energy consumption in office buildings. Energy & Buildings, 47(0) : 302–311.

  • Kondo, H. and Kikegawa, Y. (2003). Temperature variation in the urban canopy with anthropogenic energy use. Pure & Appl. Geophys., 160(1–2) : 317–324.

  • Li, Z.L., Wu, H., Wang, N., Qiu, S., Sobrino, J.A., Wan, Z., Tang, B.H. and Yan, G. (2013). Land surface emissivity retrieval from satellite data. Internat. J. Remote Sensing, 34 :3084-3127.

  • Madlener, R. and Sunak, Y. (2011). Impacts of urbanization on urban structures and energy demand: What can we learn for urban energy planning and urbanization management. Sustain. Cities & Soc., 1(1): 45-53.

  • Mirzaei, P.A. and Haghighat, F. (2010). Approaches to study Urban Heat Island, Abilities and limitations. Building & Environ., 45(10): 2192–2201.

  • Oke, T.R. (1982). The energetic basis of the Urban Heat-Island. Quarterly J. Royal Meteorol. Soc., 108(455): 1–24.

  • Quattrochi, D.A. and Luvall, J. C. (1999). Thermal infrared remote sensing for analysis of landscape ecological processes: Methods and applications. Landscape Ecol., 14(6): 577–598.

  • Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C. and Argiriou, A. (2001). On the impact of urban climate on the energy consumption of buildings, Solar Energy, 70(3): 201–216.

  • Santamouris, M. (2013). Using cool pavements as a mitigation strategy to fight urban heat island-A review. Solar Energy, 82(1): 301–312.

  • Sarrat, C., Lemonsu, A., Masson, V. and Guedalia, D. (2006). Impact of urban heat island on regional atmospheric pollution. Atmospheric Environ., 40(10): 1743–1758.

  • Skokovic, D., Sobrino, J. A., Jimenez-Munoz. J. C., Soria. G., Julien. Y., Mattar, C. and Jordi Cristobal (2014). Calibration and Validation of Land Surface Temperature for Landsat 8 – TIRS Sens. Land Product Validation and Evolution, ESA/ESRIN Frascati (Italy): 6-9, January 28-30,.

  • Sobrino, J.A., Li, Z.L., Stoll, M.P. and Becker, F. (1996). Multichannel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. Internat. J. Remote Sensing, 17: 2089–2114.

  • Tan, J., Zheng,Y., Tang, X., Guo, C., Li, L. and Song, G. (2010). The urban heat island and its impact on heat waves and human health in Shanghai. Internat. J. Biometeorol., 54(1): 75–84.

  • Xiaokang Kou, L.J., Yanchen, B., Shuang, Y. and Linna, C. (2016). Estimation of land surface temperature through blending MODIS and AMSR-E data with the Bayesian Maximum Entropy Method.Remote sensing, 108(5): 1-17, DOI: 10.3390/rs8020105.

  • Zhao, S., Qin, Q., Yang, Y., Xiong, Y. and Qiu, G. (2009). Comparison of two Split-Window Methods for Retrieving Land Surface Temperature from MODIS Data. J. Earth Syst. Sci., 118(4): 345- 353.

  • Ashley, E. and Lemay, L. (2008). Concrete’s contribution to sustainable development, from http://www.specifyconcrete.org/assets/docs/Concretes 20% Contribution to Sustainable Development.pdf.

  • https://earthexplorer.usgs.gov


Abstract Views: 231

PDF Views: 0




  • Land Surface Temperature Estimation Using Split Window Approach over US Nagar District of Uttarakhand State, India

Abstract Views: 231  |  PDF Views: 0

Authors

Daniel Prakash Kushwaha
Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
Vijay Kumar Singh
Department of Soil and Water Conservation Engineering, Gobind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
Tarate Suryakant Bajirao
Department of Soil and Water Conservation Engineering, Gobind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India

Abstract


To estimate land surface temperature (LST) has an important role for agriculture as well as global change of climate, growth of vegetation and glacier melting. It combines the results of all surface atmosphere interactions and energy fluxes between the surface and the atmosphere. Now-a-days, estimation of temperature of land surface is being calculated with the help of satellite images containing thermal infrared band. Though land surface temperature derived from satellite, could be a beneficial complement to conventional land surface temperature data sources. This research, proposed a methodology for determining land surface temperature through using a structured mathematical algorithm viz., split window (SW) algorithm. Split window algorithm has been used on LANDSAT 8 with operational land imager i.e. OLI sensor and thermal infrared sensor i.e. TIRS dataset of Udham Singh Nagar district. TIRS shows two thermal bands i.e. band 10 and band 11. SW approach requires brightness temperature value of both band 10 and band 11 as well as land surface emissivity which is calculated from OLI bands i.e. NIR and Red, for the estimation of land surface temperature. The spectral radiance was determined using thermal infrared bands i.e. band 10 and band 11. Emissivity was calculated by using normalized difference vegetation index i.e. NDVI threshold technique for which OLI bands 2, 3, 4 and 5 were utilized. SW approach uses brightness temperature of two bands of thermal infrared, mean and difference in land surface emissivity for estimating land surface temperature. In this paper, 6 Dec. 2015 date was selected as an example to show the approach of using SW technique to estimate the LST of Udham Singh Nagar district of Uttarakhand state in India.

Keywords


Split Window Approach, Fractional Vegetation Cover, Land Surface Emissivity, Land Surface Temperature.

References