Volume XL-8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 933-936, 2014
https://doi.org/10.5194/isprsarchives-XL-8-933-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 933-936, 2014
https://doi.org/10.5194/isprsarchives-XL-8-933-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  28 Nov 2014

28 Nov 2014

Retrieval of Land Surface Temperature Diurnal Cycle model parameters from Kalpana-1 VHRR data over India

D. B. Shah1, M. R. Pandya2, A. Gujrati2, H. J. Trivedi1, and R. P. Singh2 D. B. Shah et al.
  • 1N. V. Patel college of Pure and Applied Sciences, Vallabh Vidyanagar, 388120, Gujarat, India
  • 2Space Applications Centre (ISRO), Ahmedabad, 380015, Gujarat, India

Keywords: Land, Modelling, Retrieval, Temperature, Thermal

Abstract. Land Surface Temperature (LST) is an important parameter in the land surface processes on regional and global scale. The Land Surface Temperature Diurnal (LSTD) cycle of different land cover is an excellent indicator of the surface processes and their interaction with planetary boundary layer. The Kalpana-1 very high resolution radiometer (VHRR) LST product is available with 30 minute spatial resolution and 0.1 degree temporal resolution. A study was carried out with an objective to determine the LSTD parameters directly from K1-VHRR monthly averaged LST observations over Indian landmass. In this analysis, a harmonic function is fitted to LSTD from the K1-VHRR observations, where cosine term describing the effect of sun and exponential term represents decay of LST during nighttime. Using LSTD parameters, one can directly know the temperature amplitude, residual temperature and time of maximum temperature for each pixel. The LSTD parameters fitting accuracy in root mean square error (RMSE) and coefficient of determination (R2) ranges between 0.5–2.5 K and 0.90–0.99 respectively for most of the pixels over Indian landmass. These LSTD parameters may bring new insight for estimation of thermal inertia and also useful in cloud screening algorithms.