Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 395–399, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-395-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 395–399, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-395-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

MODELING THE IMPACT OF SURFACE CHARACTERISTICS ON THE NEAR SURFACE TEMPERATURE LAPSE RATE

M. K. Firozjaei1, S. Fathololuomi2, S. K. Alavipanah1, M. Kiavarz1, A. Vaezi2, A. Biswas3, and A. Ghorbani4 M. K. Firozjaei et al.
  • 1Dept. of Remote Sensing and GIS, Geography Faculty, University of Tehran, Tehran, Iran
  • 2Dept. of Soil Science, Faculty of Agriculture, University of Zanjan, Iran
  • 3School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada
  • 4Rangeland and Watershed Management Department, University of Mohaghegh Ardabili, Ardabil, Iran

Keywords: Biophysical Characteristics, Topographic Parameters, Satellite imagery, NSTLR

Abstract. Modeling of Near-Surface Temperature Lapse Rate (NSTLR) is very important in various environmental applications. The Land Surface Temperature (LST) is influenced by many properties and conditions including surface biophysical and topographic characteristics. Some researches have considered the LST - Digital Elevation Model (DEM) feature space to model NSTLR. However, the influence of detailed surface characteristics is rare. This study investigated the impact of surface characteristics on the LST-DEM feature space for NSTLR modeling. A set of remote sensing data including Landsat 8 images, MODIS products, and surface features including DEM and land use of the Balikhli-Chay on 01/07/2018, 18/08/2018 and 03/09/2018 were collected and used in this study. First, Split Window (SW) algorithm was used to estimate LST, and spectral indices were employed to model surface biophysical characteristics. Owing to the impact of surface biophysical and topographic characteristics on the LST-DEM feature space, the NSTLR was calculated for different classes of surface biophysical characteristics, land use, and solar local incident angle. The modeled NSTLR values based on the LST-DEM feature space on 01/07/2018, 18/08/2018 and 03/09/2018 were 8.5, 1.5 and 2.4 °C Km−1; respectively. The NSTLR in different classes of surface biophysical characteristics, land use type and topographical parameters were variable between 0.5 to 14 °C Km−1. This clearly showed the dependence of NSTLR on topographic and biophysical conditions. This provides a new way of calculating surface characteristic specific NSTLR.