Abstract
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.
Highlights
Temperature Lapse Rate (TLR) is the rate at which temperature changes with elevation in the atmosphere (Kattel et al 2018; Li et al 2013)
Regression relationships based on the feature space between the Land Surface Temperature (LST) and the Digital Elevation Model (DEM) can be used to model the Surface Temperature Lapse Rate (NSTLR) and this can be used as an alternative to TLR in different applications (Qin et al 2018; Romshoo et al 2018; Zhang et al 2018)
The relationship between the LST obtained from satellite images and the elevation obtained from GDEM was used to model Near-Surface Temperature Lapse Rate (NSTLR)
Summary
Temperature Lapse Rate (TLR) is the rate at which temperature changes with elevation in the atmosphere (Kattel et al 2018; Li et al 2013). Air temperature recorded in synoptic stations at different altitudes in a region were used for modeling of TLR (Blandford et al 2008; Rolland 2003). The generalized linear regression is the widely used method to model TLR in a small geographic area based on air temperatures (Kattel et al 2018). Regression relationships based on the feature space between the LST and the Digital Elevation Model (DEM) can be used to model the Surface Temperature Lapse Rate (NSTLR) and this can be used as an alternative to TLR in different applications (Qin et al 2018; Romshoo et al 2018; Zhang et al 2018)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
