In the study of snow melt runoff, the temperature lapse rate (TLR) is an essential variable. For the Beas River Basin in the Western Himalayas, it has been approximated in the current study. In this work, the split-window (SW) technique for TLR estimate has been used to recover the land surface temperature (LST). LST in the study area has a negative correlation with elevation values, and the trend shows that LST and elevation have an inverse relationship, according to data from the United States Geological Survey's (USGS) advanced spaceborne thermal emission and reflection radiometer (ASTER) and global digital elevation model (GDEM). The TLRs for the Beas River Basin region vary from 0.71°C/100 m to 0.87°C/100 m during the time period of 18 April 2013 to 27 June 2015. The findings were calculated using lapse rates calculated from maps produced by the moderate resolution image spectroradiometer (MODIS). There is excellent agreement between the MODIS-Terra data and the air temperature and LST from Landsat-8. The modelling of snow and glacier melt flow in the Himalayan region will benefit from the current work.
Top1. Introduction
An important consideration in research on snow and glacier melt runoff is the land surface temperature (LST). Typically, it is described as the ground's surface temperature. Recently advanced remote sensing technology is responsible for providing a substitute to mapping LST on a large scale. Many studies have tried to recover this information from satellite data (Jiang, 2013; Tang and Li, 2014; Jiménez-muñoz et al., 2014; Rajeshwari and Mani, 2014; Sattari and Hashim, 2014). These strategies are based on a split-window (SW) technique for estimating LST from the Landsat-8 satellite, which was jointly developed by the National Aeronautics and Space Administration NASA and the United States Geological Survey (USGS). The Landsat Data Continuity Mission satellite carried out the mission (LDCM). When the Landsat-8 satellite was sent into orbit in February 2013, it brought with it two sensors, one of which was a thermal infrared sensor (TIRS) that can detect heat. According to the underlying principle for LST remote sensing, the total amount of radiative radiation released by the ground's surface rises sharply with surface temperature. A ground object's energy emission's spectral distribution changes with ground temperature as well. Sensors that operate in the thermal infrared (TIR) region of the electromagnetic spectrum (8-12 m) at the atmospheric window may remotely detect the thermal energy of the real ambient temperature of the ground surface. The brightness temperature (TB) refers to the temperature of the ground at the satellite level.
Several studies have been conducted to estimate LST using TIR radiation emitted from surfaces using an SW algorithm (Becker and Li, 1990; Ulivieri et al., 1994; Coll and Caselles, 1997; Sobrino and Raissouni, 2000; Jain et al., 2008). To account for the different transmittances of the atmosphere, the SW technique uses two neighbouring TIR channels, centred at 11 m and 12 m, to get surface temperatures from Landsat-8. Based on the differential infrared absorption, the SW LST approach corrects atmospheric influences. The accuracy of the SW algorithm depends on the magnitude of the difference between the emissivities of the surface in the two bands (Becker, 1987; Ruescas et al., 2016).
A crucial factor in determining how much water melts from snow and glaciers are the temperature lapse rate (TLR). The air temperature data available for each elevation zone is spatially interpolated based on the TLR method defined as a rate of decrease of temperature with elevation (Singh, 1991). However, even in highly industrialised nations, there are few stations that measure air temperature, and they are sometimes hundreds of kilometres apart. Spatial coverage is even worse in developing countries where measurements are very few or non-existent (Lakshmi et al., 2001). Snow cover temperature has been mainly observed in polar regions by using the advanced very high-resolution radiometer (AVHRR) sensor (Key and Haefliger, 1992; Key et al., 1997; Stroeve and Steffen, 1998). In India, a constant value of TLR calculated from air temperature data has been used in snow melt studies in different catchments with different kinds of relief (Thapa, 1980; Bagchi, 1981; Agarwal et al., 1983; Jeyram et al., 1983; Seth, 1983). However, many researchers (Jain, 2001; Haritashya, 2005) have suggested using actual TLR data for better snow melt runoff estimation. Singh (1991) conducted a sensitivity analysis of TLR in the Beas basin and found that a difference of 1°C in TLR influences the snow melt runoff computation by 28%–37%. Saraf et al. (2005) has reported a negative linear correlation between LST and elevation. They constructed a digital elevation model (DEM) using nighttime thermal images captured by the National Oceanic and Atmospheric Administration's advanced very high-resolution radiometer (NOAA-AVHRR).