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In this paper, a model has been developed to estimate surface temperature of Antarctic ice sheet using thermal bands of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images and in situ surface temperature measurements. The brightness temperature of snow/ice surface of Antarctica has been estimated for MODIS bands 31 and 32 using Plan...
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Citations
... where σ is the Stephan Boltzmann constant, T s is the surface temperature in kelvin, and є s is the surface emissivity. Surface temperature maps for the study area have been generated using thermal bands of MODIS data by the method proposed in Gusain et al. [50] and given as follows: ...
... (10). Surface temperature map has been generated using thermal bands data of MODIS sensor images and detailed methodology is given in Gusain et al. [50]. Net radiation flux has been estimated using Eq. ...
In the present paper, incoming shortwave radiation flux, net shortwave radiation flux, net longwave radiation flux, and net radiation flux have been estimated at 1-km spatial scale for the ice sheet in East Dronning Maud Land, Antarctica. Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) products (i.e., land and atmospheric data products) have been used to estimate net radiation flux during few clear sky days of the years 2007, 2008, 2009, and 2010. Estimated surface energy fluxes using MODIS products have been evaluated using in situ recorded values of energy fluxes. In situ data of the surface energy fluxes and meteorological parameters have been collected using automatic weather stations (AWS) on ice sheet at two locations near the Indian Research Station “Maitri.” Net radiation flux has been estimated for the study area from net shortwave radiation flux and net longwave radiation flux maps. Bias, correlation, and root mean square error (RMSE) between AWS-recorded and MODIS-derived radiation fluxes have been observed as − 23 W m⁻², 0.91 and 61 W m⁻² for net shortwave radiation flux and − 21.3 W m⁻², 0.93 and 64 W m⁻² for net radiation flux, respectively. The study highlights the validation of some of the MODIS products and MODIS-derived energy fluxes in Antarctica. Spatial and temporal variations of radiative energy fluxes have also been investigated in the study area.
... However, due to rugged terrain and harsh climatic conditions, ground observations over vast areas of Himalaya are limited to few observation locations (Gusain et al. 2014). Remote sensing-based techniques have been found useful for collecting information from such inaccessible large areas and to retrieve various surface parameters (Chang and Foster 1991;König et al. 2001;Kulkarni et al. 2006;Singh et al. 2007;Das and Sarwade 2008;Gurung et al. 2011;Bolch et al. 2012;Singh et al. 2013;Gusain et al. 2015;Singh et al. 2016). ...
... A number of studies have been reported for retrieval of surface temperature using optical and passive microwave data (Qin et al. 2001;Li et al. 2013;Gusain et al. 2015;Liu et al. 2016). Different approaches have been proposed to derive surface temperature from thermal band data across the globe (Qin et al. 2001;Jiménez-Muñoz and Sobrino 2003;Li et al. 2013;Bendib et al. 2016;Liu et al. 2016) and few approaches have been reported for Indian Himalaya Raj and Fleming 2008;Haq et al. 2012). ...
In this paper, an automated method for retrieval of snow surface temperature (SST) in Beas River Basin, India, using Landsat-8 thermal data is proposed. Digital number (DN) values of thermal data were converted into Top of Atmospheric (TOA) radiance. Surface radiance has been estimated from TOA radiance using a single channel method. The estimated surface radiance was then converted into SST. Cloud free Landsat-8 data for January and February 2017 has been used to estimate SST. Snow and Avalanche Study Establishment (SASE) has established a wireless sensor network (WSN) in an avalanche prone slope in Beas River Basin, India. Landsat-8 retrieved SST has been compared and validated with recorded SST at WSN stations. The retrieved SST using proposed algorithm was in good agreement with SST recorded on ground by sensor network. The mean absolute error (MAE) and root-mean-square error (RMSE) between estimated and recorded SST has been observed as ~ 1.1 K and ~ 1.5 K for 23 January 2017 and ~ 0.7 and ~ 1.6 K for 24 February 2017. Algorithm has shown a potential for automated mapping of snow and ice surface temperature using Landsat-8 data for snow cover and glaciers in Himalaya.
