Retrieval of land surface temperature from the Kalpana-1 VHRR data using a single-channel algorithm and its validation over western India

Pandya, Mehul R.; Shah, Dipti; Trivedi, Himanshu J.; Darji, Nikunj P.; Ramakrishnan, R.; Panigrahy, Sushma; Parihar, J. S.; Kirankumar, A. S.

doi:10.1016/j.isprsjprs.2014.05.004

Cited by 20 publications

(13 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various studies concentrate on the utilization of sensors onboard the polar-orbiting satellites on account of their high spatial resolutions (Pandya et al, 2014). Accurate LST estimation by utilizing the SC algorithm require high-quality air transmittance codes in estimating air quantities, Land Surface Emissivity data, precise air profile and topographic impacts Sobrino et al, (2004) predicts that LSE in the TIR regions of the 10-12μm region is established (Atitar andSobrino, 2009, Sobrino andRaissouni, 2000).…”

Section: Split Window Algorithmmentioning

confidence: 99%

An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset

Ogunode¹,

Akombelwa²

2017

SA J of Geomatics

View full text Add to dashboard Cite

show abstract

Section: Split Window Algorithmmentioning

confidence: 99%

An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset

Ogunode¹,

Akombelwa²

2017

SA J of Geomatics

View full text Add to dashboard Cite

show abstract

“…It is widely used in a variety of fields including but not limited to evapotranspiration, climate change, hydrological cycle, vegetation monitoring, urban climate and environmental studies [2]. LST is an important component of the Earth's heat budget and is commonly required in applications of hydrology, meteorology and climatology [3].…”

Section: Introductionmentioning

confidence: 99%

Application of Open Source Coding Technologies in the Production of Land Surface Temperature (LST) Maps from Landsat: A PyQGIS Plugin

2016

View full text Add to dashboard Cite

This paper presents a Python QGIS (PyQGIS) plugin, which has been developed for the purpose of producing Land Surface Temperature (LST) maps from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS, Thermal Infrared (TIR) imagery. The plugin has been developed purposely to ease the process of LST extraction from Landsat Visible, Near Infrared (VNIR) and TIR imagery. It has the ability to estimate Land Surface Emissivity (LSE), calculating at-sensor radiance, calculating brightness temperature and performing correction of brightness temperature against atmospheric interference though the Plank function, Mono Window Algorithm (MWA), Single Channel Algorithm (SCA) and the Radiative Transfer Equation (RTE). Using the plugin, LST maps of Moncton, New Brunswick, Canada have been produced for Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS. The study put much more emphasis on the examination of LST derived from the different algorithms of LST extraction from VNIR and TIR satellite imagery. In this study, the best LST values derived from Landsat 5 TM were obtained from the RTE and the Planck function with RMSE of 2.64˝C and 1.58˝C, respectively. While the RTE and the Planck function produced the best results for Landsat 7 ETM+ with RMSE of 3.75˝C and 3.58˝C respectively and for Landsat 8 TIRS LST retrieval, the best results were obtained from the Planck function and the SCA with RMSE of 2.07˝C and 3.06˝C, respectively.

show abstract

“…Satellite-based sensors that use thermal band data provide information on land surface temperature (LST) for vast areas, enabling researchers to study spatiotemporal trends in (i) the urban heat island effect (Nichole, 2005;Schwarz et al, 2011;Li et al, 2012;Skelsey et al, 2013;Dugord et al, 2014;Pandya et al, 2014;Weng and Fu, 2014); (ii) tectonic activities (Ma et al, 2010); (iii) land use cover (Momeni and Saradjian, 2007;Julien and Sobrino, 2009;Buyantuyev and Wu, 2010;Langer et al, 2010;Westermann et al, 2011;Michele et al, 2013;Chen et al, 2014;Ermida et al, 2014;Ghosh and P. Joshi, 2014); (iv) physiography (Kerchove et al, 2013); (v) geothermal anomalies (Qin et al, 2011); (vi) air temperature (Vogt et al, 1997;Cresswell et al, 1999;Jang et al, 2004;Mostovoy et al, 2006;Benali et al, 2012;Shamir and Georgakakos, 2014); and (vii) agricultural pest monitoring and control (Yones et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

Silva

Damásio

Sousa

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby improving pest management results and

show abstract

Retrieval of land surface temperature from the Kalpana-1 VHRR data using a single-channel algorithm and its validation over western India

Cited by 20 publications

References 38 publications

An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset

An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset

Application of Open Source Coding Technologies in the Production of Land Surface Temperature (LST) Maps from Landsat: A PyQGIS Plugin

Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

Contact Info

Product

Resources

About