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

Milton Isaya Ndossi,Ugur Avdan

doi:10.3390/rs8050413

Milton Isaya Ndossi, Ugur Avdan

Open Access

https://doi.org/10.3390/rs8050413

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Journal: Remote sensing	Publication Date: May 13, 2016
Citations: 99	License type: CC BY 4.0

Affiliation: Anadolu University, Eskişehir City Hospital

Abstract

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.

Highlights

Land Surface Temperature (LST) is the temperature of the surface of the ground [1]
The LST maps in this study have been produced using a QIS plugin which was written using the Python programming language. This has demonstrated how powerful open source tools can be in terms of remote sensing and geographic information systems data processing and analysis
As a result of this study being done through the use of open source tools, it encourages the use of Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS data in the production of land surface temperature maps, the improvement of algorithms and provides a platform for debates concerning LST extraction from satellite imagery

Summary

Introduction

Land Surface Temperature (LST) is the temperature of the surface of the ground [1] It is one of the most vital data recorded by satellites in the recent decades. 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]. With the development of satellite technologies and the availability of satellite imagery with a high spatial resolution, satellite data remains to be the only way that can be used to measure LST over the entire globe with sufficiently high spatial resolution and with complete spatially averaged rather than point values [2]

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