Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods

Land Surface Temperature (LST) measurements using remote sensing data are conducted using various methods, including the Single Channel Method (SCM) and the Multi-Channel Method (MCM). Emissivity, which significantly affects LST, is determined using methods such as the Correction-Based Emissivity Me...

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Main Authors: A Sediyo Adi Nugraha, Muhammad Kamal, Sigit Heru Murti, Wirastuti Widyatmanti
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Geomatics, Natural Hazards & Risk
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Online Access:https://www.tandfonline.com/doi/10.1080/19475705.2024.2324975
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author A Sediyo Adi Nugraha
Muhammad Kamal
Sigit Heru Murti
Wirastuti Widyatmanti
author_facet A Sediyo Adi Nugraha
Muhammad Kamal
Sigit Heru Murti
Wirastuti Widyatmanti
author_sort A Sediyo Adi Nugraha
collection DOAJ
description Land Surface Temperature (LST) measurements using remote sensing data are conducted using various methods, including the Single Channel Method (SCM) and the Multi-Channel Method (MCM). Emissivity, which significantly affects LST, is determined using methods such as the Correction-Based Emissivity Method (CBEM) and the Non-Linear Based Emissivity Method (NBEM). Given these factors, validating LST measurements is crucial for assessing the accuracy of data processing results. This study aims to compare LST data retrieved using SCM and MCM with various emissivity calculation methods and assess their accuracy compared to single measurements with an infrared thermometer, measurements using a grid plot, and measurements using a thermal camera with the assistance of an Unmanned Aerial Vehicle (UAV). The results show that SCMJM&S and SCMAC did not produce optimal accuracy (>2.5K) when using the emissivity estimates from CBEM and NBEM. In contrast, MCM that adopted MCMSko, MCMQin, and MCMMao generally produced high-accuracy LST with an average difference of <1 K from actual temperatures, specifically in shrub pixels. MCM with the NDVI threshold method (NDVITHM) showed excellent results in distinguishing between vegetation and non-vegetation objects. In conclusion, MCM provides more accurate LST than SCM. Nevertheless, selecting the appropriate method should consider heterogeneity, homogeneity, and regional physiography.
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spelling doaj-art-eadfcdbd46b84dd0aa0b4f7d24c266db2025-08-20T02:34:32ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2024.2324975Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methodsA Sediyo Adi Nugraha0Muhammad Kamal1Sigit Heru Murti2Wirastuti Widyatmanti3Doctoral Program in Geography, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, IndonesiaDepartment of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, IndonesiaDepartment of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, IndonesiaDepartment of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, IndonesiaLand Surface Temperature (LST) measurements using remote sensing data are conducted using various methods, including the Single Channel Method (SCM) and the Multi-Channel Method (MCM). Emissivity, which significantly affects LST, is determined using methods such as the Correction-Based Emissivity Method (CBEM) and the Non-Linear Based Emissivity Method (NBEM). Given these factors, validating LST measurements is crucial for assessing the accuracy of data processing results. This study aims to compare LST data retrieved using SCM and MCM with various emissivity calculation methods and assess their accuracy compared to single measurements with an infrared thermometer, measurements using a grid plot, and measurements using a thermal camera with the assistance of an Unmanned Aerial Vehicle (UAV). The results show that SCMJM&S and SCMAC did not produce optimal accuracy (>2.5K) when using the emissivity estimates from CBEM and NBEM. In contrast, MCM that adopted MCMSko, MCMQin, and MCMMao generally produced high-accuracy LST with an average difference of <1 K from actual temperatures, specifically in shrub pixels. MCM with the NDVI threshold method (NDVITHM) showed excellent results in distinguishing between vegetation and non-vegetation objects. In conclusion, MCM provides more accurate LST than SCM. Nevertheless, selecting the appropriate method should consider heterogeneity, homogeneity, and regional physiography.https://www.tandfonline.com/doi/10.1080/19475705.2024.2324975Single channel methodmulti-channel methodland surface temperatureemissivityLandsat
spellingShingle A Sediyo Adi Nugraha
Muhammad Kamal
Sigit Heru Murti
Wirastuti Widyatmanti
Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods
Geomatics, Natural Hazards & Risk
Single channel method
multi-channel method
land surface temperature
emissivity
Landsat
title Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods
title_full Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods
title_fullStr Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods
title_full_unstemmed Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods
title_short Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods
title_sort accuracy assessment of land surface temperature retrievals from remote sensing imagery pixel based single and multi channel methods
topic Single channel method
multi-channel method
land surface temperature
emissivity
Landsat
url https://www.tandfonline.com/doi/10.1080/19475705.2024.2324975
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