Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

Wang, Lei; Lu, Yuanxu; Yao, Yanjie

doi:10.3390/s19225049

Cited by 76 publications

(64 citation statements)

References 42 publications

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“…Since the input parameters used in the retrieval methods inevitably have errors, affecting the LST accuracy, some papers reported sensitivity analyses of the input parameters on LST methods [131][132][133]. In this appendix a sensitivity analysis of each retrieval method to a specific input parameter is carried out, with the other input parameters fixed (Table A5).…”

Section: Appendix Dmentioning

confidence: 99%

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Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

2020

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Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

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Section: Appendix Dmentioning

confidence: 99%

“…Thus, we assumed τ 10 and τ 11 to be 0.82 and 0.77, respectively. A fixed value of 1.5 K for T 10 -T 11 as in [131]. Table A5 dhows that LSE is the most important parameter influencing the results of MWA and SWA compared to the other inputs.…”

Section: Appendix Dmentioning

confidence: 99%

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

2020

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“…Given that the main coverage area of cloud was farmland and woodland, cloud coverage had little impact on this research. Specifically, the LST of the study area was retrieved from Band 6 of the Landsat TM imagery on 26 July 2008, and from Band 10 of Landsat TIRS imagery on 22 July 2018, using the generalized single-channel algorithm [43,44], which has been proven an effective method to retrieve LST from Landsat imagery [45][46][47]. This algorithm mainly calculates LST from a combination of the surface emissivity, at-sensor registered radiance, atmospheric functions, and parameters dependent on Planck's function.…”

Section: Land Surface Temperature Retrievalmentioning

confidence: 99%

The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China

et al. 2020

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To improve land use efficiency, urban renewal must also consider urban microclimates and heat islands. Existing research has depended on manual interpretation of high-resolution optical satellite imagery to resolve land surface temperature (LST) changes caused by urban renewal; however, the acquired ground time series data tend to be uneven and unique to specific frameworks. The objective of this study was to establish a more general framework to study LST changes caused by urban renewal using multi-source remote sensing data. Specifically, urban renewal areas during 2007–2017 were obtained by integrating Landsat and yearly Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and LST was retrieved from Landsat thermal infrared data using the generalized single-channel algorithm. Our results showed that urban renewal land (URL) area accounted for 1.88% of urban land area. Relative LST between URL and general urban land (GUL) of Liwan, Yuexiu, Haizhu, and Tianhe districts dropped by 0.88, 0.42, 0.43, and 0.10 K, respectively, whereas those of Baiyun, Huangpu, Panyu, and Luogang districts presented opposite characteristics, with a rise in the LST of 0.98, 1.03, 1.63, and 2.11 K, respectively. These results are attributable to population density, building density, and landscape pattern changes during the urban renewal process.

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“…Based on this, the present work seeks to make an in-depth study of the existing single-channel methodologies for computing LST based on satellite thermal data. It differs from similar works, such as [27], in the fact that the focus is set on the development of an adaptive strategy to apply the best methodology based on the atmospheric conditions of each case study, instead of searching for the best method in a general way. In this case, Landsat 8 and MODIS sensors onboard Terra were chosen, but the process can be extrapolated to any other satellite sensor that offers thermal, visible, and near-infrared data, such as ASTER.…”

Section: Introductionmentioning

confidence: 99%

Retrieving Land Surface Temperature from Satellite Imagery with a Novel Combined Strategy

et al. 2020

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Land surface temperature (LST) is a key parameter for land cover analysis and for many fields of study, for example, in agriculture, due to its relationship with the state of the crop in the evaluation of natural phenomena such as volcanic eruptions and geothermal areas, in desertification studies, or in the estimation of several variables of environmental interest such as evapotranspiration. The computation of LST from satellite imagery is possible due to the advances in thermal infrared technology and its implementation in artificial satellites. For example, Landsat 8 incorporates Operational Land Imager(OLI) and Thermal InfraRed Sensor(TIRS)sensors the images from which, in combination with data from other satellite platforms (such as Terra and Aqua) provide all the information needed for the computation of LST. Different methodologies have been developed for the computation of LST from satellite images, such as single-channel and split-window methodologies. In this paper, two existing single-channel methodologies are evaluated through their application to images from Landsat 8, with the aim at determining the optimal atmospheric conditions for their application, instead of searching for the best methodology for all cases. This evaluation results in the development of a new adaptive strategy for the computation of LST consisting of a conditional process that uses the environmental conditions to determine the most suitable computation method.

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Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

Cited by 76 publications

References 42 publications

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China

Retrieving Land Surface Temperature from Satellite Imagery with a Novel Combined Strategy

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