An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

Cristóbal, Jordi; Jiménez-Muñoz, Juan C.; Prakash, Anupma; Mattar, Cristián; Skokovic, D.; Sobrino, José A.

doi:10.3390/rs10030431

Cited by 129 publications

(87 citation statements)

References 37 publications

(88 reference statements)

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“…However, SCA values are preferred, because both algorithms only need the known parameters ε 10 and the W (case of Jiménez-Muñoz et al [9] method) or the τ 10 (case of Wang et al [10] method). Moreover, the RMSE observed in this study for the SCAs and RTE applied to L8-TIRS band 10 data are in good agreement with other LST validation studies recently published [25,26].…”

Section: Rtesupporting

confidence: 90%

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

et al. 2018

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After Landsat 8 was launched in 2013, it was observed that for Thermal Infrared sensor (TIRS) bands, radiance from outside of an instrument’s field-of-view produced a non-uniform ghost signal across the focal plane that varied depending on the out-of-scene content (i.e., the stray light effect). A new stray light correction algorithm (SLCA) is currently operational and has been implemented into the United States Geological Survey (USGS) ground system since February 2017. The SLCA has also been applied to reprocess historical Landsat 8 scenes. After approximately two years of SLCA implementation, more land surface temperature (LST) validation studies are required to check the effect of correction in the estimation of LST from different retrieval algorithms. For this purpose, three different LST estimation method algorithms (i.e., the radiative transfer equation (RTE), single-channel algorithm (SCA), and split-window algorithm (SWA)) have been assessed. The study site is located on the campus of the University of Balearic Islands on the island of Mallorca (Spain) in the western Mediterranean Sea. The site is considered a heterogeneous area that is composed of different types of surfaces, such as buildings, asphalt roads, farming areas, sloped terrains, orange fields, almond trees, lawns, and some natural vegetation regions. Data from 21 scenes, which were acquired by the Landsat 8-TIRS sensor and extracted from a 100 × 100 m2 pixel, were used to retrieve the LST with different algorithms; then, they were compared with in situ LST measurements from a broadband thermal infrared radiometer located on the same Landsat 8 pixel. The results show good performances of the three methods, with the SWA showing the lowest observed RMSE (within 1.6–2 K), whereas the SCA applied to the TIRS band 10 (10 µm) was also appropriate, with a RMSE ranging within 2.0–2.3 K. The LST estimates using the RTE algorithm display the highest observed RMSE values (within 2.0–3.6 K) of all of the compared methods, but with an almost unbiased value of −0.1 K for the case of techniques applied to band 10 data. The SWAs are the preferred method to estimate the LST in our study area. However, further validation studies around the world are required.

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Section: Rtesupporting

confidence: 90%

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

et al. 2018

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“…The reason for working with these methods was the fact that they can be applied to many different satellites, from those that acquire in only one range of the thermal IR band, to those that acquire images in two or more bands in the thermal IR range. When working with Landsat 8 in this article, the use of thermal band 10 was recommended, since band 11 presented problems in its calibration as indicated by different investigations [18].…”

Section: Lst Retrieval Methodsmentioning

confidence: 99%

“…However, its application is not limited to this satellite, being usable by other satellites that incorporate the visible, near infrared, and thermal infrared bands needed. In fact, original single-channel methods were implemented for satellites earlier than Landsat 8 [12,18,21].…”

Section: Application Of Combined Strategy To Other Sensorsmentioning

confidence: 99%

“…Thus, for the case of using a single IR thermal band, single-channel methods based on the resolution of the radiative transfer equation and the Planck equation are applied [17]. These methods attenuate the scarce terrain information by introducing several atmospheric parameters, such as indirect measurements of air temperature and humidity [18]. On the other hand, for the case of using more than one thermal band, split window [17] or multi-angle [19] algorithms are applied.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The large-area monitoring of urban forest currently relies mainly on remote sensing images, combined with ground-survey methods to evaluate the various functional types and different spatial configurations [5,7,8]. This method is promising for large-area monitoring of forest resources [9,10].…”

Section: Introductionmentioning

confidence: 99%

Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models

et al. 2018

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The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.

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An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

Cited by 129 publications

References 37 publications

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

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

Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models

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