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

Ogunode, Ayodeji; Akombelwa, Mulemwa

doi:10.4314/sajg.v6i2.10

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…In addition, estimations of relative humidity (RH) and the temperature of the upper air are needed. The following equations outline the process to obtain the equation required to calculate AT [18,41,42]:…”

Section: Atmospheric Transmittancementioning

confidence: 99%

“…Land surface emissivity (LSE (ε)) is essential to estimate LST, because LSE is a proportionality factor that scales blackbody radiance (Planck's law) to predict emitted radiance and represents the efficiency of transmitting thermal energy across the surface into the atmosphere. Using a formula used by Ogunode and Akombelwa [18], Dutta [43] and Jimenez-Munoz et al [44], LSE can be calculated using:…”

Section: Land Surface Emissivity (Lse) Assessmentmentioning

confidence: 99%

“…Thus, these unrealistic LST estimations are partly due to the erratic cloud coverage seen in 2011, 2013, 2018, and 2019 ( Figure 9). Because the base image was not trained with a specific emissivity value in specific areas, the resulting image is as shown in the figure [12,17,18,33,35,38].…”

Section: Air Temperature Against Landsat and Modismentioning

confidence: 99%

“…This has enabled uncalibrated datasets to be studied globally. However, despite the effect of stray light on Band 11, it has been found in various studies that the use of a multi-band LST retrieval method outperforms a single-channel LST method [17][18][19][20][21][22]. Nevertheless, due to unreliable data, studies have used secondary datasets such as Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal, ground-based thermal sensory, and meteorological data as a means of trend comparison [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia

Aik

Ismail

Muharam

2020

Land

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Mountainous regions are more sensitive to climatic condition changes and are susceptible to recent increases in temperature. Due to urbanization and land use/land cover (LULC) issues, Cameron Highlands has been impacted by rising land surface temperature (LST) variation. Thus, this study was carried out to explore the impact of the LULC change on LST in the Cameron Highlands from 2009 to 2019 using remote sensing images acquired from Landsat 7 ETM+, Landsat 8 Operational Land Imager (OLI/TIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS) 11A Thermal sensors. A split-window algorithm was applied to Landsat 8 images (2013–2019) to derive the LST. Air temperature data of the study area were also obtained to cross-validate data sources. Based on the validation results, the accuracy of LULC and LST outputs were more than 94.6% and 80.0%, respectively. The results show that the current trend of urban growth continues at a rate of 0.16% per year, and the area experienced an LST increase of 2 °C between 2009 and 2019. This study is crucial for land planners and environmentalists to understand the impacts of LULC change on LST and to propose appropriate policy measures to control development in Cameron Highlands.

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Section: Atmospheric Transmittancementioning

confidence: 99%

Section: Land Surface Emissivity (Lse) Assessmentmentioning

confidence: 99%

Section: Air Temperature Against Landsat and Modismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia

Aik

Ismail

Muharam

2020

Land

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“…NDVI can be obtained from bringing near infrared wavelength and red visible wavelengths reflected from the earth's surface to calculate the difference of reflections. The values of NDVI will display in the range of -1 and +1, which can be calculated according to Equation 2 (Ogunode & Akombelwa, 2017;Rotjanakusol & Laosuwan, 2019). From Equation 2, it can be explained that the NDVI value approaching 0 means that the area is slightly covered with vegetation.…”

Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

The Relationship Between Normalized Difference Vegetation Index and Canopy Temperature That Affects the Urban Heat Island Phenomenon

Prohmdirek¹,

Chunpang²,

Laosuwan³

2020

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The objective of this research is to analyze the relationship between Normalized Difference Vegetation Index (NDVI) and canopy temperature that affects the Urban Heat Island (UHI) phenomenon in Mueang Maha Sarakham Municipality. The operational methods were divided into three main steps: 1) Analyzing NDVI by using Landsat 8 OLI satellite data 2) Monitoring canopy temperature with mobile traverses installed with digital thermometer and auto-record GPS receiver 3) Analyzing the relationship between NDVI and canopy temperature that passed through the spatial interpolation process using the kriging method. The results of the operational methods indicated that the trend of average atmospheric temperature in the urban area was higher than the outer area. Mueang Maha Sarakham Municipality had the highest UHI in the atmosphere at 4.65 o C. And from the buffer zone with a distance of 800 m around the centroid point, the highest UHI in the atmosphere was 5.08 o C. From the regression analysis between NDVI and the atmospheric temperature, it could be found a negative relationship between the data with a significance level of 0.01 and the decision coefficient (R 2) of 0.9392. This can be explained that if the area from the buffer zone decreased towards the centroid point, the value of NDVI decreased as well. In addition, when the equation set from the regression analysis was brought to predict the difference in atmospheric temperature in the area of Mueang Maha Sarakham Municipality and the surrounding area, the UHI sizes at all periods (16.00-20.00 hrs.) increased in accordance with the decreases of NDVI values. Meanwhile, considering the tendency between the changes in UHI size in the atmosphere in each period and NDVI values with the polynomial regression analysis (degree 4), it was found that the maximum formation of UHI was during 18.00-19.00 hrs.'. The decreases in NDVI values resulted in a higher UHI size in each period. In addition, when the NDVI values in Mueang Maha Sarakham Municipality were less than 0.302, the largest size of UHI in the atmosphere will change at the time approaching 19.00 hrs.

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LS8pyCalc: semi-automated Python calculator to retrieve land surface temperature, cross verified through in situ and MODIS datasets

Rasheed,

Mahmood,

Khan

et al. 2023

Arab J Geosci

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

Abstract: Abstract

Cited by 8 publications

References 23 publications

Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia

Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia

The Relationship Between Normalized Difference Vegetation Index and Canopy Temperature That Affects the Urban Heat Island Phenomenon

LS8pyCalc: semi-automated Python calculator to retrieve land surface temperature, cross verified through in situ and MODIS datasets

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