Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine

Roy, Bishal; Bari, Ehsanul

doi:10.1016/j.heliyon.2022.e10668

Cited by 26 publications

(12 citation statements)

References 71 publications

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“…This is consistent with the research of most experts and scholars ( Mathew and P S, Khandelwal S. , 2022,5: , Yang et al, 2016 ), which reported the importance of vegetation to mitigate urban heat island effect, regardless of in islands or in mainland cities ( Julien and Sobrino, 2009 ). In contrast to NDVI, NDBI showed a significant positive correlation with LST in four seasons, and the contribution value calculated by RF was significant high ( Roy and Bari, 2022 ). Results also showed that the relationship between MNDWI and LST was different from that of most studies.…”

Section: Discussionmentioning

confidence: 83%

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

Zhu

Shen

et al. 2023

Ecological Indicators

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Section: Discussionmentioning

confidence: 83%

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

Zhu

Shen

et al. 2023

Ecological Indicators

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“…In this study, the multiband (band 1-7) Landsat 8 remote sensing data was retrieved and classi ed by utilizing the online cloud platform Google Earth Engine (GEE) (Ernida et al, 2020). The GEE uses to retrieve the land surface temperature (Roy & Bari, 2022). The training sample area within the ROI was digitized from the Landsat 8 guided by the google earth view.…”

Section: Local Climate Zone Mappingmentioning

confidence: 99%

Geoinformatics Heat Hazards Threat Based on Local Climate Zone.

Wibowo

Ramadhan

Shidiq

et al. 2023

Preprint

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Sumedang Regency has experienced considerable development in recent times. They started by expanding residential areas and toll roads to construct the Jatigede Dam. Changes in land cover may influence the temperature above and around it. This study aims to analyze the spatial pattern of surface temperature in Sumedang Regency between 2007-2022 based on land cover changes regarding the Local Climate Zone and heat hazard threat. The data used are Landsat TM, Landsat OLI/TIRS, and Landsat OLI-2/TIRS-2 images. The data is taken and processed using Google Earth Engine. The results found an increase in LST from 2007 until 2022, with the most significant increase occurring in 2022. This increase is accompanied by changes in land cover in Sumedang Regency. This increase is accompanied by changes in land cover in Sumedang Regency. Land surface temperatures in Sumedang Regency have continued to increase from 2007 until 2022, and based on the observed urban heat signature, the most significant increase in average temperature occurred from 2007 to 2022 (4.68℃). The strong heat stress category can cause dynamic thermal sensation (DTS), a core-to-skin, and an increase in rectal temperature. Extreme heat stress can cause an increase in rectal temperature time gradient, a steep decrease in total net heat loss, and a steep increase in average sweat rate.

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“…LST offers insights into temperature variations between urban and rural areas, influencing urban planning strategies. The thermal characteristics of the surface undergo significant changes due to the strong influence of landscape features (Roy & Bari, 2022).…”

Section: Introductionmentioning

confidence: 99%

Assessing land surface temperature variability over a large region: A temporal consistency approach using a single day satellite imagery

Rengma,

Yadav

2024

Preprint

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Land surface temperature (LST) is a vital parameter in various scientific disciplines, including climate studies, environmental monitoring, and urban planning. This study focuses on the crucial parameter of LST and its diverse applications in understanding Earth's dynamic systems. The study addresses the limitations of traditional LST measurement methods and emphasizes the importance of satellite remote sensing for large-scale monitoring. It explores the impact of land use and land cover changes on LST, using machine learning algorithms to enhance accuracy. The research proposes a novel approach of capturing satellite data on a single day to achieve consistent atmospheric conditions, reducing uncertainties in LST estimations. A case study over Chandigarh city using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, and Random Forest (RF) reveals RF's superior performance in LST predictions during both summer and winter seasons. Building on these findings, the study extends its focus to Ranchi, demonstrating RF's robustness with impressive accuracy in capturing LST variations. The research contributes to bridging existing gaps in large-scale LST estimation methodologies, offering valuable insights for its diverse applications in understanding Earth's dynamic systems.

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Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine

Cited by 26 publications

References 71 publications

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

Geoinformatics Heat Hazards Threat Based on Local Climate Zone.

Assessing land surface temperature variability over a large region: A temporal consistency approach using a single day satellite imagery

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