Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data

Duan, Si‐Bo; Chen, Ru; LI, Zhaoliang; Wang, Mengmeng; Xu, Huan; Li, Hua; Wu, Penghai; Hong, Falu; Zhou, Ji; Wang, Zhao; Ren, Huazhong; Wu, Hua; Tang, Bo‐Hui; ZHANG, Xia; Shang, Gao; Qin, Zhihao

doi:10.11834/jrs.20211296

Cited by 22 publications

(6 citation statements)

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“…Among the commonly used land surface retrieval algorithms, the algorithm based on radiative transfer equation has a solid physical basis and high inversion accuracy (Lu et al, 2020;Duan et al, 2021). Radiative transfer equation-based method and the fused Landsat-like data were used to retrieve the LST in our study.…”

Section: Land Surface Temperature Retrievalmentioning

confidence: 99%

Nature-based Solutions for Urban Resilience and Human Health

2024

Frontiers Research Topics

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Climate change and rapid urbanization have significant impacts on biodiversity and ecosystem functions and services. Nature-based solutions (NBS) is an action to work with and enhance nature to solve social challenges, and NBS is an "umbrella concept" for other mature nature-based approaches. Blue-green spaces (BGS) can provide a wide range of ecosystem services, including mitigation of urban heat island effects, reduction of flooding, mitigation of air pollution, and provision of recreational spaces, thereby promoting physical and mental health. Hence, NBSs can serve as cost-effective climate mitigation and adaptation tool that contribute to additional co-benefits for ecosystem health and human well-being. Environmentalists, epidemiologists, ecologists, urban planners, and policymakers have paid more attention to NBSs for urban resilience and human health. In this Research Topic, we hope to discuss these topics: (1) ecological exposure and health benefits; (2) climate adaptation and human health promotion possibilities by NBSs; (3) methodological and theoretical approaches as well as technologies of NBSs corresponding to urban resilience; (4) underlying pathways and potential mechanisms of NBSs in improving human health; and (5) policies and management for planning and design of the successful implementation of NBSs in relation to urban resilience and human health. This Research Topic focuses on, but is not restricted to the following issues: • Nature-based interventions for climate adaptation. • Ecological exposure and physical and psychological health outcomes. • Climate adaption environmental policies and management.

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Section: Land Surface Temperature Retrievalmentioning

confidence: 99%

Nature-based Solutions for Urban Resilience and Human Health

2024

Frontiers Research Topics

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“…This paper chooses remote sensing data acquired by the on Landsat 8, which was launched on 11 February 2013. The OLI collects nine spectral bands in various wavelengths of visible, near-infrared, and sh enabling it to observe a swath of the Earth 185 km (115 miles) wide with a 15-30 m. The TIRS bands 10-11 are originally captured at a resolution of 10 are resampled to 30 m to align with the resolution of OLI multispectral band infrared band offers a resolution of 100 m, revisiting every 16 days, and ha detect temperature variations within the range of 0.2-0.5 °C [48,49].…”

Section: Landsat 8 Datamentioning

confidence: 99%

“…However, the RTE algorithm requires real-time atmospheric profile parameters, which is not suitable for batch inversion of LST. The SW algorithm, which requires data from two thermal infrared channels of Landsat 8, is also not recommended due to the incomplete calibration parameters of band 11 [49]. Therefore, in this study, we use the MW algorithm for LST batch inversion, employing multi-temporal Landsat series image data in GEE.…”

Section: Lst Computationmentioning

confidence: 99%

Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau

Li,

Jiang,

Tang

et al. 2023

Remote Sensing

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Geothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST readings are influenced by various factors such as sun radiation, cyclical variations, and precipitation, which can mask the temperature anomalies caused by geothermal heat. To address these issues and highlight the LST anomalies caused by geothermal heat, this paper proposes a methodology to efficiently and quickly calculate the multi-temporal LST leveraging of the Google Earth Engine (GEE) in the Damxung–Yangbajain basin, Qinghai–Tibet Plateau. This method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The existing geothermal manifestations are used as a benchmark to further refine the methodology. The results indicate that the annual mean winter LST is a sensitive indicator of geothermal anomaly signals. The annual mean winter LST between 2015 and 2020 varied from −14.7 °C to 26.7 °C, with an average of 8.6 °C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from −22.1 °C to 23.3 °C, with an average of 6.2 °C. When combining the distribution of faults with the results of the annual mean winter LST, this study delineated the geothermal potential areas that are located predominantly around the fault zone at the southern foot of the Nyainqentanglha Mountains. Geothermal potential areas exhibited a higher LST, ranging from 12.6 °C to 23.3 °C. These potential areas extend to the northeast, and the thermal anomaly range reaches as high as 19.6%. The geothermal potential area makes up 8.2% of the entire study area. The results demonstrate that the approach successfully identified parts of known geothermal fields and indicates sweet spots for future research. This study highlights that utilizing the multi-temporal winter LST is an efficient and cost-effective method for prospecting geothermal resources in plateau environments.

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“…However, urban surfaces are extremely complex, with varied surface components and materials with different thermal properties. In addition, urban surfaces contain complex three-dimensional (3-D) structures, which further exacerbate LST heterogeneity [1][2][3]. Satellite thermal remote sensing suffers from a tradeoff between the spatial and temporal resolutions of LST, which greatly limits the application of LST in urban systems.…”

Section: Introductionmentioning

confidence: 99%

Localized Downscaling of Urban Land Surface Temperature—A Case Study in Beijing, China

Ouyang

2022

Remote Sensing

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High-resolution land surface temperature (LST) data are essential for fine-scale urban thermal environment studies. Urban LST downscaling studies mostly remain focused on only two-dimensional (2-D) data, and neglect the impact of three-dimensional (3-D) surface structure on LST. In addition, the choice of window size is also important for LST downscaling over heterogeneous surfaces. In this study, we downscaled Landsat-LST using localized and stepwise approaches in a random forest model (RF). In addition, both 2- and 3-D building morphologies were included. Our results show that: (1) The performances of a local moving window and stepwise downscaling are dependent on the extent of surface heterogeneity. For mixed surfaces, a localized window performed better than the global window, and a stepwise approach performed better than a single-step approach. However, for monotonous surfaces (e.g., urban impervious surfaces), the global window performed better than a localized window; (2) That multi-scale geographically weighted regression (MGWR) could provide a possibility for selection of the optimal moving window. 7 × 7 windows derived from MGWR by the minimum bandwidth of predictors, performed better than other windows (3 × 3, 5 × 5, and 11 × 11) in the Beijing area; (3) That the morphology of buildings has a non-negligible impact and scaling effect on urban LST. When building morphologies were included in downscaling, the performance of the RF model improved. Furthermore, the importance of the sky view factor, building height, and building density was greater at a higher resolution than at a lower resolution.

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Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data

Cited by 22 publications

References 0 publications

Nature-based Solutions for Urban Resilience and Human Health

Nature-based Solutions for Urban Resilience and Human Health

Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau

Localized Downscaling of Urban Land Surface Temperature—A Case Study in Beijing, China

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