Dynamics of Land Surface Temperature with Changing Land-Use: Building a Climate ResilientSmart City

Shafia, A.; Nimish, G.; Hariharan, Bharath

doi:10.1109/i2ct.2018.8529459

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…As a crucial parameter in the global energy balance and environmental monitoring [1][2][3][4], land surface temperature (LST) plays an essential role in evapotranspiration estimation [5][6][7][8] monitoring [9], fire detection [10,11], and global climate change studies [12]. Remote sensing is the only possible technique that can obtain high-spatiotemporal LST at localized and worldwide scales [13,14].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product

Zhang

Duan

et al. 2019

Remote Sensing

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Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.

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Section: Introductionmentioning

confidence: 99%

Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product

Zhang

Duan

et al. 2019

Remote Sensing

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“…Accuracy assessment was performed to evaluate the classification, with the help of ground truth data and Google earth. Confusion matrix/error matrix was created and overall accuracy and kappa coefficient was estimated (Shafia et al, 2018…”

Section: Land Use Analysismentioning

confidence: 99%

Understanding Current and Future Landuse Dynamics With Land Surface Temperature Alterations: A Case Study of Chandigarh

Nimish

Chandan

Hariharan

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Conversion of pervious layer to impervious layer through unplanned urbanization has been a major cause of natural disturbances across the world. However, urbanization is considered a metric that defines the socio-economic value of the city planning and management, if unplanned leads to many serious implications on the environment such as ecological imbalance, increased concentration of pollutants, loss of bio-diversity, etc. A steep increase in population growth, migration to cities and conversion of vegetated lands into other land use classes have been prime factors in expanding urban regions at faster urban growth rates causing sprawl in the periphery and the regions adjacent to the city resulting in increased temperatures. This paper addresses two major facades of urban growth: modelling urban growth pattern with business as usual scenario and understanding the land surface temperature (LST) dynamics with changing patterns of land use. Chandigarh administrative boundary with 10 km buffer has been considered for this analysis to understand the dynamics and sprawl. The outcome indicates that built-up has increased from 3.7% in 1991 to 16.16% in 2017 and the mean LST of the study area has increased by almost 12&deg;C (months of March–May). Future prediction performed shows that there would be an extensive outgrowth due to saturated infilling in the core of the city. This analysis would be helpful to planners to understand region specific growth and transitions.

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Spatio-temporal dynamic land cover changes and their impacts on the urban thermal environment in the Chittagong metropolitan area, Bangladesh

et al. 2020

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Dynamics of Land Surface Temperature with Changing Land-Use: Building a Climate ResilientSmart City

Cited by 4 publications

References 11 publications

Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product

Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product

Understanding Current and Future Landuse Dynamics With Land Surface Temperature Alterations: A Case Study of Chandigarh

Spatio-temporal dynamic land cover changes and their impacts on the urban thermal environment in the Chittagong metropolitan area, Bangladesh

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