Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Feng, Yang; Gao, Chen; Tong, Xiaohua; Chen, Shurui; Lei, Zhenkun; Wang, Jiafeng

doi:10.3390/rs11020182

Cited by 65 publications

(43 citation statements)

References 67 publications

(99 reference statements)

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“…e normalized difference water index (NDWI) and normalized difference bareness index (NDBaI) were analyzed for delineating the water content in vegetation and identifying the bareness of soil, respectively [33,34]. Feng et al [35] found a strong correlation between LST and NDBI followed by NDWI and NVDI in Suzhou city. Furthermore, the urban index (UI) was found to have a high correlation with surface temperature in Harare, Zimbabwe [36].…”

Section: Introductionmentioning

confidence: 99%

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

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Section: Introductionmentioning

confidence: 99%

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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“…This assumption can be violated by rapidly changing vegetation conditions or large changes in soil moisture, but neither of these conditions typically is relevant to an urban environment. [15][16][17] Air temperature variations tend to be much smaller in magnitude than corresponding LST variations. Therefore, our method computes and applies normalized MODIS LST spatial anomalies to disaggregate daily maximum NLDAS air temperature; the normalization procedure accounts for this difference in magnitude of variation.…”

Section: Nldas Meteorological Reanalysis 1 × 1 Km 2 Downscaled Tempermentioning

confidence: 97%

Ground-truth of a 1-km downscaled NLDAS air temperature product using the New York City Community Air Survey

Eliezer

Johnson

Crosson

et al. 2019

J. Appl. Rem. Sens.

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Ground-truthing results are presented for a new 1-km air temperature product downscaled for New York City (NYC) from ∼12 km North American Land Data Assimilation System (NLDAS) air temperature data using 1 km moderate resolution imaging spectroradiometer surface temperature data. The downscaled product was compared against a unique highly spatially resolved ground-level ambient air temperature dataset collected through the New York City Community Air Survey (NYCCAS), a neighborhood level air pollution and temperature monitoring network, for the years 2009 and 2010. This work focuses on the spatial variation in daily minimum temperatures within the five counties that comprise NYC (∼784 km 2). Overall, the downscaled daily minimum temperature was well correlated with ground station data, with NYCCAS minimum temperatures being slightly higher. Minimum temperature R 2 values were 0.9 and 0.92, and mean absolute errors were 0.69°C and 0.86°C for years 2009 and 2010, respectively. The smallest differences between NYCCAS and the downscaled data were seen at lower temperatures, in less densely urbanized areas, and in areas with higher vegetative cover, suggesting systematic bias in the downscaled data related to land-use. The 1-km dataset discerned neighborhood level temperature differences in high-density urban situations with heterogeneous land cover. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Identification of the dominant LST-related factors minimizing their adverse effects, was recognized as an important challenge by recent studies [35,98]. Extensive research demonstrated that LST is closely linked to many parameters: land-use patterns, land coverage, landscape structures and land-use configuration [99,100].…”

Section: The Role Of Urban Features On Thermal Patternmentioning

confidence: 99%

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

et al. 2021

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This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

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Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Cited by 65 publications

References 67 publications

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Ground-truth of a 1-km downscaled NLDAS air temperature product using the New York City Community Air Survey

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

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