scholarly journals A New Material-Oriented TES for Land Surface Temperature and SUHI Retrieval in Urban Areas: Case Study over Madrid in the Framework of the Future TRISHNA Mission

2021 ◽  
Vol 13 (24) ◽  
pp. 5139
Author(s):  
Aurélie Michel ◽  
Carlos Granero-Belinchon ◽  
Charlène Cassante ◽  
Paul Boitard ◽  
Xavier Briottet ◽  
...  
Keyword(s):  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
State Of The Art ◽  
The Other ◽  
Empirical Relationships ◽  
Spectral Bands ◽  
Artificial Materials ◽  
The Future ◽  
Artificial Surface

The monitoring of the Land Surface Temperature (LST) by remote sensing in urban areas is of great interest to study the Surface Urban Heat Island (SUHI) effect. Thus, it is one of the goals of the future spaceborne mission TRISHNA, which will carry a thermal radiometer onboard with four bands at a 60-m spatial resolution, acquiring daytime and nighttime. In this study, TRISHNA-like data are simulated from Airborne Hyperspectral Scanner (AHS) data over the Madrid urban area at 4-m resolution. To retrieve the LST, the Temperature and Emissivity Separation (TES) algorithm is applied with four spectral bands considering two main original approaches compared with the classical TES algorithm. First, calibration and validation datasets with a large number of artificial materials are considered (called urban-oriented database), contrary to most of the previous studies that do not use a large number of artificial material spectra during the calibration step, thus impacting the LST retrieval over these materials. This approach produces one TES algorithm with one empirical relationship, called 1MMD TES. Second, two empirical relationships are used, one for the artificial materials and the other for the natural ones. These relationships are defined thanks to two calibration datasets (artificial-surface-oriented database and natural-surface-oriented database, respectively), one containing mainly artificial materials and the other mainly natural ones. Finally, in order to use two empirical relationships, a ground cover classification map is given to the TES algorithm to separate artificial pixels from natural ones. This approach produces one material-oriented TES algorithm with two empirical relationships, called 2MMD TES. In order to perform a complete comparison of these two addenda in the TES algorithm and their impact on the LST retrieval, both AHS and TRISHNA spatial resolutions are studied, i.e., 4-m and 60-m resolutions, respectively. Relative to the calibration of the TES algorithm, we conclude that (1) the urban-oriented database is more representative of the urban areas than previous databases from the state-of-the-art, and (2) using two databases (artificial-surface-oriented and natural-surface-oriented) instead of one prevents the overestimation of the LST over natural materials and the underestimation over artificial ones. Thus, for both studied spatial resolutions (AHS and TRISHNA), we find that the 2MMD TES outperforms the 1MMD TES. This difference is especially important for artificial materials, corroborating the above conclusion. Furthermore, the comparison with ground measurements shows that, on 4-m spatial resolution images, the 2MMD TES outperforms both the 1MMD TES and the TES from the state-of-the-art used in this study. Finally, we conclude that the 2MMD TES method, with only four spectral bands, better retrieves the LST over artificial and natural materials and that the future TRISHNA sensor is suited for the monitoring of the LST over urban areas and the SUHI effect.

The Use of Satellite TIR Time Series for Thermal Anomalies’ Detection on Natural and Urban Areas

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Malvina Silvestri ◽  
Federico Rabuffi ◽  
Massimo Musacchio ◽  
Sergio Teggi ◽  
Maria Fabrizia Buongiorno
Keyword(s):  
Time Series ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Time Series ◽  
Campi Flegrei ◽  
Thermal Anomalies ◽  
Pca Method ◽  
Temperature Time

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.

scholarly journals Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

2021 ◽  
Vol 10 (12) ◽  
pp. 809
Author(s):  
Jing Sun ◽  
Suwit Ongsomwang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Decomposition Analysis ◽  
Lulc Change ◽  
Main Components ◽  
The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

scholarly journals Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning

2019 ◽  
Vol 11 (8) ◽  
pp. 959 ◽  
Author(s):  
Yanwei Sun ◽  
Chao Gao ◽  
Jialin Li ◽  
Run Wang ◽  
Jian Liu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Form ◽  
Urban Areas ◽  
Thermal Environment ◽  
High Density ◽  
Cooling Effects ◽  
Urban Thermal Environment ◽  
The Impact

It is widely acknowledged that urban form significantly affects urban thermal environment, which is a key element to adapt and mitigate extreme high temperature weather in high-density urban areas. However, few studies have discussed the impact of physical urban form features on the land surface temperature (LST) from a perspective of comprehensive urban spatial structures. This study used the ordinary least-squares regression (OLS) and random forest regression (RF) to distinguish the relative contributions of urban form metrics on LST at three observation scales. Results of this study indicate that more than 90% of the LST variations were explained by selected urban form metrics using RF. Effects of the magnitude and direction of urban form metrics on LST varied with the changes of seasons and observation scales. Overall, building morphology and urban ecological infrastructure had dominant effects on LST variations in high-density urban centers. Urban green space and water bodies demonstrated stronger cooling effects, especially in summer. Building density (BD) exhibited significant positive effects on LST, whereas the floor area ratio (FAR) showed a negative influence on LST. The results can be applied to investigate and implement urban thermal environment mitigation planning for city managers and planners.

scholarly journals Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations

2021 ◽  
Vol 13 (24) ◽  
pp. 5003
Author(s):  
Elisa Castelli ◽  
Enzo Papandrea ◽  
Alessio Di Roma ◽  
Ilaria Bloise ◽  
Mattia Varile ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
State Of The Art ◽  
Spectral Response ◽  
High Spectral Resolution ◽  
Learning Techniques

In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.

scholarly journals Quantification of Urban Heat Island-Induced Contribution to Advance in Spring Phenology: A Case Study in Hangzhou, China

2021 ◽  
Vol 13 (18) ◽  
pp. 3684
Author(s):  
Yingying Ji ◽  
Jiaxin Jin ◽  
Wenfeng Zhan ◽  
Fengsheng Guo ◽  
Tao Yan
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Heat Island ◽  
Spring Phenology ◽  
Urban Heat ◽  
The Difference

Plant phenology is one of the key regulators of ecosystem processes, which are sensitive to environmental change. The acceleration of urbanization in recent years has produced substantial impacts on vegetation phenology over urban areas, such as the local warming induced by the urban heat island effect. However, quantitative contributions of the difference of land surface temperature (LST) between urban and rural (ΔLST) and other factors to the difference of spring phenology (i.e., the start of growing season, SOS) between urban and rural (ΔSOS) were rarely reported. Therefore, the objective of this study is to explore impacts of urbanization on SOS and distinguish corresponding contributions. Using Hangzhou, a typical subtropical metropolis, as the study area, vegetation index-based phenology data (MCD12Q2 and MYD13Q1 EVI) and land surface temperature data (MYD11A2 LST) from 2006–2018 were adopted to analyze the urban–rural gradient in phenology characteristics through buffers. Furthermore, we exploratively quantified the contributions of the ΔLST to the ΔSOS based on a temperature contribution separation model. We found that there was a negative coupling between SOS and LST in over 90% of the vegetated areas in Hangzhou. At the sample-point scale, SOS was weakly, but significantly, negatively correlated with LST at the daytime (R2 = 0.2 and p < 0.01 in rural; R2 = 0.14 and p < 0.05 in urban) rather than that at nighttime. Besides, the ΔSOS dominated by the ΔLST contributed more than 70% of the total ΔSOS. We hope this study could help to deepen the understanding of responses of urban ecosystem to intensive human activities.

scholarly journals THE EFFECT OF GREEN SPACE ON THE LAND SURFACE TEMPERATURE IN HUE CITY

2018 ◽  
Vol 55 (4C) ◽  
pp. 129
Author(s):  
Nguyen Bac Giang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Green Space ◽  
Satellite Image ◽  
Urban Green Space ◽  
Landsat 8 ◽  
Urban Green ◽  
Study Results

This paper presents the analysis of the effect of urban green space types on land surface temperature in Hue city. Data are collected with temperature monitoring results from each green space type and the interpretation of surface temperature based on Landsat 8 satellite image data to determine temperatures at different times of the year. Results showed that there was a significant correlation between types of urban green space and the surface temperature. Types of green space with a large area and vegetation indexes have a greater effect on temperature than areas with a smaller green space do. Green space types including forest green space, dedicated green space and agriculture green space have the most effect on the surface temperature. The forest area has the greatest influence on the temperature with a temperature difference of more than 1.6 degrees Celsius at 9:00 in the daytime. Besides, the results extracted from satellite images also show that the area of urban green space going to be reduced makes a contribution to increase the surface temperature of urban areas. The study results have established foundation for planning the green spaces in climate change challenges in Hue City.

scholarly journals Impact of urban environment on land surface temperature in local climate zones

2021 ◽  
Vol 333 ◽  
pp. 02008
Author(s):  
Anna Gosteva ◽  
Sofia Ilina ◽  
Aleksandra Matuzko
Keyword(s):  
Surface Temperature ◽  
Urban Environment ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Landsat 8 ◽  
Local Climate ◽  
Spatial Coverage ◽  
Artificial Environment ◽  
Local Climate Zones

The replacement of the natural landscape by artificial environment has led to changes in the ecosystem and physical properties of the surface, such as heat storage capacity, and thermal conductivity properties. These changes increase the difficulty of heat transfer between urban areas and the environment. Land surface temperature (LST) images from various satellites are widely used to represent urban thermal environments, which are more convenient and intuitive way. LST maps provide full spatial coverage, which distinguishes them from air temperature data obtained from meteorological stations. The study of LST according to the Landsat 8 data of Krasnoyarsk city over the past 10 years allowed the authors to talk about the observation of constant seasonal urban heat islands (UHI). For a more detailed consideration of the urban environment, this study further considers urban landscapes, thus the idea of local climate zone (LCZ) is introduced to study these diverse impacts in addition to the traditional map of LST. And analysis of the interaction of UHI and LCZ.

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