Detecting heat-inducing urban built-up surface material with multi remote sensing datasets using reflectance and emission spectroscopy

Kamaraj, Nivedita Priyadarshini; Shekhar, Sulochana; Sivashankari, V.; Balasubramani, K.; Prasad, Kumar Arun

doi:10.1016/j.rse.2021.112591

Cited by 6 publications

(4 citation statements)

References 70 publications

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“…The ECOSTRESS experimental mission, which focuses on measuring plant temperature to better understand plant water requirements and how they relieve stress, consists of four levels of data, from which the level 2 product LST&E data and cloud mask product have been widely used for agricultural crop monitoring, volcanic surface temperature estimation, and studies of urban thermal environments with good accuracy ( 60 , 61 ). In a systematic and comprehensive evaluation study of different satellite LST products, Li et al ( 48 ) found that ECOSTRESS data had the highest consistency with ground-based observations during the day, with absolute deviations of <1.89°C at night.…”

Section: Methodsmentioning

confidence: 99%

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Shi

Yang

Wang

et al. 2022

Front. Public Health

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High urban temperatures affect city livability and may be harmful for inhabitants. Analyzing spatial and temporal differences in surface temperature and the thermal impact of urban morphological heterogeneity can promote strategies to improve the insulation of the urban thermal environment. Therefore, we analyzed the diurnal variation of land surface temperature (LST) and seasonal differences in the Fifth Ring Road area of Beijing from the perspective of the Local Climate Zone (LCZ) using latest ECOSTRESS data. We used ECOSTRESS LST data with a resolution of 70 m to accurately interpret the effects of urban morphology on the local climate. The study area was dominated by the LCZ9 type (sparse low-rise buildings) and natural LCZ types, such as LCZA/B (woodland), LCZD (grassland), and LCZG (water body), mainly including park landscapes. There were significant differences in LST observed in different seasons as well as day and night. During daytime, LST was ranked as follows: summer > spring > autumn > winter. During night-time, it was ranked as follows: summer > autumn > spring > winter. All data indicated that the highest and lowest LST was observed in summer and winter, respectively. LST was consistent with LCZ in terms of spatial distribution. Overall, the LST of each LCZ during daytime was higher than that of night-time during different seasons (except winter), and the average LST of each LCZ during the diurnal period in summer was higher than that of other seasons. The LST of each LCZ during daytime in winter was lower than that of the corresponding night-time, which indicates that it is colder in the daytime during winter. The results presented herein can facilitate improved analysis of spatial and temporal differences in surface temperature in urban areas, leading to the development of strategies aimed at improving livability and public health in cities.

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Section: Methodsmentioning

confidence: 99%

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Shi

Yang

Wang

et al. 2022

Front. Public Health

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“…This means that both sensors have picked up a large portion of the area differently. For in the northern parts (Rihan et al, 2021), which generally exhibits spectral values that are pretty similar to the built-up land (Kotthaus et al, 2014;Kamaraj et al, 2021). Furthermore, the sparse vegetation along the open land and the fringe of the built-up area has been detected as built-up area because of the similar spectral values.…”

Section: A N U S C R I P Tmentioning

confidence: 99%

Comparative evaluation of operational land imager sensor on board landsat 8 and landsat 9 for land use land cover mapping over a heterogeneous landscape

Shahfahad

Talukdar

Naikoo

et al. 2023

Geocarto International

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Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat OLI and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy (83.4%) than OLI (92.4%). The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran's I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor.

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“…2 estimated from meteorological stations based on radiance measurements for point locations (Becker and Li, 1995;Hale et al, 2011) but more recently, thermal remote sensing-based approaches primarily in the Thermal Infrared (TIR) region of the Electromagnetic (EM) spectrum have been used for LST retrieval (Zhao and Li, 2013;Petropoulos et al, 2020;Kamaraj et al, 2021). TIR sensors map the radiation emitted by the earth in a range of 8-15 µm and measure the radiation emitted by the ground to determine the surface temperature.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Land Surface Temperature at Farm-Scale Using Multi-Model Approaches over Diverse Agro Climatic Zones

Roy,

Das,

Rathore

et al. 2024

Preprint

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Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current land surface temperature estimates for field-level hydrological, agricultural, and ecological operations is still challenging due to their coarse spatiotemporal resolution. In the current article, we have downscaled 100 m LST to 10 m by using high-resolution Sentinel-1,2 optical-microwave data and three different models- 1) Thermal Sharpening (TsHARP); 2) Thin Plate Spline (TPS); and 3) Random Forest (RF). The extensive analysis was performed at agricultural farms in the semi-arid (IARI) region of India during the winter and summer seasons of 2020-21 and 2021-22, arid (CAZRI) and per-humid (UBKV) region (2021-2022). The calibration accuracy of the RF model was shown to better agreement with the coefficient of determination (R2) of 0.982, root means square error (RMSE) 0.181 and normalized root means square error (RMSE) 0.061 with their lower values of standard errors over three diverse agro-climatic zones. The findings demonstrate that the validation accuracy of models' varied according to the agroclimatic zones, although RF and TPS consistently outperformed as compared to the TsHARP models.

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Detecting heat-inducing urban built-up surface material with multi remote sensing datasets using reflectance and emission spectroscopy

Cited by 6 publications

References 70 publications

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Comparative evaluation of operational land imager sensor on board landsat 8 and landsat 9 for land use land cover mapping over a heterogeneous landscape

Downscaling Land Surface Temperature at Farm-Scale Using Multi-Model Approaches over Diverse Agro Climatic Zones

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