Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data

Bird, David Neil; Banzhaf, Ellen; Knopp, Julius; Wu, Wanben; Jones, Laurence

doi:10.3390/atmos13071152

Cited by 7 publications

(2 citation statements)

References 60 publications

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“…In previous studies, LST and NDVI were two important parameters for Ta estimation [47,62]. In this study, NDVI significantly influenced long-term Ta modeling, with a maximum relative importance of 25% in the GLASS combination.…”

Section: Discussionmentioning

confidence: 55%

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

Lin,

Fang,

Xia

et al. 2023

Remote Sensing

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Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R2 reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming.

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Section: Discussionmentioning

confidence: 55%

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

Lin,

Fang,

Xia

et al. 2023

Remote Sensing

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“…Meanwhile, the dense observation stations and new remote sensing data lay the foundations for further enhancing the accuracy of gridded datasets of air temperature. Therefore, research on near-surface UHI effects based on station observed air temperature should move towards the way of greater precision and refinement (Liu et al, 2021;Bird et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Shi,

Sun,

Luo

2024

Front. Environ. Sci.

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Compared with surface temperature, the near-surface temperature is more related with human health. However, extensive researches have been conducted on the UHI effect globally using surface temperature considering its accessibility. In this study, a comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta Region is investigated. This study first proposed a spatialization method suitable for air temperature in highly urbanized areas with complex land cover. Based on this method, a dataset of 1-km gridded air temperature is developed, and an in-depth analysis of the changes of near-surface and surface heat island is further carried out. Results show that both the near-surface urban heat island intensity (NSUHII) and surface urban heat island intensity (SUHII) are rather strong over the past 20 years, presenting similar spatial distributions as well. However, in the rapidly expanding urban areas especially during summer and winter seasons, the difference in magnitude and time variations (R) between NSUHII and SUHII are pronounced. Hence, adaptions and mitigation strategies on NSUHI and SUHI should be developed and implemented separately in such occasions, which is especially important for developed areas such as Yangtze River Delta Region.

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Seasonal Environmental Cooling benefits of urban green and blue spaces in arid regions

Abd-Elmabod,

Gui,

Liu

et al. 2024

Sustainable Cities and Society

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Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data

Cited by 7 publications

References 60 publications

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Seasonal Environmental Cooling benefits of urban green and blue spaces in arid regions

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