Spatial interpolation of urban air temperatures using satellite-derived predictors

Nikoloudakis, N.; Stagakis, Stavros; Mitraka, Zina; Kamarianakis, Yiannis; Chrysoulakis, Nektarios

doi:10.1007/s00704-020-03230-3

Cited by 21 publications

(7 citation statements)

References 76 publications

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“…In 2020 Nikoloudakis N et al applied the hybrid methodology to predict Tair in urban areas without LST. They developed and used predictive models, which are based on urban morphological peculiarities such as land cover and terrain, and in situ Tair measurements from urban weather stations [10].…”

Section: Introductionmentioning

confidence: 99%

“…The area has a dry climate, and only 3 weather stations are operating, which limits the information on spatial variation of Tair. In other case studies from literature, like Athens and Heraklion (Greece), Los Angeles (USA), Seoul (South Korea), Vancouver (Canada), Erbil (Iraq), where Tair predictions were performed using remote sensing data [10,12,16,23] such a combination is never found.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Based Modelling of Air Temperature in the Complex Environment of Yerevan City, Armenia.

Tepanosyan¹,

Asmaryan²,

Muradyan³

et al. 2023

Preprint

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Machine Learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the PLSR model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to be strongly impacting on Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations such as precipitation and wind speed, and the use of non-parametric ML techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine-Learning Based Modelling of Air Temperature in the Complex Environment of Yerevan City, Armenia.

Tepanosyan¹,

Asmaryan²,

Muradyan³

et al. 2023

Preprint

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“…According to research data (Chalkias et al, 2020), as a rule, the market value is determined only by the purchasing power of contractors, and the cadastral value is determined mainly based on the market value, taking into account the establishment of correction factors based on the characteristics of the object (Kuokkanen, 2020), as well as the appraiser's subjective opinion concerning the real estate object (Figure 1). However, the market value is usually subjective (Abramovich et al, 2017), and in turn, the cadastral value is according to (Nikoloudakis et al, 2020;Nguyen et al, 2020) more reasonable; however, the studies (Isaikina et al, 2021, Garcia, 2021 contain evidence of its subjectivity, as its use is based on the opinion of the appraiser, which determines the relevant characteristics of the object, laid in the construction of calculations models. Their values directly affect the source of their pre-values by the form, in particular, residential or non-residential property (Waychunas, 2020;Pogosyan, 2021a, b).…”

Section: Introductionmentioning

confidence: 99%

Creating an interference model of the cadastral value of real estate: advantages for business

Kulchytskiy,

Aleshko,

Fomin

et al. 2023

Br. J. Ed., Tech. Soc.

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Resumo. Na formação de relações de mercado em países com economias em desenvolvimento, a avaliação cadastral de imóveis não é apenas uma forma de construir modelos matemáticos. Neste caso, um papel importante é atribuído às fontes de informação para a construção de modelos estatísticos confiáveis que levem em consideração as propriedades de interferência dos objetos imobiliários. A escolha do método de avaliação cadastral é confiada a especialistas altamente profissionais e com experiência relevante. Neste sentido, o objetivo desta investigação sobre os valores e as relações entre o valor de mercado e o valor cadastral do imóvel é determinar o seu impacto na forma de determinar a base tributável dos impostos prediais. Ao longo do estudo, as medidas propostas permitem o cálculo do valor cadastral, em primeiro lugar, para agilizar o atual sistema de formação de avaliações. Além disso, a utilização do fator de correção permitirá aos contribuintes estabelecer equidade na tributação dos impostos sobre a propriedade, uma vez que o valor cadastral é determinado por diferentes abordagens interferenciais, utilizando vários fatores de influência nos modelos. Método comparativo, método de custo, método de receita e combinação dos métodos propostos são utilizados. Face aos resultados, estas propostas são relevantes, especialmente nas actuais condições de influência da COVID-19 em todas as economias, bem como no clima de negócios das estruturas empresariais, uma vez que devido a factores macroeconómicos está a assumir uma tendência decrescente.

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“…However, low‐level clouds often have similar temperatures as the ground, depending on the atmospheric profile and the cloud base height (CBH). Thus, comparing ground‐based and satellite‐derived surface‐air temperature often indicates discrepancies between these two measurements (Jin et al., 1997; Kawashima et al., 2000; Nikoloudakis et al., 2020; T. Sun et al., 2020; Urban et al., 2013). The difference can be as significant as ±5°C at night (Good et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Observations of Nighttime Clouds Over Chiba, Japan, Using Digital Cameras and Satellite Images

2021

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Nighttime cloud cover (CC) is analyzed from the data observed for 34 months over Chiba, Japan, using continuously operated digital cameras. The normal camera works in the visible region, providing quantitative and temporal changes of nighttime CC. The second camera captures sky images without the near‐infrared cut filter, and by applying the thresholding method, we obtain information on the temporal changes of thin (pixel values between 72 and 115) and thick (>115) clouds. Comparing the CC from the visible camera with cloud fraction from Atmospheric Infrared Sounder (AIRS) shows that long‐term data from the camera are valuable for correcting the underestimation of the cloud fraction product from AIRS for low‐level clouds. Furthermore, a comparison between the near‐ground temperature and CC results in estimating the seasonal changes of feedback effects of upward thermal radiation from the ground. The current results reveal that thick clouds contribute to longwave cloud forcing (LWCF) below 100 Wm−2, while thin clouds with CC below 30% contribute to LWCF below 10.2 Wm−2.

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Spatial interpolation of urban air temperatures using satellite-derived predictors

Cited by 21 publications

References 76 publications

Machine-Learning Based Modelling of Air Temperature in the Complex Environment of Yerevan City, Armenia.

Machine-Learning Based Modelling of Air Temperature in the Complex Environment of Yerevan City, Armenia.

Creating an interference model of the cadastral value of real estate: advantages for business

Observations of Nighttime Clouds Over Chiba, Japan, Using Digital Cameras and Satellite Images

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