A review of the estimation methods for daily mean temperatures using remote sensing data

doi:10.6046/zrzyyg.2022338

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Abstract

Daily mean temperatures, as a primary indicator reflecting climatic characteristics, play a decisive role in monitoring urban heat island effects and agroecological environments. However, daily mean temperatures measured at meteorological stations lack spatial representativeness in regional-scale models. By contrast, the inversion results of daily mean temperatures using remote sensing data can better accommodate the large-scale monitoring needs, but with insufficient accuracy and quality. This study presented several common estimation methods for daily mean temperatures using remote sensing data, including multiple linear regression, machine learning, and feature space-based extrapolation. Then, based on the principle and process for estimation of daily mean temperatures using remote sensing data, this study systematically analyzed the effects of uncertainties such as clouds and aerosols and offered corresponding solutions. Finally, this study predicted the development trend of such estimation methods. Additionally, this study posited that image fusion and multi-source data fusion at different transit times can significantly improve the estimation accuracy under cloud interference.

Keywords daily mean temperature inversion of remote sensing data estimation method cloud interference

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Issue Date: 21 December 2023

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	Yan WANG
	Licheng WANG
	Jinwen WU

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Yan WANG,Licheng WANG,Jinwen WU. A review of the estimation methods for daily mean temperatures using remote sensing data[J]. Remote Sensing for Natural Resources, 2023, 35(4): 1-8.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022338 OR https://www.gtzyyg.com/EN/Y2023/V35/I4/1

Tab.1 List of methods for estimating daily mean temperature by remote sensing

Fig.1 Schematic diagram of NDVI-T_s characteristic space^[41]

特征空间模型	模型算法^①	算法精度	文献来源
TVX	$T s = a t, i + b t, i N D V I$ $T t, i = a t, i + b t, i N D V I m a x$ $T a, t = T m i n + (T m a x - T m i n) s i n [π (t + t d l 2 - 12) / (t d l + 2 t T m a x)]$	RMSE为3.43 ℃ MAE为2.54 ℃	Zhu等^[33]
NDVI-LST空间三角形和梯形	$T L S T N D V I i, m a x = b 1 + c 1 + N D V I i$ $T L S T N D V I i, m i n = b 2 + c 2 + N D V I i$ $T a = T L S T N D V I i - T L S T N D V I i, m i n T L S T N D V I i, m a x - T L S T N D V I i, m i n (T a, m a x - T a, m i n)$	稀疏植被覆盖区 MAE为1.5~1.8 ℃ 中高植被覆盖区MAE为1.61 ℃	侯英雨等^[34]
EVI-LST空间三角形和梯形	$T a m e a n = 1 - E V I c 1 - E V I (T s d a y - T s n i g h t) + T s n i g h t$	平原区RMSE为1.84 K 山区RMSE为2.34 K 高山区RMSE为2.45 K	Sun等^[44]

Tab.2 List of methods for air temperature estimation extrapolated from characteristic space

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