A review of the estimation methods for daily mean temperatures using remote sensing data
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摘要: 日平均气温作为反映气候特征的重要指标,在城市热岛效应、农业生态环境等众多领域发挥着举足轻重的作用。气象站实测的日平均气温应用在大区域模型时,在空间上缺乏一定的代表性。相比之下,日平均气温遥感反演结果更能够满足大范围监测的需要,但同时也存在着精度和质量上的限制和挑战。首先,总结了几种目前广泛使用的日平均气温遥感估算算法,如多元线性回归方法、机器学习法和基于特征空间外推法; 其次,基于日平均气温遥感估算的原理和过程,系统分析了云、气溶胶等不确定因素的影响,并提出了相应的解决方案; 最后,对日平均气温遥感估算的发展趋势进行了展望,并指出了不同过境时刻影像融合和多源数据融合是提升云干扰下日平均气温遥感估算精度的重要途径。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.
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