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自然资源遥感  2023, Vol. 35 Issue (4): 1-8    DOI: 10.6046/zrzyyg.2022338
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日平均气温遥感估算方法综述
王岩1(), 汪利诚1, 武晋雯2,3()
1.沈阳建筑大学交通与测绘工程学院,沈阳 110168
2.中国气象局大气环境研究所,沈阳 110166
3.辽宁省农业气象灾害重点实验室,沈阳 110166
A review of the estimation methods for daily mean temperatures using remote sensing data
WANG Yan1(), WANG Licheng1, WU Jinwen2,3()
1. School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China
2. Institute of Atmospheric Environment, CMA, Shenyang 110166, China
3. Key Laboratory of Agrometeorological Disasters, Shenyang 110166, China
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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。
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王岩
汪利诚
武晋雯
关键词 ： 日平均气温,  遥感反演,  估算方法,  云影响    
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.
Key words： daily mean temperature    inversion of remote sensing data    estimation method    cloud interference
收稿日期: 2022-08-16      出版日期: 2023-12-21
ZTFLH:  TP79  
基金资助:中青年科技创新人才支持计划项目“阴影校正及颜色纹理耦合对火烧迹地信息提取的影响研究”(RC210431);风云卫星应用先行计划二期项目“FY-3E微光成像仪在东北地区干旱和火点监测中的应用研究”(FY-APP-2021.0302);辽宁省民生科技计划项目“农业气象信息化防灾减灾关键技术集成与体系构建”(2021JH2/10200024);辽宁省教育厅科学研究项目“基于多源数据融合的建筑物三维重建关键问题研究”(lnjc202015);中央级公益性科研院所结余资金项目“基于深度学习和多源遥感数据的东北地区干旱监测研究”(2022YIAEJY3)
通讯作者: 武晋雯(1980-),女,硕士,研究员,主要从事生态环境领域的遥感应用研究。Email: pipi824@126.com。
作者简介: 王岩(1979-),男,硕士,副教授,主要从事精密工程测量及多源数据的融合与应用研究。Email: wyan413@163.com。
引用本文:   
王岩, 汪利诚, 武晋雯. 日平均气温遥感估算方法综述[J]. 自然资源遥感, 2023, 35(4): 1-8.
WANG Yan, WANG Licheng, WU Jinwen. A review of the estimation methods for daily mean temperatures using remote sensing data. Remote Sensing for Natural Resources, 2023, 35(4): 1-8.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022338      或      https://www.gtzyyg.com/CN/Y2023/V35/I4/1
模型类型 算法类型 适用范围 文献来源
多元线性回归法 全子集回归方法 适合平原地区的日平均气温的估算,山区估算误差较大 姚永慧等[21];
Colombi等[22];
Zhang等[23];
Benali等[24];
Yang等[25];
Golkar等[26]
标准化回归系数方法
机器学习方法 随机森林 适用于山区,地形起伏大,复杂的地区日平均气温估算 Agathangelidis等[27]; Li等[28]; Moser等[29];
Zhang等[30]; Ho等[31]
支持向量机
神经网络
特征空间外推法 NDVI-LST梯形空间外推法 适用于中、高植被覆盖区域的日平均气温的估算 Sun等[32]; Zhu等[33];侯英雨等[34]
NDVI-LST三角形特征空间外推法
Tab.1  日平均气温遥感估算方法一览表
Fig.1  NDVI-Ts特征空间示意图[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  特征空间外推估算气温方法一览表
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