数据驱动的蒸散发遥感反演方法及产品研究进展  

Progress of data-driven remotely sensed retrieval methods and products on land surface evapotranspiration

作　　者：刘萌 唐荣林[2,3] 李召良 高懋芳[1] 姚云军[4] LIU Meng;TANG Ronglin;LI Zhaoliang;GAO Maofang;YAO Yunjun(Key Laboratory of Agricultural Remote Sensing,Ministry of Agriculture and Rural Affairs,Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China;State Key Laboratory of Resources and Environment Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;State Key Laboratory of Remote Sensing Science,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China)

机构地区：[1]中国农业科学院农业资源与农业区划研究所,农业农村部农业遥感重点实验室,北京100081 [2]中国科学院地理科学与资源研究所,资源与环境信息系统国家重点实验室,北京100101 [3]中国科学院大学,北京100049 [4]北京师范大学,地理科学学部遥感科学国家重点实验室,北京100875

出　　处：《遥感学报》2021年第8期1517-1537,共21页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42001310,41922009,42071332);中央级科研院所基本科研业务费专项(编号:1610132020007)。

摘　　要：蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。Evapotranspiration(ET)links the water cycle and energy exchange in hydrosphere,atmosphere,and biosphere.From a global perspective,ET accounts for approximately 60%of the total land precipitation,and the latent heat accompanying ET accounts for approximately 80%of the total surface net radiation energy.With the development of eddy covariance technology,global long-term and continuous observed meteorological and flux data are publicly available online.In last decade,data-driven remotely sensed ET retrieval methods have achieved rapid development.In terms of data-driven remotely sensed retrieval ET,this paper reviews and summarizes the existing researches on empirical regression methods,machine learning methods,data fusion methods and their corresponding products,then points out the existing problems and deficiencies on the driven data,retrieval methods,and available products.To be specific,these problems include:(1)There are few data-driven remotely sensed ET products with high precision and high spatiotemporal resolution;(2)The spatial scale mismatch between satellite pixel and in situ measurements makes the data-driven remotely sensed ET estimates challenging;(3)The lack of physical mechanisms for the data-driven remotely sensed retrieval ET methods and the insufficient regional representativeness for observed data from hundreds of sites,the spatial application of the ET model is limited;(4)Several important driving factors of ET,such as land surface temperature and soil moisture,were not sufficiently considered in previous studies;(5)The energy balance at flux measurement sites that based on eddy covariance method is not closed with about 0.8 unclosed rate globally,whether carry out energy balance closure correction before modeling is still a controversy;(6)The partitioning between soil evaporation and vegetation transpiration is of great significance,but the data-driven remotely sensed models that could estimate soil evaporation and vegetation transpiration respectively were not well studied.In the era of big d

关 键 词：蒸散发 数据驱动 地表温度 经验方法 机器学习 数据融合 遥感产品 

分 类 号：P426.2[天文地球—大气科学及气象学] P407