Sentinel-3 SLSTR数据的组分温度反演  

Component temperature inversion algorithm based on Sentinel-3 SLSTR data

作　　者：康童健 郭明珠 曹彪 任华忠[1] 范闻捷[1] KANG Tongjian;GUO Mingzhu;CAO Biao;REN Huazhong;FAN Wenjie(Institution of Remote Sensing and Geographical Information System,Peking University,Beijing 100871,China;State Key Laboratory ofRemote Sensing Science,Aerospace Information Research Institute of Chinese Academy of Sciences,Beijing 100101,China)

机构地区：[1]北京大学,遥感与地理信息系统研究所,北京100871 [2]中国科学院空天信息创新研究院,遥感科学国家重点实验室,北京100101

出　　处：《遥感学报》2021年第8期1671-1682,共12页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家重点研发计划(编号:2017YFE0122400);国家自然科学基金(编号:41571329,41871258)。

摘　　要：陆表温度是全球气候观测系统的基本变量。由于地表目标多数为具有三维结构、组成成分复杂、温度分布不均一的混合像元,因此相较于平均温度,组分温度具有更明确的物理意义和应用价值,对地—气相互作用过程和水分循环的定量分析研究具有重要意义。本研究针对Sentinel-3 SLSTR数据,基于CBT-P模型和CE-P模型构建了植被—土壤组分温度反演框架,并分析了劈窗算法和LAI对反演误差的影响。使用小汤山、漯河、塞罕坝3地实测数据进行结果验证,结果表明,5个验证点的植被组分温度反演结果的绝对偏差在0.1—1.6 K之间,平均绝对偏差为1.1 K,土壤组分温度反演结果的绝对偏差在0.5—1.4 K之间,平均绝对偏差为0.8 K,在中纬度的稀疏连续植被和垄行植被冠层中取得了较高的精度,初步证明了本研究提出的Sentinel-3 SLSTR双通道双角度地表组分温度反演算法的可行性。Land surface temperature is the basic variable of the global climate observation system.Since most of the surface targets are mixed pixels with three-dimensional structure,complex composition,and uneven temperature distribution,compared with the average temperature,the component temperature has a clearer physical meaning and application value which is of great significance for the earthatmosphere interaction process and quantitative analysis of water cycle.Vegetation and soil are two ground objects with completely different thermal properties in the vegetation-soil system.Obtaining accurate vegetation/soil component temperature is considered a prerequisite for improving the surface energy balance model.To develop component temperature inversion technology,this study proposes a new component temperature inversion algorithm based on Sentinel-3 SLSTR data.Based on the CBT-P model and the CE-P model,this study constructed a vegetation-soil component temperature inversion framework,and analyzed the impact of the split window algorithm and LAI on the inversion error.946 clear sky atmospheric profiles were selected and the split window algorithm was used to obtain the surface brightness temperature.Then,LAI retrieved from the VNIR bands is used together with the measured component emissivity to be presented into the CE-P model to calculate the emissivity matrix.Finally,build an inversion framework based on the CBT-P model,and input the surface radiant brightness temperature and emissivity matrix to invert the component temperature.The results are verified using the measured data from Xiaotangshan,Luohe and Saihanba that show a high inversion accuracy can be achieved.The site vegetation types in these three places are mainly wheat and grassland,which belong to sparse vegetation and ridge crops respectively.At 5 sites,the vegetation component temperature retrieval error is between 0.1—1.6 K,and the average absolute error is 1.1 K.The soil component temperature retrieval error is between 0.5—1.4 K,and the average absol

关 键 词：遥感 植被冠层 组分温度 劈窗算法 CE-P模型 CBT-P模型 Sentinet-3 SLSTR 

分 类 号：P407[天文地球—大气科学及气象学] TP79[自动化与计算机技术—检测技术与自动化装置]