耦合双时相与邻近像元信息的极轨卫星地表组分温度反演  

Separating land surface component temperatures from Low Earth Orbit(LEO)satellite data by coupling of dual-time and multi-pixel data

作　　者：刘向阳 唐伯惠 李召良[1,3] LIU Xiangyang;TANG Bohui;LI Zhaoliang(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;Faculty of Land Resource Engineering,Kunming University of Science and Technology,Kunming 650093,China;State Key Laboratory of Resources and Environment Information System(LREIS),Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences,Beijing 100101,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]昆明理工大学,国土资源与工程学院,昆明650093 [3]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

出　　处：《遥感学报》2021年第8期1700-1709,共10页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:41871244);中国博士后科学基金(编号:2020M680774)。

摘　　要：与混合像元的地表温度相比,植被和土壤的组分温度具有更明确的物理意义。因此,本文提出了一种从具有广泛应用的极轨卫星地表温度产品中分离出植被和土壤组分温度的算法。该算法使用温度日变化模型作为桥梁连接极轨卫星一日内的两次观测,利用多像元数据进行模型求解,从而得到过境时刻的地表植被和土壤组分温度。论文针对MODIS数据开展了地表组分温度的反演,并利用实测站点数据和高分辨率卫星数据对反演结果进行了验证。结果表明,该算法可以提供合理的植被和土壤组分温度信息,反演温度的误差变化范围为1.4 K到2.5 K。此外,对观测时刻组合方式的分析表明该算法只需要一次白天观测和一次夜晚观测就可以得到精度较好的分离结果,并且两次观测可以来自于不同传感器,进一步表明了算法具有良好的可操作性。The component temperature encapsulates more physical meaning than Land Surface Temperature(LST)and better meets the requirements of estimating evapotranspiration,monitoring drought and other studies.The polar-orbit satellites can observe the entire globe with a high spatial resolution and a modest temporal resolution from 1980 to present,and therefore have more wide applications than geostationary satellites.For these reasons,the study focuses on the methodology for estimating vegetation and soil component temperatures from polar-orbit satellite data.To meet operational and accurate requirements,the study proposed to use multi-temporal and multi-pixel data to separate the vegetation and soil component temperature.Specifically,a well-studied Diurnal Temperature Cycles(DTC)model was applied to link the two observations on one day,and then the moving-window technology was used to add available observations for solving the retrieval model.In addition,a spatial weighting matrix was adopted to improve the limitation of using multi-pixel data.The proposed algorithm was implemented by using Moderate Resolution Imaging Spectroradiometer(MODIS)data,and was evaluated by using in-situ measurements on Skukuza site and high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)data,respectively.In the case of the validation of field data,the separation accuracy of component temperatures is about 2 K,and RMSEs of daytime vegetation,nighttime vegetation,daytime soil,and nighttime soil are 2.3 K,2.5 K,1.5 K and 1.9 K,respectively.The better performance at daytime is resulted from the fact that DTC model cannot describe the temperature decrease at night well.Regarding with the validation of ASTER data,the separation accuracies of the vegetation and soil component are 1.4 K and 1.7 K,respectively.The vegetation component is slightly overestimated(bias=0.3 K)while the soil component is slightly underestimated(bias=-0.7 K),which is because of the systematic error between MODIS LST and ASTER LST.Moreover,this

关 键 词：遥感 组分温度 极轨卫星 地表温度 多时相 多像元 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置] P407[自动化与计算机技术—控制科学与工程]