积雪季森林冠层微波透过率半经验模型  

A semi-empirical microwave transmissivity model for forest canopies during the snow season

作　　者：杨建卫 蒋玲梅 武胜利 栾英宏 潘金梅 施建成 YANG Jianwei;JIANG Lingmei;WU Shengli;LUAN Yinghong;PAN Jinmei;SHI Jiancheng(State Key Laboratory of Remote Sensing Science,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China;National Satellite Meteorological Center(National Center for Space Weather),Beijing 100081,China;Innovation Center for FengYun Meteorological Satellite(FYSIC),Beijing 100081,China;Shanghai Aerospace Electronic Technology Institute,Shanghai 201109,China;National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]遥感科学国家重点实验室北京师范大学地理科学学部,北京100875 [2]国家卫星气象中心(国家空间天气监测预警中心),北京100081 [3]许健民气象卫星创新中心,北京100081 [4]上海航天电子技术研究所,上海201109 [5]中国科学院国家空间科学中心,北京100190

出　　处：《遥感学报》2024年第4期981-994,共14页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42090014,42201346);中央高校基本科研业务费专项资金(编号:2021NTST02);中国航天科技集团有限公司第八研究院产学研合作基金资助项目(编号:SAST2020-082);风云卫星应用先行计划(2022)(编号:FY-APP-2022.0305)。

摘　　要：星载被动微波遥感是获取宏观尺度雪深时空分布的重要手段。森林冠层不仅衰减了来自地表的微波辐射,同时自身也是一个热辐射源,因此森林冠层增加了被动微波遥感反演雪深的不确定性。本研究基于植被辐射传输tau-omega模型(τ-ω)提出了相邻像元(森林和非森林)的冠层微波透过率提取方法,探索冠层微波透过率模型在卫星尺度的应用。该方法假设相邻的森林和非森林像元存在相同的积雪和环境参数,通过联立相邻像元的辐射传输方程从理论上推算冠层微波透过率,进而借助森林生物量建立森林透过率的半经验估算模型。通过对比微波辐射模型模拟亮温和AMSR2卫星观测亮温,发现未经过森林辐射校正的亮温(18.7 GHz和36.5 GHz)往往存在低估现象,而经过森林辐射校正后的模拟亮温更接近于卫星观测;通过留一法(Leave-One-Out Cross Validation)对发展的透过率半经验模型验证,发现反演的透过率与参考值相关性高于0.7,在18.7 GHz和36.5 GHz频段的均方根误差RMSE分别为0.0589和0.0787;通过分析高低频亮温差(Tb18.7V-Tb36.5V)与雪深的关系,发现相关系数由森林辐射校正前的0.26提高到校正后的0.46;最后利用传统的经验性雪深反演算法对森林辐射校正效果进行测试,发现雪深反演误差(unRMSE)由原来的8.9 cm降低到7.8 cm,相关系数由0.32提高到0.49。本研究发展的冠层微波透过率半经验模型可以实现卫星遥感尺度的应用,为提高林区的雪深反演精度提供了参考和支撑。Spaceborne passive microwave remote sensing is a crucial technique for monitoring the global spatiotemporal distribution of snow depth.The forest canopy not only attenuates microwave radiation from the soil but also emits radiation into the sensor.Therefore,forest canopies increase the uncertainty of snow depth retrievals via passive microwave sensing.This research aimed to develop a microwave transmissivity model at the scale of satellite observations(0.25°×0.25°)to realize forest correction via satellite observations.The proposed novel method(hereafter referred to as the adjacent pixel approach)for estimating canopy transmissivity combines the radiative transfer functions of adjacent forests and open pixels.A semi-empirical transmissivity model based on forest biomass was built to correct satellite-observed brightness temperatures.The modeling brightness temperature data were compared with the AMSR2 observations in Northeast China to demonstrate the ability of the proposed transmissivity model to retrieve snow depth.As forest canopy effects were ignored by the microwave emission model,the brightness temperature was somewhat underestimated with respect to the satellite observations.By contrast,the proposed method corrected the information by using AMSR2 observations;hence,the model simulations were much closer to the AMSR2 observations.Then,the proposed semi-empirical microwave transmissivity model was further verified via the leave-one-out cross-validation method.The correlation coefficient between the estimates and reference values reached 0.7,and the RMSEs were 0.0589 and 0.0787 at 18.7 GHz and 36.5 GHz,respectively.The relationship between the brightness temperature spectral difference(Tb18.7V−Tb36.5V)and ground-based snow depth improved after forest correction,from 0.26 before correction to 0.46 after correction.An empirical retrieval algorithm was subsequently selected for testing to demonstrate the improvement in snow depth retrieval via forest radiation correction.The RMSE was 7.8 cm with forest rad

关 键 词：被动微波遥感 森林冠层 相邻像元法 微波透过率 雪深 

分 类 号：TP701[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]