海面盐度多源遥感协同反演方法研究
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摘要
海面盐度是描述海洋状态的重要参数之一，研究其变化和分布规律对分析海洋自身特性以及了解海洋在海-气复杂系统中的作用有重要意义。海面盐度的获取也是气象学、生态学、水文学和渔业等其他学科与应用领域重点关注的研究对象。光学与微波遥感反演海面盐度各有优势与不足，有效协同利用两种遥感数据，将有助于提高海面盐度的反演精度。本文选取南海北部珠江口东岸香港海域为研究区，采用2009-2011年遥感数据和海面实测数据，研究海面盐度光学与微波遥感数据协同反演方法，提出基于ETM+与SAR数据的海面盐度协同反演模型，主要研究工作包括以下几个方面：
（1）分别研究光学与微波遥感的海面盐度反演机理，提出基于单一遥感数据的海面盐度反演算法。利用改进型经验光谱指数与半经验半物理海面辐射传输模型提取黄色物质浓度，根据高光谱数据反演海面盐度算法，以黄色物质浓度为中介物，间接反演海面盐度；利用SAR数据散射系数获取海面亮温，根据K-S模型，可从反演得到的海面亮温中提取海面盐度。
（2）从光学微波遥感数据级融合协同的角度，提出一种SAR插补ETM+缺失数据方法，利用SAR数据穿云透雾特性去除ETM+影像中云及其阴影影响，从而达到在云遮挡情况下提取海面盐度的目的。利用像元位置匹配转化算法，将SAR影像转化成为基于ETM+像元的无云无阴影影像，再将该无云影响影像像元与ETM+原影像有云影响像元做对应的像元替换，即可生成SAR插补ETM+缺失数据后的新ETM+影像。针对有云影响的光学影像，利用该缺失数据插补方法与高光谱数据反演海面盐度算法，可以获取得到一定精度的海面盐度。
（3）从光学微波遥感模型级耦合协同的角度，提出一种ETM+与SAR数据协同反演海面盐度的方法。利用ETM+与SAR数据分别提取得到的海面亮温与海面发射率，通过海面温度反演算法获取海面温度，将其与从SAR数据中提取得到海水介电常数一并代入德拜方程，即可建立基于光学与微波遥感模型耦合的海面盐度协同反演模型。经实测海面盐度数据验证，基于光学与微波遥感模型级耦合的海面盐度协同反演模型的反演精度较其它方法更高（复相关系数达到R2=0.8672，均方根误差为RMSE=0.6253），其协同反演结果明显优于单一遥感数据的反演结果。
本文的创新点是：（1）提出了光学微波遥感数据级融合协同反演的方法，可以推广应用到光学影像数据缺失（如云遮挡）情况下的海面盐度反演研究；（2）根据光学微波遥感模型级耦合协同反演方法，建立了基于ETM+与SAR数据的海面盐度协同反演模型，提高了海面盐度反演精度，增强了海面盐度反演模型的适应性。
Sea Surface Salinity is one of the important parameter to describe the state of theocean. It is significant to study the variation and distribution of sea surface salinity, sothat the characteristics of the ocean itself and the knowledge about the role it plays inthe complex ocean-atmosphere system could be better understood. Meanwhile,meteorology, ecology, hydrology, fishery and other disciplines also focus on theextraction of sea surface salinity. The sea surface salinity retrieved from optical ormicrowave remotely sensed data is of advantage and disadvantage respectively. Bycoordinating above two remote sensing data effectively, the retrieval of sea surfacesalinity could be improved in accuracy. In this article, the research area is located inthe Hong Kong waters, near the eastern Pearl River of the northern South China Sea;the sample data is remote sensing data and measured sea surface data which werecollected from the year of2009-2011; the experiment is carried out to study theretrieval of sea surface salinity by coordinating optical and microwave remote sensingdata. Ultimately, the coordinated retrieval model of sea surface salinity based onETM+and SAR data was proposed. The following main research work is conducted:
(1) From the perspective of single remote sensing data, study the mechanism ofsea surface salinity retrieved from optical and microwave data, and propose the seasurface salinity retrieval algorithm based on single remote sensing data. First, yellowsubstance concentration is obtained from the improved empirical spectral index andthe half empirical and half physical sea surface radiation transfer model; with yellowsubstance concentration as mediator, sea surface salinity is indirectly retrieved fromhyperion data algorithm. Secondly, sea surface brightness temperature is obtainedfrom the scattering coefficient of SAR data; sea surface salinity is collected fromretrieved sea surface brightness temperature on the basis of K-S model.
(2) From the perspective of coordinating optical and microwave remote sensingdata fusion, put forward a method in which SAR data interpolates ETM+missing data,and remove the cloud and its shadow in ETM+images with SAR data, which has theinherent characteristic, that is, SAR data could penetrate through the cloud and fog. According to pixel position matching algorithm, SAR images can be converted tocloudless and shadow-free images based on ETM+pixels; replace the original ETM+cloud pixels by the corresponding cloudless pixels, then, the new ETM+images aregenerated after SAR interpolates ETM+missing data. In view of optical imagesaffected by cloud, the missing data interpolation method coordinates with the seasurface salinity retrieved from hyperion data algorithm, which could obtain seasurface salinity in some accuracy.
(3) From the perspective of coordinating optical and microwave remote sensingmodel coupling, propose a method in which sea surface salinity is retrieved fromcoordinated ETM+and SAR data. The sea surface brightness temperature and seasurface emissivity are extracted from ETM+and SAR data respectively, the seasurface temperature is obtained from sea surface temperature retrieval algorithm, andthe sea water dielectric constant is extracted from SAR data; then, all above values areput into the Debye equation. Ultimately, the sea surface salinity coordinated retrievalmodel based on optical and microwave remote sensing model coupling is established.Verification analysis with the measured sea surface salinity shows that the coordinatedmodel displays higher multiple correlation coefficient (R2=0.8672) and smaller rootmean square error (RMSE=0.6253) than the other methods. The retrieved result ofcoordinated model is superior to any retrieved results using singe remote sensing data.
The innovations of this article are:(1) the proposal of the retrieval methodcoordinating optical and microwave remote sensing data fusion. The method can beapplied to sea surface salinity retrieval research under the circumstance of shortage ofcloudless optical images;(2) the establishment of sea surface salinity coordinatedretrieval model based on ETM+and SAR data, according to coordinated retrievalmethod based on optical and microwave remote sensing model. It is not only increasethe retrieval accuracy of sea surface salinity, but also improve the adaptability of thesea surface salinity retrieval method.
（1）分别研究光学与微波遥感的海面盐度反演机理，提出基于单一遥感数据的海面盐度反演算法。利用改进型经验光谱指数与半经验半物理海面辐射传输模型提取黄色物质浓度，根据高光谱数据反演海面盐度算法，以黄色物质浓度为中介物，间接反演海面盐度；利用SAR数据散射系数获取海面亮温，根据K-S模型，可从反演得到的海面亮温中提取海面盐度。
（2）从光学微波遥感数据级融合协同的角度，提出一种SAR插补ETM+缺失数据方法，利用SAR数据穿云透雾特性去除ETM+影像中云及其阴影影响，从而达到在云遮挡情况下提取海面盐度的目的。利用像元位置匹配转化算法，将SAR影像转化成为基于ETM+像元的无云无阴影影像，再将该无云影响影像像元与ETM+原影像有云影响像元做对应的像元替换，即可生成SAR插补ETM+缺失数据后的新ETM+影像。针对有云影响的光学影像，利用该缺失数据插补方法与高光谱数据反演海面盐度算法，可以获取得到一定精度的海面盐度。
（3）从光学微波遥感模型级耦合协同的角度，提出一种ETM+与SAR数据协同反演海面盐度的方法。利用ETM+与SAR数据分别提取得到的海面亮温与海面发射率，通过海面温度反演算法获取海面温度，将其与从SAR数据中提取得到海水介电常数一并代入德拜方程，即可建立基于光学与微波遥感模型耦合的海面盐度协同反演模型。经实测海面盐度数据验证，基于光学与微波遥感模型级耦合的海面盐度协同反演模型的反演精度较其它方法更高（复相关系数达到R2=0.8672，均方根误差为RMSE=0.6253），其协同反演结果明显优于单一遥感数据的反演结果。
本文的创新点是：（1）提出了光学微波遥感数据级融合协同反演的方法，可以推广应用到光学影像数据缺失（如云遮挡）情况下的海面盐度反演研究；（2）根据光学微波遥感模型级耦合协同反演方法，建立了基于ETM+与SAR数据的海面盐度协同反演模型，提高了海面盐度反演精度，增强了海面盐度反演模型的适应性。
Sea Surface Salinity is one of the important parameter to describe the state of theocean. It is significant to study the variation and distribution of sea surface salinity, sothat the characteristics of the ocean itself and the knowledge about the role it plays inthe complex ocean-atmosphere system could be better understood. Meanwhile,meteorology, ecology, hydrology, fishery and other disciplines also focus on theextraction of sea surface salinity. The sea surface salinity retrieved from optical ormicrowave remotely sensed data is of advantage and disadvantage respectively. Bycoordinating above two remote sensing data effectively, the retrieval of sea surfacesalinity could be improved in accuracy. In this article, the research area is located inthe Hong Kong waters, near the eastern Pearl River of the northern South China Sea;the sample data is remote sensing data and measured sea surface data which werecollected from the year of2009-2011; the experiment is carried out to study theretrieval of sea surface salinity by coordinating optical and microwave remote sensingdata. Ultimately, the coordinated retrieval model of sea surface salinity based onETM+and SAR data was proposed. The following main research work is conducted:
(1) From the perspective of single remote sensing data, study the mechanism ofsea surface salinity retrieved from optical and microwave data, and propose the seasurface salinity retrieval algorithm based on single remote sensing data. First, yellowsubstance concentration is obtained from the improved empirical spectral index andthe half empirical and half physical sea surface radiation transfer model; with yellowsubstance concentration as mediator, sea surface salinity is indirectly retrieved fromhyperion data algorithm. Secondly, sea surface brightness temperature is obtainedfrom the scattering coefficient of SAR data; sea surface salinity is collected fromretrieved sea surface brightness temperature on the basis of K-S model.
(2) From the perspective of coordinating optical and microwave remote sensingdata fusion, put forward a method in which SAR data interpolates ETM+missing data,and remove the cloud and its shadow in ETM+images with SAR data, which has theinherent characteristic, that is, SAR data could penetrate through the cloud and fog. According to pixel position matching algorithm, SAR images can be converted tocloudless and shadow-free images based on ETM+pixels; replace the original ETM+cloud pixels by the corresponding cloudless pixels, then, the new ETM+images aregenerated after SAR interpolates ETM+missing data. In view of optical imagesaffected by cloud, the missing data interpolation method coordinates with the seasurface salinity retrieved from hyperion data algorithm, which could obtain seasurface salinity in some accuracy.
(3) From the perspective of coordinating optical and microwave remote sensingmodel coupling, propose a method in which sea surface salinity is retrieved fromcoordinated ETM+and SAR data. The sea surface brightness temperature and seasurface emissivity are extracted from ETM+and SAR data respectively, the seasurface temperature is obtained from sea surface temperature retrieval algorithm, andthe sea water dielectric constant is extracted from SAR data; then, all above values areput into the Debye equation. Ultimately, the sea surface salinity coordinated retrievalmodel based on optical and microwave remote sensing model coupling is established.Verification analysis with the measured sea surface salinity shows that the coordinatedmodel displays higher multiple correlation coefficient (R2=0.8672) and smaller rootmean square error (RMSE=0.6253) than the other methods. The retrieved result ofcoordinated model is superior to any retrieved results using singe remote sensing data.
The innovations of this article are:(1) the proposal of the retrieval methodcoordinating optical and microwave remote sensing data fusion. The method can beapplied to sea surface salinity retrieval research under the circumstance of shortage ofcloudless optical images;(2) the establishment of sea surface salinity coordinatedretrieval model based on ETM+and SAR data, according to coordinated retrievalmethod based on optical and microwave remote sensing model. It is not only increasethe retrieval accuracy of sea surface salinity, but also improve the adaptability of thesea surface salinity retrieval method.
引文
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