摘要
自从2003年5月31日,从陆地卫星-7发回的ETM+图像数据就存在缺陷。这是由于增强专题制图仪的扫描线校正器发生故障引起的。这些称为SLC-OF数据的图像有一些黑色的不存在任何数据的扫描行。丢失的数据约占全景数据的25%,使它们难以正常使用。但是,数据本身仍然保留了良好的辐射和几何性质,如加以妥善修复,仍可以在一些特殊领域中使用。首先介绍了如何使用自适应局部回归算法(ALR)恢复这些图像,然后使用修复后的图像反演武汉东湖的水质参数。结果表明:ALR算法可以对SLC-OF图像进行较好的修复,而且利用修复后的图像和东湖的地面水质监测数据,通过多元逐步回归分析,可以建立很好的叶绿素a、透明度、总磷以及总氮等水质参数的经验遥感反演模型,模型的相关系数R2分别为0.86、0.75、0.73和0.71。反演得到的水质参数分布与实际情况符合。这些数据有许多优点,如空间分辨率高、存档数据非常丰富、可以从NASA的服务器免费下载等。在其他遥感数据不足或无法获得的情况下,这些数据经过适当的修复,可以作为补充或替代数据使用。
ETM+ images from Landsat7 exists defects since May 31,2003.This is caused by the failure of the Scan Lines Corrector on the Enhanced Thematic Mapper.These images called SLC-OFF data have some black scan lines which have no data.The lost data is about 25% of whole scene,and this makes them difficult for normal use.However,the data itself still retains some good radiometric and geometric properties;it can be used in some special purposes if they are properly repaired.This paper described how to use the Adaptive Local Regression Algorithm(ALR)to restore these images,and then used the restored image to inverse the parameters of water quality of East Lake in Wuhan,China.The results show that,SLC-OF image could be repaired quite well by ALR algorithm,and the repaired image could be used to build quite good empirical retrieval models for water quality parameters such as Chla,SD,TP and TN in East Lake with in situ water quality monitoring data by multiple regression analysis.The square correlation coefficients(R2)of the models were 0.86,0.75,0.73 and 0.71,respectively.The distributions of the retrieval water quality parameters agreed with the actual situation of the lake quite well.The author suggests that ETM+ SLC-OFF data shouldn't be neglected.They have some advantages,such as high spatial resolution,very rich in archived data and can be downloaded for free from NASA's servers.After repaired properly as the paper shows,they can be used as supplement or substitution for other remote sensing data while it is insufficient or unavailable.
出处
《长江流域资源与环境》
CAS
CSSCI
CSCD
北大核心
2011年第1期90-95,共6页
Resources and Environment in the Yangtze Basin
基金
中国科学院知识创新工程重要方向项目(kzcx2-yw-141)
国家自然科学基金项目(51079137)