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引用本文:龚绍琦,陆品廷,孙德勇,李云梅,吴鹏飞.HJ-1B/IRS热红外数据反演太湖水温的方法比较.湖泊科学,2016,28(3):645-653. DOI:10.18307/2016.0321
GONG Shaoqi,LU Pinting,SUN Deyong,LI Yunmei,WU Pengfei.Comparison of water surface temperature retrieval algorithms from HJ-1B/IRS thermal infrared data over Lake Taihu, China. J. Lake Sci.2016,28(3):645-653. DOI:10.18307/2016.0321
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HJ-1B/IRS热红外数据反演太湖水温的方法比较
龚绍琦1, 陆品廷1, 孙德勇1, 李云梅2, 吴鹏飞1
1.南京信息工程大学地理与遥感学院, 南京 210044;2.江苏省地理信息资源开发与利用协同创新中心, 南京 210023
摘要:
太湖是我国典型的富营养化湖泊,水温是影响太湖藻类生长的重要环境因子之一,我国环境减灾卫星HJ-1B搭载的红外多光谱相机IRS对太湖水温动态遥感监测具有较大的性能优势.利用6景过境太湖的IRS热红外遥感影像,分别采用单通道普适性算法、辐射传输模型法和单窗算法反演太湖水温,并与实测水温和同期的TERRA/MODIS温度产品进行对比.结果表明,普适性单通道算法反演水温偏高,而辐射传输模型法和单窗算法则偏低;3种算法反演水温的均方根误差在1.001 K以内,单窗算法反演精度最高,其次是辐射传输模型法,再次为普适性单通道算法,而同期MODIS温度产品的均方根误差为1.507 K.3种算法从IRS热红外数据反演的水温直方图均呈正峰态、尖峰状态分布,反演结果能真实地反映太湖水温的空间分布特征.本研究对只有单个热红外通道的卫星传感器开展内陆水体水温遥感监测具有一定的参考意义.
关键词:  太湖  水温  HJ-1B/IRS  热红外数据  算法
DOI:10.18307/2016.0321
分类号:
基金项目:国家自然科学基金项目(40801145,41576172)、江苏省自然科学基金项目(BK20151526)、江苏高校优势学科建设工程资助项目和高分辨率对地观测系统重大专项(32-Y30B08-9001-13)联合资助.
Comparison of water surface temperature retrieval algorithms from HJ-1B/IRS thermal infrared data over Lake Taihu, China
GONG Shaoqi1, LU Pinting1, SUN Deyong1, LI Yunmei2, WU Pengfei1
1.School of Geography and Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China;2.Jiangsu Center for Collaboration Innovation in Geographical Information Resource Development and Application, Nanjing 210023, P. R. China
Abstract:
Lake Taihu is a typical eutrophic lake in China, and water surface temperature(WST) becomes one of the most important environmental factors for algae growing in Lake Taihu. The infrared scanner on board Chinese environment and disaster monitoring satellite HJ-1B has a good capability to monitor periodically WST by remote sensing technique. Generalized single-channel method(GSCM), radiative transfer code method(RTM) and mono-window algorithm(MWA) are applied to retrieve WST from six IRS thermal infrared images passing over Lake Taihu, then the retrieved WST is compared with measured one and TERRA/MODIS temperature product. The results show that the WST retrieved by GSCM is a little higher than measured one while that by RTM and WMA is a bit lower, in which the root mean square error(RMSE) of WST calculated by three algorithms is within 1.001 K. The accuracy of WMA is at the best, RTM is at the second good and the third good for GSCM. The RMSE of MODIS temperature product is 1.507 K. The WST histograms retrieved from IRS thermal infrared data by three algorithms showed a positive deviation and a steep normal distribution, suggesting that WST thematic map can display correctly the spatial variability of temperature in Lake Taihu. This will play a guide role to carry out WST monitoring in inland waters by sensor which has only one thermal infrared channel on board satellite.
Key words:  Lake Taihu  water surface temperature  HJ-1B/IRS  thermal infrared data  algorithm
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