千岛湖水体表层温度遥感估算方法对比  

Comparison of lake surface water temperature retrieval algorithms:A case study of Lake Qiandaohu

作　　者：张琳敏 梅格致 刘明亮 李渊 施坤[3,4] 朱梦圆 李慧赟[3] 郭宇龙[5] 王嘉诚 ZHANG Linmin;MEI Gezhi;LIU Mingliang;LI Yuan;SHI Kun;ZHU Mengyuan;LI Huiyun;GUO Yulong;WANG Jiacheng(School of Tourism and Urban&Rural Planning,Zhejiang Gongshang University,Hangzhou 310018,China;Hangzhou Institute of Ecological and Environmental Sciences,Hangzhou 310014,China;State Key Laboratory of Lake Science and Environment,Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences,Nanjing 210008,China;University of Chinese Academy of Sciences,Beijing 100049,China;Henan Agricultural University,College of Resources and Environmental Sciences,Zhengzhou 450002,China)

机构地区：[1]浙江工商大学旅游与城乡规划学院,杭州310018 [2]杭州市生态环境科学研究院,杭州310014 [3]中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室,南京210008 [4]中国科学院大学,北京100049 [5]河南农业大学资源与环境学院,郑州450002

出　　处：《遥感学报》2024年第8期2113-2130,共18页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:U22A20561,41922005,42071333);中国科学院科研仪器研制项目(编号:YJKYYQ20200071);中国科学院南京地理与湖泊研究所青年科学家小组项目(编号:E1SL002);杭州市社会发展科研专项(编号:20140533B11);浙江省大学生科技创新活动计划(编号:JS2021847270)。

摘　　要：湖泊水体表层温度是气候和环境变化的重要指示因子,遥感技术是水体表层温度监测的重要手段。温度反演算法在不同湖库水体的适用性各异,针对清洁型深水湖库的水体表层温度反演算法适用性仍有待进一步研究。本研究以千岛湖为研究区,利用Landsat 8卫星数据,对比了基于辐射传输方程的算法(RTE)、单窗算法(MWA)、普适性单通道算法(GSCA)、实用单通道算法(PSCA)、劈窗算法(SWA_D和SWA_G)和Landsat 8Collection 2 Level-2(C2L2)温度产品的精度,探究了各算法中相关参数的适用性和敏感性,刻画了千岛湖2013年—2021年水体表层温度时空分布特征。研究结果表明:(1)针对Landsat 8数据的第10和11波段,千岛湖最适宜的水体比辐射率分别为0.9926和0.9877;(2)整体上,劈窗算法的精度优于单通道算法,Landsat温度产品的估算精度适中。其中,劈窗算法SWA_G精度最优,平均相对误差(MAPE)为7.61%,均方根误差(RMSE)为2.0℃;(3)千岛湖水体表层温度具有显著的时空分异特征。季节上,千岛湖水体表层温度冬季最低(14.2±0.6℃),夏季最高(31.0±0.5℃)。空间上,西北库区(23.0±0.3℃)和西南库区(22.8±0.2℃)水体表层温度最高,东北库区(22.2±0.3℃)水体表层温度最低。本研究验证了不同温度反演算法在清洁型深水湖库的适用性,为清洁型深水湖库水体表层温度反演提供了经验借鉴。Lake surface water temperature is an important indicator of water quality,lake physical environment,and climate change.Monitoring lake surface water temperature and understanding its spatiotemporal variations are critical for local governments to protect lake ecosystems.Remote sensing is an effective method to monitor lake surface water temperature,and many algorithms have been developed and applied to retrieve lake surface water temperature.However,the suitability of these algorithms varies in different lakes.Especially,the suitability of these algorithms in deep,oligotrophic-to-mesotrophic lakes still needs to be discussed.Thus,taking Lake Qiandaohu,China as the study area,we attempt to validate the performance of various land surface temperature retrieval algorithms,analyze the sensitivity of the parameters in each algorithm,and map the spatiotemporal distribution of lake surface water temperature.In this study,six land surface temperature retrieval algorithms(i.e.,radiative transfer equation algorithm,monowindow algorithm,generalized single-channel algorithm,practical single-channel algorithm,and two split-window algorithms)were selected to retrieve lake surface water temperature using Landsat 8 data in Lake Qiandaohu.The performance of these algorithms and the Landsat 8 Collection 2Level-2(C2L2)temperature product were validated with in-situ buoy data.By applying the best performing algorithm to 37 cloud-free Landsat 8 data collected from 2013 to 2021,the spatial and temporal distribution of lake surface water temperature in Lake Qiandaohu were mapped.Furthermore,the sensitivity of the relevant parameters(i.e.,water surface emissivity,effective mean atmospheric temperature,atmospheric water vapor content,upwelling radiance,downwelling radiance,and atmospheric transmittance)in each algorithm were explored.The results showed the following:(1)For bands 10 and 11 of Landsat 8 data,the most suitable water surface emissivity in Lake Qiandaohu is 0.9926 and 0.9877,respectively.(2)The accuracy of the split-window al

关 键 词：遥感 水温 LANDSAT 千岛湖 水体比辐射率 敏感性分析 

分 类 号：P2[天文地球—测绘科学与技术]