首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于神经网络算法的Sentinel-1和Sentinel-2遥感数据联合反演土壤湿度研究
引用本文:吴善玉,鲍艳松,李叶飞,吴莹.基于神经网络算法的Sentinel-1和Sentinel-2遥感数据联合反演土壤湿度研究[J].大气科学学报,2021,44(4):636-644.
作者姓名:吴善玉  鲍艳松  李叶飞  吴莹
作者单位:南京信息工程大学 气象灾害预报预警与评估协同创新中心/中国气象局气溶胶与云降水重点开放实验室/大气物理学院, 江苏 南京 210044;上海卫星工程研究所, 上海 200240
基金项目:中国航天科技集团公司第八研究院产学研合作基金资助项目;国家自然科学基金国际(地区)合作与交流项目(61661136005);国家重点研发计划项目(2017YFC1501704;2016YFA0600703)
摘    要:以西班牙萨拉曼卡地区为研究区域,联合Sentinel-1后向散射系数和入射角信息、Sentinel-2光学数据提取的植被指数以及地面实测数据,构建了BP神经网络土壤湿度反演模型,并将该模型应用于试验区土壤湿度反演。结果表明:1)基于Sentinel-1卫星VV和VH极化雷达后向散射系数、雷达入射角和Sentinel-2植被指数数据构建的BP神经网络土壤湿度反演模型,能够实现对该地区土壤湿度高精度反演;2)在光学与微波数据联合反演植被覆盖区土壤湿度中,Sentinel-2的NDVI、NDWI1和NDWI2指数都可以用于削弱植被对土壤湿度反演的影响,但基于SWRI1波段的NDWI1能够获得更高精度的土壤湿度反演结果(RMSE为0.049 cm~3/cm~3,ubRMSE为0.048 cm~3/cm~3,Bias为0.008 cm~3/cm~3,r为0.681);3)相比于Sentinel-1 VH极化模式,Sentinel-1 VV极化模式在土壤湿度中表现出更大优势,说明Sentinel-1 VV极化模式更适用于土壤湿度反演。

关 键 词:土壤水分  Sentinel-1  Sentinel-2  BP神经网络
收稿时间:2019/4/19 0:00:00
修稿时间:2019/5/9 0:00:00

Joint retrieval of soil moisture from Sentinel-1 and Sentinel-2 remote sensing data based on neural network algorithm
WU Shanyu,BAO Yansong,LI Yefei,WU Ying.Joint retrieval of soil moisture from Sentinel-1 and Sentinel-2 remote sensing data based on neural network algorithm[J].大气科学学报,2021,44(4):636-644.
Authors:WU Shanyu  BAO Yansong  LI Yefei  WU Ying
Institution:Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration/School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China;Shanghai Institute of Satellite Engineering, Shanghai 200240, China
Abstract:Soil moisture is an important parameter of ecological environment and an important part of water cycle.The retrieval of surface soil moisture based on multi-source remote sensing data is a hotspot and trend in recent years.As a new generation of Sentinel satellites, the Sentinel-1 SAR data combined with the Sentinel-2 optical data have broad application prospects.Taking Salamanca, Spain as the research area, a BP neural network soil moisture retrieval model is constructed by combining the Sentinel-1 backscatter coefficient and incidence angle information, the vegetation index extracted from the Sentinel-2 optical data, and the ground observation data, and the model is applied to retrieve the soil moisture in the area.Finally, the model retrieval results are tested and evaluated.Results show that:(1) Based on the Sentinel-1 satellite VV and VH polarization radar backscatter coefficients and radar incidence angles and the Sentinel-2 vegetation index data, the BP neural network soil moisture retrieval model can realize high-precision retrieval of soil moisture in Salamanca area;(2) In the joint retrieval of soil moisture of optical and microwave data in vegetation coveragearea, the NDVI, NDWI1 and NDWI2 indices from the Sentinel-2 can be used to weaken the influence of vegetation on soil moisture retrieval, but the NDWI1 based on SWRI1 band can obtain more accurate soil moisture retrieval results (RMSE=0.049 cm3/cm3, ubRMSE=0.048 cm3/cm3, Bias=0.008 cm3/cm3, r=0.681);(3) Comparing with the Sentinel-1 VH polarization model, the Sentinel-1 VV polarization model shows greater advantages in soil moisture, indicating that the Sentinel-1 VV polarization model is more suitable for soil moisture retrieval.
Keywords:soil moisture  Sentinel-1  Sentinel-2  BP neural network
本文献已被 CNKI 等数据库收录!
点击此处可从《大气科学学报》浏览原始摘要信息
点击此处可从《大气科学学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号