Sentinel-1和Sentinel-2协同反演稀疏非光合植被覆盖度  

Retrieving sparse non-photosynthetic vegetation fractional cover bySentinel-1 and Sentinel-2

作　　者：姬翠翠 骆义峡 李晓松 徐金鸿 杨雪梅 陈茂霖 JI Cuicui;LUO Yixia;LI Xiaosong;XU Jinhong;YANG Xuemei;CHEN Maolin(School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074,China;International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;Tourism school,Lanzhou University of Arts and Science,Lanzhou 730000,China)

机构地区：[1]重庆交通大学智慧城市学院,重庆400074 [2]可持续发展大数据国际研究中心,北京100094 [3]中国科学院空天信息创新研究院,北京100094 [4]兰州文理学院旅游学院,兰州730000

出　　处：《遥感学报》2023年第12期2873-2881,共9页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家重点研发计划(编号:2020YFC1511602);国家自然科学基金(编号:32060373,41801394);重庆市教育委员会科学技术研究项目(编号:KJQN202000746)。

摘　　要：精准定量反演光合植被(PV)和非光合植被(NPV)覆盖度对了解植被碳循环过程至关重要,同时,获取的非光合植被覆盖度信息也为土地沙漠化及植被转化机制研究提供重要信息。本文以甘肃省民勤县为研究区、Sentinel-1B IW GRD和Sentinel-2A为数据源,采用线性指数模型(LIM)和随机森林模型(RFM),基于控制变量法开展微波与光学遥感数据协同反演NPV覆盖度的方法研究,并参考野外获取样地真实性检验数据,将均方根误差(RMSE)和相对均方根误差(RMSE,%)作为指标评价反演结果精度。结果表明:(1)与仅采用Sentinel-2光学遥感数据相比,Sentinel-1和Sentinel-2协同反演NPV能够明显提高NPV覆盖度的估算精度;(2)由Sentinel-1和Sentinel-2获取植被指数构建的RFM在NPV覆盖度估算上较LIM精度更高,RFM和LIM估算NPV的RMSE分别为0.0149和0.0153,估算精度提高了1.4%;(3)垂直水平极化(VH)和垂直极化(VV)两种极化方式参与建立RFM可有效提高NPV覆盖度的估算精度,尤其VH极化对非光合植被信息探测更为敏感,较VV模型估算精度提高了5.1%;(4)加入表征土壤信息的比值土壤指数(RSI)有效减少了土壤对NPV覆盖度估算影响,提高了NPV覆盖度估算精度。综上,微波和光学遥感数据结合是提高NPV覆盖度估算精度的有效方法,同时,土壤作为独立重要指标参与模型计算对提高NPV覆盖度估算具有重要意义。Quantitatively estimating the fractional cover of photosynthetic vegetation,non-photosynthetic vegetation(NPV),and bare soil plays an important role in establishing carbon dynamics models.Accurately obtaining the fractional cover of NPV provides the important information for the study of land desertification and vegetation transformation mechanisms.Although some progress has been made in obtaining NPV fractional cover(fNPV)by optical remote sensing in previous studies,many interfering factors and difficulties are still present.We will attempt to combine microwave and optical remote sensing information to obtain NPV fractional cover for further improving the accuracy of the fractional cover estimation of NPV.In this study,we used Minqin County in Gansu Province as the research area,and we employed Sentinel-1B IW GRD and Sentinel-2A as data sources.The experiments employed the control variable method with the linear index model and the random forest regression(RFR)model to conduct the fractional cover estimation of NPV by using microwave and optical remote sensing data.Then,the estimated endmember fractions were validated with reference to fraction measurements.In addition,the Root Mean Square Error(RMSE)and Relative Root Mean Square Error(RMSE%)were employed as indicators to evaluate the inversion accuracy.Results show that(1)using cooperative Sentinel-1 and Sentinel-2 remote sensing data to estimate the fractional cover of NPV can effectively improve the estimated accuracy compared with using Sentinel-2 data alone.(2)The RFR model is an effective method for the fractional cover estimation of sparse NPV,and its estimation accuracy is higher than that of the linear index model.The validation RMSE of the random forest model and the estimated fNPV of the linear index model are 0.0149 and 0.0153,respectively.Obviously,the accuracy of fNPV estimation increases by 1.4%when using the RFR model instead of the linear index model.(3)The VH and VV polarization bands of Sentinel-1 data can effectively detect the characteristi

关 键 词：非光合植被 Sentinel-1 Sentinel-2 线性指数模型 随机森林回归模型 VV和VH极化 甘肃省民勤县 

分 类 号：TP701[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]