高分六号遥感影像植被特征及其在冬小麦苗期LAI反演中的应用  

Vegetation characteristics of GF-6 remote sensing image and application on LAI retrieval of winter wheat at seedling stage

作　　者：张矞勋 齐拓野[4] 孙源 璩向宁[1,2] 曹媛 吴梦瑶[1,2] 刘春虹 王磊 ZHANG Yu-Xun;QI Tuo-Ye;SUN Yuan;QU Xiang-Ning;CAO Yuan;WU Meng-Yao;LIU Chun-Hong;WANG Lei(Breeding Base for State Key Laboratory of Land Degradation and Ecosystem Restoration in Northwest China,Ningxia University,Yinchuan 750021,Ningxia,China;Key Laboratory for Restoration and Reconstruction of Degenerated Ecosystem in Northwest China under Ministry of Education,Ningxia University,Yinchuan 750021,Ningxia,China;Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Beijing,100101,China;School of Ecology and Environment,Ningxia University,Yinchuan 750021,Ningxia,China)

机构地区：[1]宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏银川750021 [2]宁夏大学西北退化生态系统恢复与重建教育部重点实验室,宁夏银川750021 [3]中国科学院遥感与数字地球研究所,北京100101 [4]宁夏大学生态环境学院,宁夏银川750021

出　　处：《作物学报》2021年第12期2532-2540,共9页Acta Agronomica Sinica

基　　金：“西部之光”人才培养计划“西部青年学者”A类项目(XAB2017AW06);国家民用空间基础设施陆地观测卫星共性应用支撑平台(Y930280A2F);国家自然科学基金项目(31760707);宁夏回族自治区西部一流学科建设项目(NXYLXK2017B06)资助。

摘　　要：高分六号(GF-6)遥感卫星是中国首颗精准农业观测的高分卫星,与高分一号(GF-1)组网运行,除具有与GF-1 WFV传感器相同的波段外,国内首次增加了能够有效反映作物特有光谱特性的红边波段。为评价高分六号卫星影像的农作物监测能力,以苗期冬小麦为研究对象,结合地面同步观测的冠层光谱和LAI实测数据,分析高分六号卫星影像的波段数量、波段光谱范围及新增的红边植被波段特征;通过提取GF-6 WFV影像中的反射率数据构建植被指数,借助人工神经网络,对比GF-6 WFV传感器不同植被指数组合构建反演模型的精度,以此探究GF-6 WFV红边波段在冬小麦苗期叶面积指数反演的应用能力。结果表明,高分六号遥感影像能较好反映真实的植被特征;同时在对冬小麦LAI反演时增加GF-6 WFV传感器的两个红边波段及红边植被指数数据,对苗期冬小麦LAI反演模型精度有较大的提高,R2分别调高12.48%,RMSE降低14.75%。As Chinese first high-resolution satellite for precision agricultural observations,the GF-6 remote sensing satellite oper-ates in a network with GF-1.In addition to having the same waveband as the GF-1 WFV sensor,red-edge band is added to the Chinese satellite firstly,which can effectively reflect the unique spectral characteristics of the crop.In order to evaluate the moni-toring capabilities of the GF-6 satellite imagery for crops,the seedling stage of winter wheat was selected as the research object.Combined with the ground synchronous observation canopy spectrum and the LAI measured data,we analyzed the quantity of bands,the band spectrum and the features of the added red edge vegetation band of the GF-6 satellite image.Furthermore,we constructed vegetation indices by extracting reflectance data from GF-6 remote sensing images and made comparison between the inversion accuracy of the model established by the combination of different wavebands of GF-6 WFV sensor with the help of artificial neural network.Finally,the application ability of GF-6 WFV red edge band in inversing LAI of winter wheat at seeding stage was explored.The results showed that the GF-6 remote sensing image reflected the characteristics of vegetation more realistically.When inverting the winter wheat LAI,the two red-edge bands and the red-edge vegetation index data of the GF-6 WFV sensor were added,which greatly improved the accuracy of the winter wheat LAI inversion model at seedling stage,with the in-creased R2 of 12.48%and the decreased RMSE of 14.75%.

关 键 词：高分六号 红边参数 叶面积指数反演 冬小麦 BP神经网络 

分 类 号：S512.11[农业科学—作物学] S127