摘要
采用GF-1号、ZY-3号以及Landsat-8卫星数据,利用回归模型和像元二分模型,通过对建立的四类植被指数NDVI、MSAVI、MVI和RVI,结合野外调查数据,提出NSD的概念来评价模型及方法的精度。实测数据与各类遥感影像的4种植被指数间均存在着显著的相关关系;通过NSD精度验证,说明空间分辨率较低的遥感数据,在一定程度上提高了反演精度;在4类植被指数中,RVI与MSAVI对于三类数据反演精度较高,且MSAVI对于较低分辨率遥感数据可能具有更好的消除土壤背景影响的作用。
By using GF-1,ZY-3,and Landsat-8 satellite images,this paper proposed the concept of NSD to evaluate the precision of models and methods with the establishment four types vegetation index( NDVI,MSAVI,MVI and RVI) in the combination with field investigation data. These results showed that there exists an obvious correlation between four types vegetation index of each image.Through validation by NSD,it indicates that the precision of the low spatial resolution of remote sensing data have been improved to some certain extent. In addition,the retrieving accuracy of RVI and MSAVI is better than that of other vegetation index,and the MSAVI may have better effect on the lower resolution remote sensing data by eliminating the influence of soil background.
作者
张瑜伟
苗小莉
张泳
ZHANG Yuwei;MIAO Xiaoli;ZHANG Yong(Jiangsu Geologic Surveying and Mapping Institute,Nanjing 211102,China)
出处
《测绘与空间地理信息》
2020年第3期131-134,137,共5页
Geomatics & Spatial Information Technology
关键词
多源遥感数据
植被覆盖度
植被指数
回归模型
像元二分模型
归一化标准差
multi-source remote sensing data
vegetation coverage
vegetation index
regression model
dimidiate pixel model
normalized standard deviation
