应用时间序列EVI的MERSI多光谱混合像元分解  

Decomposition of MERSI multispectral mixed pixels by EVI time series

作　　者：李耀辉[1] 王金鑫[1] 李颖[2,3] 

机构地区：[1]郑州大学水利与环境学院,河南郑州450001 [2]中国气象局河南省农业气象保障与应用技术重点实验室,河南郑州450003 [3]河南省气象科学研究所,河南郑州450003

出　　处：《遥感学报》2016年第3期459-467,共9页NATIONAL REMOTE SENSING BULLETIN

基　　金：中国气象局.河南省农业气象保障与应用技术重点实验室开放基金(编号:AMF201507;AMF201407);河南省基础研究计划项目(编号:142300410048)

摘　　要：针对风云3数据的特点,本文将EVI生长曲线引入多光谱混合像元的分解。首先,利用Landsat8 OLI影像,采用支持向量机的分类方法,提取研究区域的耕地信息,利用该信息对风云MERSI数据进行掩膜处理,获得研究区域的耕地影像。接着,利用MERSI时序影像,计算像元EVI值,通过SG滤波,构建农作物(端元)和混合像元的EVI生长曲线。通过实地调查,获取研究区的农作物端元,尤其对主要的农作物玉米,在空间上均匀选取了14个端元。然后,采用传统的方法,将14种玉米端元生长曲线分别与其它端元组合,进行混合像元分解。发现分解的效果差异很大,提取的玉米种植面积从191.90 km2到574.83 km2不等。为提高分解精度,借用光谱匹配(光谱夹角最小)的方法(用生长曲线代替光谱曲线)自适应选择与混合像元EVI曲线最相似的玉米端元作为组合端元,进行混合像元分解。结果得到玉米的种植面积为589.95 km2,比传统方法的最好(相对)精度提高了2%。Remote-sensing technology features and the environmental elements of surface complexity together determine mixed pixels in re- mote-sensing images. Many mature methods of hyper spectral mixed-pixel decomposition are available, but research on the multispectral de- composition of mixed pixels are rare. The purpose of this study is to decompose mixed pixels based on their multispectral imaging character- istics.Hyperspectral images with high spectral resolution may benefit from the spectral unmixing of end-members.By contrast, FY3 multis- pectral （MERSI）image shavea lower spectral resolution but a higher temporal resolution. Thus,MERSI-EVI time series is introduced in this paper to decompose mixed pixels. The basic parameters of the experiment areas are as follows： study area： Hebi City, Henan Province, China; data： 79 MERSI images ac- quired from May 1, 2013 to October 15, 2013 （89 days had no data） and a Landsat 80LI image oftbe year; purpose： extraction of 2013 corn acreage from the data images. First, the remote-sensing images were processed, and the support-vector-machine classification method was used to extract information on farmlands with the use ofa Landsat 80LI image. Then, SG-filtered MERSI time-series images were used to calculate EVI; the EVI growth curves of the mixed pixels and the crop end-numbers were then generated. The end-members were determ- ined by field investigation. Corn is the main crop in the area. A total of 14 corn end-members were evenly selected in the space.Then, using the traditional method, the 14 corn end-members were combined with other end-members for unmixing. Finally, the spectral angle matching （SAM） method was used to improve the accuracy of the decomposition and adaptively select the most similar corn end-member with mixed pixels. In this case, a growth curve was used instead of a spectral curve. The results of the traditional decomposition methods vary widely; the extracted corn acreage ranges from 191.90 km2 to 574.83 km2,whereas the generated corn

关 键 词：混合像元分解 增强型植被指数 时间序列 风云MERSI数据 作物生长曲线 

分 类 号：S513[农业科学—作物学] O211.61[理学—概率论与数理统计]