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遥感技术与应用  2021, Vol. 36 Issue (2): 332-341    DOI: 10.11873/j.issn.1004-0323.2021.2.0332
农业遥感专栏     
多源遥感数据小麦识别及长势监测比较研究
尹捷1,2(),周雷雷1,2,李利伟2,张雅琼3,黄文江2,张赫林4,王岩2,5,郑诗军2,5,范海生6,纪婵6,陈俊杰1,彭代亮2()
1.河南理工大学测绘与国土信息工程学院,河南 焦作 454003
2.中国科学院空天信息创新研究院数字地球科学重点实验室,北京 100094
3.生态环境部卫星环境应用中心,北京 100094
4.北京市陆表遥感数据产品工程技术研究中心,北京师范大学地理科学学部,北京 100875
5.中国科学院大学,北京 100049
6.珠海欧比特宇航科技股份有限公司,广东 珠海 519000
A Comparative Study on Wheat Identification and Growth Monitoring based on Multi-source Remote Sensing Data
Jie Yin1,2(),Leilei Zhou1,2,Liwei Li2,Yaqiong Zhang3,Wenjiang Huang2,Helin Zhang4,Yan Wang2,5,Shijun Zheng2,5,Haisheng Fan6,Chan Ji6,Junjie Chen1,Dailiang Peng2()
1.School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454003,China
2.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
3.Center for Satellite Application on Ecology and Environment,Ministry of Ecology and Environment,Beijing 100094,China
4.Beijing Engineering Research Center for Global Land Remote Sensing Products,Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China
5.University of Chinese Academy of Sciences,Beijing 100049,China
6.Zhuhai Orbita Aerospace Science & Technology Co. LTD,Zhuhai 519000,China
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摘要:

小麦是我国主要的农作物之一,对于我国的经济发展有着重要意义。遥感技术经过不断发展,已成为提取小麦及长势研究的重要手段。利用高光谱珠海一号OHS-2A卫星、多光谱Sentinel-2A卫星以及MODIS等多源遥感数据,以雄安为研究区,采用支持向量机的方法进行小麦提取,结合野外实测数据利用混淆矩阵进行精度评价分析;分别对比小麦的两个重要生育期返青期和抽穗期,将小麦长势分为3个等级(按长势较好、长势持平、长势较差)进行长势监测比较。研究表明:环境条件相同下,OHS-2A的总体精度为82.08%,Kappa系数为0.76;Sentinel-2A的总体精度为85.57%,Kappa系数为0.81,相比之下Sentinel-2A对于小麦的识别效果最佳。在进行长势监测中,对比小麦各长势情况及长势变化相对幅度,Sentinel-2A数据比MODIS数据对于雄安小麦的长势监测及研究分析更有效。采用多源遥感数据分析雄安小麦识别及长势监测情况,有利于小麦种植管理,这对于推动绿色雄安有着重大意义。

关键词: 珠海一号长势监测小麦识别雄安    
Abstract:

Wheat is one of the main crops in China, which is of great significance to the economic development of China.With the continuous development of remote sensing technology, remote sensing technology has become an important means to extract wheat and growth monitoring. The identification of wheat is the premise of its planting area management, and the growth research is an important indicator of its growth evaluation and yield control. In this paper, the multi-source remote sensing data such as the hyperspectral zhuhai No.1 OHS-2A satellite, the multi-spectral Sentinel-2A satellite and MODIS were used to extract wheat by using Support Vector Machine(SVM) in Xiong'an as the research area. The accuracy of wheat was evaluated and analyzed by using the confusion matrix based on the field measurement data. Comparing the two important growth stages of wheat: the return green period and the heading period, wheat growth was divided into three grades (good growth, similar growth, worse growth) for growth monitoring and comparing. The results showed that under the same environmental conditions, the Overall accuracy of OHS-2A was 82.08%, and the Kappa coefficient was 0.76;The Overall accuracy of Sentinel-2A was 85.57% ,and the Kappa coefficient was 0.81, By contrast, Sentinel-2A is the best at identification wheat. In the process of growth monitoring, the Sentinel-2A satellite is more effective than MODIS in monitoring and analyzing the growth of Xiong'an wheat by comparing the growth conditions and the relative amplitude of the change of wheat growth.This study analyzed the status of wheat identification and growth monitoring in Xiong'an from remote sensing data of different resolutions, which is conducive to wheat planting management and the formulation of agricultural policies, which is of great significance for promoting the economic development of green Xiong’an and the city.

Key words: OHS-2A    Growth monitoring    Wheat identification    Xiong'an
收稿日期: 2020-07-24 出版日期: 2021-05-24
ZTFLH:  S127  
基金资助: 国家自然科学重点基金项目“基于深度学习的小麦植被参量高光谱遥感反演研究”(42030111);珠海一号卫星大数据云服务平台与应用示范(遥感大数据服务团队)(ZH0111?0405?170027?P?WC)
通讯作者: 彭代亮     E-mail: 644185719@qq.com;pengdl@radi.ac.cn
作者简介: 尹捷(1996-),男,山西大同人,硕士研究生,主要从事作物长势监测方面的研究。E?mail: 644185719@qq.com
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尹捷
周雷雷
李利伟
张雅琼
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张赫林
王岩
郑诗军
范海生
纪婵
陈俊杰
彭代亮

引用本文:

尹捷,周雷雷,李利伟,张雅琼,黄文江,张赫林,王岩,郑诗军,范海生,纪婵,陈俊杰,彭代亮. 多源遥感数据小麦识别及长势监测比较研究[J]. 遥感技术与应用, 2021, 36(2): 332-341.

Jie Yin,Leilei Zhou,Liwei Li,Yaqiong Zhang,Wenjiang Huang,Helin Zhang,Yan Wang,Shijun Zheng,Haisheng Fan,Chan Ji,Junjie Chen,Dailiang Peng. A Comparative Study on Wheat Identification and Growth Monitoring based on Multi-source Remote Sensing Data. Remote Sensing Technology and Application, 2021, 36(2): 332-341.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.2.0332        http://www.rsta.ac.cn/CN/Y2021/V36/I2/332

光谱波段号中心波长/nm波宽/nm空间分辨率/m
band24906510
band35603510
band46653010
band884211510
表1  Sentinel-2A影像所用波段信息简介
数据源空间分辨率/m幅宽/km波段数重访周期/d
OHS-2A10150325
Sentinel-2A10290410
206
603
MOD13Q12502 3301216
表2  多源遥感数据比较
图1  研究区分类点与验证点分布图
卫星PA/%UA/%总体精度/%Kappa系数
OHS-2A94.4092.8682.080.76
Sentinel-2A98.3990.1185.570.81
表3  识别结果精度验证
图2  雄安小麦识别结果
图3  Sentinel-2A与MODIS雄安各县长势变化相对幅度
时期长势等级Sentinel-2AMOD13Q1
安新县容城县雄县安新县容城县雄县

长势较好37.8620.2018.6391.0286.7041.42
长势持平29.9737.7731.608.4312.8658.24
长势较差32.1742.0349.770.550.440.34

长势较好16.3014.3515.8234.6028.3918.76
长势持平51.4151.5850.0847.1244.8048.75
长势较差32.2934.0734.1018.2826.8132.49
表4  雄安各县在返青期、抽穗期长势等级所占小麦面积百分比 (%)
图4  雄安小麦长势监测比较
图5  Sentinel-2A、MODIS小麦长势直方图
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[1] 刘新杰,魏云霞,焦全军,孙奇,刘良云. 基于时序定量遥感的冬小麦长势监测与估产研究[J]. 遥感技术与应用, 2019, 34(4): 756-765.