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Remote Sensing Technology and Application  2021, Vol. 36 Issue (2): 332-341    DOI: 10.11873/j.issn.1004-0323.2021.2.0332
    
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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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     
Received:  24 July 2020      Published:  24 May 2021
ZTFLH:  S127  
Corresponding Authors:  Dailiang Peng     E-mail:  644185719@qq.com;pengdl@radi.ac.cn
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Articles by authors
Jie Yin
Leilei Zhou
Liwei Li
Yaqiong Zhang
Wenjiang Huang
Helin Zhang
Yan Wang
Shijun Zheng
Haisheng Fan
Chan Ji
Junjie Chen
Dailiang Peng

Cite this article: 

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.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2021.2.0332     OR     http://www.rsta.ac.cn/EN/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
Table 1  Introduction of band information used in Sentinel-2A image
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
Table 2  Comparison of multi-remote sensing data
Fig.1  Study area classification point and verification point distribution
卫星PA/%UA/%总体精度/%Kappa系数
OHS-2A94.4092.8682.080.76
Sentinel-2A98.3990.1185.570.81
Table 3  Accuracy verification of identification results
Fig.2  Recognition results in Xiong'an
Fig.3  The relative range of Sentinel-2A and MODIS growth change rate in Xiong'an County
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
Table 4  Percentage of wheat area in the growth grade of the green return period and heading period in Xiong’an County
Fig.4  Comparison of wheat growth monitoring in Xiong’an
Fig.5  The wheat growth histogram of Sentinel-2A and MODIS
1 Gu Yushuang. Study on Winter Wheat Classification and Monitoring basaed on Time-series Remote Sensing Data[D]. Fu xin: Liaoning Technical University, 2017.
1 谷玉双. 基于时间序列遥感数据的冬小麦分类和监测研究[D]. 阜新:辽宁工程技术大学, 2017.
2 Wang Siheng. Application Status and Prospect of Hyperspectral Remote Sensing Technology in Agriculture[J]. China Agriculture Information, 2013(13): 203-204.
2 王思恒. 高光谱遥感技术在农业中的应用现状及展望[J]. 中国农业信息, 2013(13): 203-204.
3 Shi Ninzhuo. Research on Wheat Area Extraction based on MODIS-NDVI Time-series:A Case Study in the Haihe Basin[D]. Xi’an: Xi’an University of Science and Technolog, 2015.石宁卓. 基于MODIS-NDVI时间序列小麦面积提取方法研究—以海河流域为例[D]. 西安:西安科技大学, 2015.
4 Yu Min, Wei Lifei, Yin Feng, et al. Hyperspectral Remote Sensing Image Crop Fine Classification based on Conditional Random Field[J]. China Agricultural Informatics, 2018, 30(3): 74-82.
4 余铭, 魏立飞, 尹峰, 等. 基于条件随机场的高光谱遥感影像农作物精细分类[J]. 中国农业信息, 2018, 30(3): 74-82.
5 Sun Huasheng, Xu Aigong, Lin Hui, et al. Optimization of Frequency Domain Denoising Algorithms for Time-series Vegetaion Index[J]. Remote Sensing Information, 2013, 28(1): 24-28.
5 孙华生, 徐爱功, 林卉, 等. 时间序列植被指数频域滤波去噪算法的优化研究[J]. 遥感信息, 2013, 28(1): 24-28.
6 Ding Xiao. Study on Distribution of Crop’s Structure in Heilongjiang[D]. Harbin: Northeast Gricultural University, 2014.
6 丁潇. 黑龙江省农作物种植结构布局研究[D]. 哈尔滨:东北农业大学, 2014.
7 Li Weiguo, Wang Jihua, Zhao Chunjiang, et al. Preliminary Study on Remote Sensing Monitoring Winter Wheat Growth at Heading Stage[J]. Jiangsu of Agriculture Science, 2007(5): 499-500.李卫国, 王纪华, 赵春江, 等. 冬小麦抽穗期长势遥感监测的初步研究[J]. 江苏农业学报, 2007(5): 499-500.
8 Zhang Yanhong, Li Yanhua. Spring Management Technology of Winter Wheat in Xiong'an New Area[J]. Modern Rural Science and Technology, 2019(6): 23.
8 张艳红, 李艳花. 雄安新区冬小麦春季管理技术[J].现代农村科技, 2019(6):23.
9 Kontoes C, Wilkinson G G, Burril A, et al. An Experimental System for the Integration of GIS Data in Knowledge based Image Analysis for Remote Sensing of Agriculture[J]. International Journal of Geographical Information Systems, 1993,7(3): 247-262. doi: .
doi: 10.1080/02693799408902016
10 Liu Xinjie, Wei Yunxia, Jiao Quanjun, et al. Growth Monitoring and Yield Prediction of Winter Wheat based on Time-series Quantitative Remote Sensing Data[J].Remote Sensing Technology and Application,2019,34(4): 756-765.
10 刘新杰, 魏云霞, 焦全军, 等. 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.
11 Bhumika N V, Hitesh A S, Manik H K. Winter Wheat Growth Assessment Using Temporal Normalized Phenology Index (TNPI) in Bhuj Taluka, Gujarat State, India[J]. Remote Sensing Applications:Society and Enviroment, 2020, 20:100422. doi: .
doi: 10.1016/j.rsase.2020.100422
12 Sun Li,Wang Weidan,Chen Yuanyuan, et al. Analysis of Winter Wheat Growth of United States with Remote Sensing Data in 2019[J]. Anhui Agricural Science, 2020,48(1): 241-244.
12 孙丽, 王蔚丹, 陈媛媛, 等. 2019年美国冬小麦长势遥感监测分析[J]. 安徽农业科学, 2020, 48(1): 241-244.
13 Wu Jianjun, Yang Qinye. Crop Monitoring and Yield Estimation Using Synthetic Methods in Arid Land[J]. Geographical Research, 2002,21(5): 593-598.
13 武建军, 杨勤业. 干旱区农作物长势综合监测[J]. 地理研究,2002,21(5):593-598.
14 Luo Ge, Wei Zheng. China Aerospace Remote Sensing and Spacial Information Industry Development[J]. Spacecraft Recovery and Remote Sensing, 2018,39(4): 10-17.
14 罗格, 卫征. 航天遥感与中国空间信息产业发展[J].航天返回与遥感, 2018, 39(4): 10-17.
15 Chen Xu, Hao Zhenghuan. Sentinel-2A Data Products’ Characteristics and the Potential Applications[J]. Science & Technology Vision, 2018(16): 48-50.
15 陈旭, 郝震寰. 哨兵卫星Sentinel-2A数据特性及应用潜力分析[J]. 科技视界, 2018(16): 48-50.
16 Bi Kaiyi, Niu Zheng, Huang Ni, et al. Identifying Vegetation with Decision Tree Model based on Object-oriented Method using Multi-temporal Sentinel-2A Images[J]. Geography and Geo-Information.Science, 2017,33(5):16-20,27,127.
16 毕恺艺, 牛铮, 黄妮, 等. 基于Sentinel-2A时序数据和面向对象决策树方法的植被识别[J]. 地理与地理信息科学, 2017, 33(5):16-20,27,127.
17 Hua Jinxi. Monitoring of Saline-alkali Soil in Songnen Plain based on MODIS Time-series Data[D]. Harbin: Harbin Normal University, 2017.
17 花锦溪. 基于MODIS时间序列的松嫩平原盐碱地动态变化研究[D]. 哈尔滨:哈尔滨师范大学, 2017.
18 Shi Feifei, Gao Xiaohong, Yang Linyu, et al. Research on Typical Crop Classification based on HJ-1A Hyperspectral Data in the Huangshui River Basin[J]. Remote Sensing Trchnology and Application, 2017,32(2): 206-217.
18 史飞飞, 高小红, 杨灵玉, 等. 基于HJ-1A高光谱遥感数据的湟水流域典型农作物分类研究[J]. 遥感技术与应用, 2017, 32(2): 206-217.
19 Ge Shanyun. Feature Extraction Method based on the Combination of MNF PCA and ICA for Hyperspectral Data[J]. Urban Geotechnical Investigation and Surveying, 2013(2): 103-106.
19 葛山运. 基于MNF、PCA与ICA结合的高光谱数据特征提取方法[J]. 城市勘测, 2013(2): 103-106.
20 Du Peng, Zhao Huijie. Noise Robust ICA Feature Extraction Algorithm for Hyperspectral Image[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005,31(10):56-60.
20 杜鹏, 赵慧洁. 基于抗噪声ICA的高光谱数据特征提取方法[J]. 北京航空航天大学学报, 2005,31(10):56-60.
21 Yan Jining, Zhou Kefa, Wang Jinlin, et al. Extraction of Hyper-Spectral Remote Sensing Alteration Information based on SAM and SVM[J]. Computer Engineering and Applications, 2013,49(19): 141-146.
21 阎继宁, 周可法, 王金林, 等. 基于SAM与SVM的高光谱遥感蚀变信息提取[J]. 计算机工程与应用, 2013, 49(19): 141-146.
22 Fu Wenjie, Hong Jinyi, Zhu Guchang. The Extraction of Mineralized and Altered Rock Information from Remote Sensing Image based on SVM[J]. Remote Sensing for Land and Resources, 2006,18(2):16-19,82.
22 傅文杰, 洪金益, 朱谷昌. 基于SVM遥感矿化蚀变信息提取研究[J]. 国土资源遥感, 2006,18(2):16-19,82.
23 Yang Guopeng, Yu Xuchu, Liu Wei, et al. Research of Hyperspectral Image Classification based on Support Vector Machine[J]. Computer Engineering and Design, 2008(8): 2029-2031,2034.杨国鹏, 余旭初, 刘伟, 等. 基于支持向量机的高光谱影像分类研究[J]. 计算机工程与设计, 2008(8): 2029-2031,2034.
24 Wei Li. Research on Land Use Change of Chanba Ecological District based on RS GIS Technologies[D]. Xi’an: Xi’an University of Science and Technolog, 2013.魏力. 基于3S的浐灞生态区土地利用动态监测研究[D]. 西安:西安科技大学, 2013.
25 Du Xiaoyan. Main Management Measures of Winter Wheat Returning to Green[J]. Modern Rural Science and Technology, 2019(11): 15.
25 杜晓晔. 冬小麦返青期主要管理措施[J]. 现代农村科技, 2019(11): 15.
26 Liu Qiang, Li Yuehua, Feng Lihui, et al. Classification of Spring Strength of Main Winter Wheat Varieties in Central and Southern Hebei Province[J]. Journal of Hebei Agricultural Sciences, 2015, 19(2): 12-14,32.
26 刘强, 李月华, 冯立辉, 等. 冀中南主栽冬小麦品种春性强弱的分类研究[J]. 河北农业科学, 2015, 19(2): 12-14,32.
27 Wu Suxia, Mao Renzhao, Li Hongjun, et al. .Review of Crop Condition Monitoring Using Remote Sensing in China[J]. Chinese Agricultural Science Bulletin, 2005,21(3): 319-322,345.
27 吴素霞, 毛任钊, 李红军, 等. 中国农作物长势遥感监测研究综述[J]. 中国农学通报, 2005,21(3): 319-322,345.
28 Wu Bingfang, Zhang Feng, Liu Chenglin, et al. An Integrated Method for Crop Condition Monitoring[J]. Journal of Remote Sensing, 2004,8(6):498-514.
28 吴炳方, 张峰, 刘成林, 等. 农作物长势综合遥感监测方法[J]. 遥感学报, 2004,8(6):498-514.
29 Wang Yan, Peng Dailiang, Yu Le, et al. Monitoring Crop Growth during the Period of the Rapid Spread of COVID-19 in China by Remote Sensing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020, 13:6195-6205. doi: .
doi: 10.1109/JSTARS.2020.3029434
30 Shi Pingting. The Three Counties of Xiong'an are Busy Preparing for Sowing in Spring. [EB/OL]..
30 石萍婷,战“疫”不误农时,雄安三县春耕备播忙. [EB/OL]., 2020-02-25.
31 Wang Linyan. High Yield Planting Management Technology of Winter Wheat[J].Hebei Agriculture,2017(12):6-7.
31 王玲艳.冬小麦高产种植管理技术[J].河北农业,2017(12): 6-7.
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