Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

被引:6
作者
Feng, Yongyu [1 ]
Chen, Bingyao [2 ]
Liu, Wei [3 ]
Xue, Xiurong [1 ]
Liu, Tongqing [2 ]
Zhu, Linye [4 ]
Xing, Huaqiao [5 ,6 ]
机构
[1] Shandong Geog Inst Land Spatial Data & Remote Sens, Jinan 250002, Peoples R China
[2] Shandong Coal Geol Bur, Prospecting Team 5, Jinan 250100, Peoples R China
[3] Shandong Yuanhong Survey Planning & Design Co Ltd, Jinan 250014, Peoples R China
[4] China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 102206, Peoples R China
[5] Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China
[6] Shandong Prov Educ Dept, Key Lab Digital Simulat Spatial Design Architectur, Jinan 250101, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 09期
关键词
winter wheat; Sentinel-2; fusion method; Google Earth Engine; Shandong Province; TIME-SERIES; CULTIVATED LAND; CROPLAND EXTENT; SCALE; CLASSIFICATION;
D O I
10.3390/app14093940
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Wheat plays an important role in China's and the world's food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources.
引用
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页数:17
相关论文
共 68 条
[1]   Large-scale crop type and crop area mapping across Brazil using synthetic aperture radar and optical imagery [J].
Ajadi, Olaniyi A. ;
Barr, Jeremiah ;
Liang, Sang-Zi ;
Ferreira, Rogerio ;
Kumpatla, Siva P. ;
Patel, Rinkal ;
Swatantran, Anu .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 97
[2]   Winter Wheat Yield Response to Plant Density as a Function of Yield Environment and Tillering Potential: A Review and Field Studies [J].
Bastos, Leonardo M. ;
Carciochi, Walter ;
Lollato, Romulo P. ;
Jaenisch, Brent R. ;
Rezende, Caio R. ;
Schwalbert, Rai ;
Prasad, P. V. ;
Zhang, Guorong ;
Fritz, Allan K. ;
Foster, Chris ;
Wright, Yancy ;
Young, Steven ;
Bradley, Pauley ;
Ciampitti, Ignacio A. .
FRONTIERS IN PLANT SCIENCE, 2020, 11
[3]   Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany [J].
Blickensdoerfer, Lukas ;
Schwieder, Marcel ;
Pflugmacher, Dirk ;
Nendel, Claas ;
Erasmi, Stefan ;
Hostert, Patrick .
REMOTE SENSING OF ENVIRONMENT, 2022, 269
[4]   From parcel to continental scale-A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations [J].
d'Andrimont, Raphael ;
Verhegghen, Astrid ;
Lemoine, Guido ;
Kempeneers, Pieter ;
Meroni, Michele ;
Velde, Marijn van der .
REMOTE SENSING OF ENVIRONMENT, 2021, 266
[5]   Joint superpixel and Transformer for high resolution remote sensing image classification [J].
Dang, Guangpu ;
Mao, Zhongan ;
Zhang, Tingyu ;
Liu, Tao ;
Wang, Tao ;
Li, Liangzhi ;
Gao, Yu ;
Tian, Runqing ;
Wang, Kun ;
Han, Ling .
SCIENTIFIC REPORTS, 2024, 14 (01)
[6]   Early-season mapping of winter wheat in China based on Landsat and Sentinel images [J].
Dong, Jie ;
Fu, Yangyang ;
Wang, Jingjing ;
Tian, Haifeng ;
Fu, Shan ;
Niu, Zheng ;
Han, Wei ;
Zheng, Yi ;
Huang, Jianxi ;
Yuan, Wenping .
EARTH SYSTEM SCIENCE DATA, 2020, 12 (04) :3081-3095
[7]   Mapping Winter Wheat in North China Using Sentinel 2A/B Data: A Method Based on Phenology-Time Weighted Dynamic Time Warping [J].
Dong, Qi ;
Chen, Xuehong ;
Chen, Jin ;
Zhang, Chishan ;
Liu, Licong ;
Cao, Xin ;
Zang, Yunze ;
Zhu, Xiufang ;
Cui, Xihong .
REMOTE SENSING, 2020, 12 (08)
[8]  
Erenstein Olaf, 2022, Wheat improvement: food security in a changing climate, P47, DOI 10.1007/978-3-030-90673-3_4
[9]   Estimating the global number and distribution of maize and wheat farms [J].
Erenstein, Olaf ;
Chamberlin, Jordan ;
Sonder, Kai .
GLOBAL FOOD SECURITY-AGRICULTURE POLICY ECONOMICS AND ENVIRONMENT, 2021, 30
[10]   Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping [J].
Griffiths, Patrick ;
Nendel, Claas ;
Hostert, Patrick .
REMOTE SENSING OF ENVIRONMENT, 2019, 220 :135-151