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

被引:1
|
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
收藏
页数:17
相关论文
共 50 条
  • [31] SENTINEL-2 MULTI-TEMPORAL DATA FOR RICE CROP CLASSIFICATION IN NEPAL
    Baidar, Tina
    Fernandez-Beltran, Ruben
    Pla, Filiberto
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 4259 - 4262
  • [32] Forest stand segmentation with multi-temporal Sentinel-2 imagery and superpixels
    Demirpolat, Caner
    Leloglu, Ugur Murat
    JOURNAL OF APPLIED REMOTE SENSING, 2024, 18 (01)
  • [33] Automating field boundary delineation with multi-temporal Sentinel-2 imagery
    Watkins, Barry
    Van Niekerk, Adriaan
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 167
  • [34] Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data
    Hoscilo, Agata
    Lewandowska, Aneta
    REMOTE SENSING, 2019, 11 (08)
  • [35] Identification of Dominant Tree Species Based on Multi-Temporal Sentinel-2 Images and SNIC Segmentation Algorithm
    Yue W.
    Li S.
    Li Z.
    Liu Q.
    Pang Y.
    Si L.
    Linye Kexue/Scientia Silvae Sinicae, 2022, 58 (09): : 60 - 69
  • [36] A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping
    Candotti, Anna
    De Giglio, Michaela
    Dubbini, Marco
    Tomelleri, Enrico
    REMOTE SENSING, 2022, 14 (23)
  • [37] New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data
    Hao, Pengyu
    Chen, Zhongxin
    Tang, Huajun
    Li, Dandan
    Li, He
    REMOTE SENSING, 2019, 11 (11)
  • [38] Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2
    Akbari, Elahe
    Boloorani, Ali Darvishi
    Samany, Najmeh Neysani
    Hamzeh, Saeid
    Soufizadeh, Saeid
    Pignatti, Stefano
    REMOTE SENSING, 2020, 12 (09)
  • [39] Mapping of Winter Wheat Using Sentinel-2 NDVI Data. A Case of Mashonaland Central Province in Zimbabwe
    Mashonganyika, Fadzisayi
    Mugiyo, Hillary
    Svotwa, Ezekia
    Kutywayo, Dumisani
    FRONTIERS IN CLIMATE, 2021, 3
  • [40] Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping
    Moharrami, Meysam
    Attarchi, Sara
    Gloaguen, Richard
    Alavipanah, Seyed Kazem
    REMOTE SENSING, 2024, 16 (09)