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
收藏
页数:17
相关论文
共 68 条
[51]   Does Participation in Agricultural Value Chain Activities Influence Smallholder Fruit Grower Production Performance? A Cross-Sectional Study of Apple Farmers in Shandong, China [J].
Wang, Xiaolei ;
Sarkar, Apurbo ;
Wang, Hongyu ;
Zhang, Fuhong .
HORTICULTURAE, 2021, 7 (06)
[52]   Managing Land Carrying Capacity: Key to Achieving Sustainable Production Systems for Food Security [J].
Wang, Xiukang .
LAND, 2022, 11 (04)
[53]   Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data [J].
Xiao, XM ;
Boles, S ;
Liu, JY ;
Zhuang, DF ;
Liu, ML .
REMOTE SENSING OF ENVIRONMENT, 2002, 82 (2-3) :335-348
[54]   Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China [J].
Xie, Yi ;
Huang, Jianxi .
REMOTE SENSING, 2021, 13 (21)
[55]   A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery [J].
Xing, Huaqiao ;
Chen, Bingyao ;
Lu, Miao .
REMOTE SENSING, 2022, 14 (24)
[56]   Mapping irrigated, rainfed and paddy croplands from time-series Sentinel-2 images by integrating pixel-based classification and image segmentation on Google Earth Engine [J].
Xing, Huaqiao ;
Chen, Bingyao ;
Feng, Yongyu ;
Ni, Yuanlong ;
Hou, Dongyang ;
Wang, Xue ;
Kong, Yawei .
GEOCARTO INTERNATIONAL, 2022, 37 (26) :13291-13310
[57]   A Land Cover Change Detection Method Combing Spectral Values and Class Probabilities [J].
Xing, Huaqiao ;
Zhu, Linye ;
Niu, Jingge ;
Chen, Bingyao ;
Wang, Wei ;
Hou, Dongyang .
IEEE ACCESS, 2021, 9 :83727-83739
[58]   A Web Service-Oriented Geoprocessing System for Supporting Intelligent Land Cover Change Detection [J].
Xing, Huaqiao ;
Chen, Jun ;
Wu, Hao ;
Hou, Dongyang .
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 8 (01)
[59]   Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine [J].
Xiong, Jun ;
Thenkabail, Prasad S. ;
Tilton, James C. ;
Gumma, Murali K. ;
Teluguntla, Pardhasaradhi ;
Oliphant, Adam ;
Congalton, Russell G. ;
Yadav, Kamini ;
Gorelick, Noel .
REMOTE SENSING, 2017, 9 (10)
[60]   Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China [J].
Xu, Li ;
Du, Hongru ;
Zhang, Xiaolei .
SUSTAINABILITY, 2019, 11 (24)