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How do temporal and spectral features .matter in crop classification in Heilongjiang Province, China？

Journal of Integrative Agriculture 2017年第2期16卷 324-336页

作者： HU Qiong WU Wen-bin SONG Qian LU Miao CHEN Di YU Qiang-yi TANG Hua-jun Key Laboratory of Agri-informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 P.R.China Remote Sensing Technology Center Heilongjiang Academy of Agricultural Sciences Harbin 150086 PR. China

How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer （MODIS） surface reflectance product （MOD09A1） data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index （SI） was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index （NDTI）, land surface water index （LSWl） and enhanced vegetation index （EVI） are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy （producers＇ accuracy of 94.03% and users＇ accuracy of 93.77%） with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.

关键词： crop identification temporal feature spectral feature feature selection MODIS

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identification and mapping of soybean and maize crops based on Sentinel-2 data

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International Journal of Agricultural and Biological Engineering 2020年第6期13卷 171-182页

作者： Bao She Yuying Yang Zhigen Zhao Linsheng Huang Dong Liang Dongyan Zhang School of Geomatics Anhui University of Science&TechnologyHuainan 232001AnhuiChina National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China

Soybean and maize are important raw materials for the production of food and livestock *** mapping of these two crops is of great significance to crop management,yield estimation,and crop-damage *** this study,two towns in Guoyang County,Anhui Province,China,were selected as the study area,and Sentinel-2 images were adopted to map the distributions of both crops in the 2019 growing *** data obtained on August 18(early pod-setting stage of soybean)was determined to be the most applicable to soybean and maize mapping by means of the Jeffries-Matusita(JM)***,three machine-learning algorithms,i.e.,random forest(RF),support vector machine(SVM)and back-propagation neural network(BPNN)were employed and their respective performance in crop identification was evaluated with the aid of 254 ground truth *** appeared that RF with a Kappa of 0.83 was superior to the other two ***,twenty candidate features containing the reflectance of ten spectral bands(spatial resolution at 10 m or 20 m)and ten remote-sensing indices were input into the RF algorithm to conduct an important *** features were screened out and served as the optimum subset,the mapping results of which were assessed based on the ground truth derived from the unmanned aerial vehicle(UAV)images covering six ground *** optimum feature-subset achieved high-accuracy crop mapping,with a reduction of data volume by 65%compared with the total twenty features,which also overrode the performance of ten spectral ***,feature-optimization had great potential in the identification of the two ***,the findings of this study can provide a valuable reference for mapping soybean and maize in areas with a fragmented landscape of farmland and complex planting structure.

关键词： soybean and maize crop identification Sentinel-2 data machine learning feature selection

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