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

文献类型： 外文期刊

作者： Hu Qiong 1 ; Wu Wen-bin 1 ; Song Qian 1 ; Lu Miao 1 ; Chen Di 1 ; Yu Qiang-yi 1 ; Tang Hua-jun 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agri Informat, Minist Agr, Beijing 100081, Peoples R China

2.Heilongjiang Acad Agr Sci, Remote Sensing Technol Ctr, Harbin 150086, Peoples R China

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

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）

ISSN： 2095-3119

年卷期： 2017 年 16 卷 2 期

页码：

收录情况： SCI

摘要： 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 (MODO9A1) 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 (LSWI) 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.

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