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中国农学通报 ›› 2014, Vol. 30 ›› Issue (33): 278-285.doi: 10.11924/j.issn.1000-6850.2014-0380

• 农业科技信息 • 上一篇    下一篇

作物类型遥感识别研究进展

张喜旺   

  1. 河南大学环境与规划学院
  • 收稿日期:2014-02-18 修回日期:2014-02-18 接受日期:2014-04-24 出版日期:2015-01-08 发布日期:2015-01-08
  • 通讯作者: 张喜旺
  • 基金资助:
    中国博士后科学基金资助项目“基于多源遥感数据综合季相节律和特征光谱的作物类型识别方法研究”(20100470994);河南省教育厅科学技术研究重点项目“河南省农作物长势动态监测方法研究”(13A420617);公益性行业(气象)科研专项“主要农作物生长动态监测与定量评价技术研究”(GYHY200906022)。

A Review of Remote Sensing Application in Crop Type Discrimination

  • Received:2014-02-18 Revised:2014-02-18 Accepted:2014-04-24 Online:2015-01-08 Published:2015-01-08

摘要: 及时获取作物种植面积是研究粮食区域平衡,预测农业综合生产力和人口承载力的基础。遥感技术已经成为提取作物种植面积的重要手段,而前提是识别作物。为了理清当前该领域的国内外研究现状,以遥感在作物类型识别中的应用为主线,归纳了国内外作物类型识别研究中常用的各类遥感数据,如资源遥感影像、气象遥感影像、高分辨率影像、高光谱影像和微波影像等,分析其优缺点和适用性;同时总结了利用遥感进行作物类型识别的3 类研究方法,包括基于光谱的识别方法、基于物候差异的识别方法以及光谱与物候相结合的方法,分析了各种方法的特点;指出目前作物类型遥感识别中存在的主要问题,如影像空间精度与价格的平衡问题,多分辨率遥感数据的综合应用问题,物候差异对作物识别的影响问题等;认为不同分辨率遥感数据的结合可以弥补各自不足,遥感影像的时相选择是提高精度的关键,另外需要应用除光谱和物候以外的更多解译标志;建议进一步深入研究作物识别机理和多尺度数据融合方法。以期为遥感技术在作物类型识别中的深入研究提供参考和借鉴。

关键词: 基因组, 基因组

Abstract: Timely crop planting acreage and yield information are important basis for researching regional balance of food supplies, predicting overall agricultural productivity and population carrying capacity. Remote sensing technology has become an important means to extract crop acreage, while the prerequisite is crop type discrimination. In order to illuminate the current status of research in this field at home and abroad, based on the remote sensing application in crop type discrimination, this paper firstly introduced the remote sensing data commonly used in crop type discrimination researches, including Landsat satellite imagery, meteorological satellite imagery, high resolution imagery, hyperspectral remote sensing imagery, and microwave remote sensing imagery, then further analyzed the advantages, disadvantages and practicability of each type of remote sensing data. Moreover, this paper summarized three types of crop type discrimination methods based on remote sensing, namely, the methods based on spectral characteristic, those based on phenology differences and the combination of both methods, and analyzed the characteristics of various methods. Finally this paper pointed out and discussed the current problems in the crop type remote sensing discrimination researches, for instance, the balance of image spatial resolution and price, comprehensive application of multi- resolution remote sensing data, the impact of phenological differences on crop type discrimination, etc. The author considered that the combination of different resolution remote sensing data could compensate for their lack; temporal selection of remote sensing images was the key to improve the discrimination accuracy; in addition, more interpretation signs need to be applied besides spectrum and phenology. Therefore, the author recommended further development crop discrimination mechanism and multi-scale image data fusion method, making research results more accurate and practical. The study could provide the references for the remote sensing crop type discrimination in-depth research.