基于RS和GIS的农作物估产方法研究进展

doi:10.6046/gtzyyg.2014.04.01

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摘要

实时获取农作物长势及产量等信息对于现代农业的发展具有重要意义。近年来，随着遥感技术（remote sensing，RS）和地理信息系统（geographic information system，GIS）广泛应用于农作物估产领域，相继出现了一些较为实用的估产方法，主要有结合辅助数据的估产方法、基于植被指数的估产方法、基于特定模型的估产方法和基于农作物估产平台（软件）的开发等。其中，基于植被指数的估产方法又分为单一和多植被指数估产2类方法。在对近年来该领域大量文献深入研究的基础上，着重就几类热点方法展开论述，并对每类方法的优势和缺陷进行了评述，最后对该领域需要进一步研究的方向进行了探讨和展望，以期为后续研究提供参考。

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关键词 ： ETM⁺6, γ能谱, 热红外遥感, 花岗岩, 铀矿, 预测

Abstract：

The acquisition of such agricultural information as crop growth and output is of great significance for the development of modern agriculture. Recently, the techniques of remote sensing (RS) and geographic information system (GIS) have been widely used to estimate the crop yield and, as a result, a set of practical yield estimation methods are put forward. The yield estimation methods mainly include the yield estimation method combined with relative secondary data, the yield estimation method based on vegetation index, the yield estimation method based on the specific models, and the development of crop yield estimation platform (software). Among these means, the yield estimation method based on vegetation index is divided into two categories, i.e., the single vegetation yield estimation method and the multiple vegetation index yield estimation method. A few crop yield estimation methods are analyzed in this paper based on studying many recently published papers in this field, and the advantages and disadvantages of each method are reviewed. In addition, the orientations for further research in this field are discussed and forecast so as to provide some valuable references for researchers in this field.

Key words： ETM⁺6 γ spectrum thermal infrared remote sensing granite uranium ore deposit prediction

收稿日期: 2013-08-26 出版日期: 2014-09-17

:	TP391
	TP79

作者简介: 胡莹瑾（1971-），女，硕士研究生，副教授，研究方向为计算机技术及其在农业领域的应用。Email：huyinjin2013vip@126.com。

引用本文:

胡莹瑾, 崔海明. 基于RS和GIS的农作物估产方法研究进展[J]. 国土资源遥感, 2014, 26(4): 1-7.
HU Yingjin, CUI Haiming. Progress in the study of crop yield estimation methods based on remote sensing and geographic information system. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 1-7.

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https://www.gtzyyg.com/CN/10.6046/gtzyyg.2014.04.01 或 https://www.gtzyyg.com/CN/Y2014/V26/I4/1

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