首页 >  2004, Vol. 8, Issue (6) : 611-617

摘要

全文摘要次数: 3439 全文下载次数: 46
引用本文:

DOI:

10.11834/jrs.20040611

收稿日期:

2003-12-18

修改日期:

2004-04-27

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美国冬小麦产量遥感预测方法
中国科学院遥感应用研究所,北京100101
摘要:

介绍了依据时序遥感植被指数数据进行产量预测的方法。通过美国冬小麦产量的历史趋势分析去除趋势产量 ,得到气象产量。利用区域作物生长过程线 ,提取曲线的各个特征参数 ,并将各参数与气象产量的值进行相关分析 ,得到美国冬小麦产量遥感敏感因子 ,采用一次线性拟合的方法建立回归方程 ,估算当年的冬小麦产量。依据此方法对美国 2003年各州的冬小麦单产进行了预测 ,并将最终的预测结果与美国农业统计局的数据进行了对比 ,两者间的误差在-1142%至1110%之间 ,相关系数为089。

关键词:

作物产量  遥感  美国  冬小麦
Winter Wheat Yield Predicting for America Using Remote Sensing Data
Abstract:

Inth isp aperw ed evelopeda na pproachu singti mes eriesN ormalizeD iferenceV egetationI ndex(NDVI)d erived from SPOT VGT for crop yield predicting in American during a five-year span(1998-2002). In o rd er to r emovec louda nde xtractth ec haracteristicso fth ev egetation街namics,th eH armonicA nalysiso fT imeS eries (HANTS)a lgorithmw asu sedo nth eti mes eriesof N DVIim age.Toe xploitef ectivelyth eti mes eriesof N DVI,lin kingth ema s mucha sp ossiblet oc ropg rowingc onditions,in dicatorsw hichc anb er elatedc loselyt oc ropy ieldw eree xtracteda ndu sedf or building the predicting models.The weight average method was used to extract crop growth profile with land cover and SPOT Vegetationd ata.A ndt heni ndicatorsw erere trievedf romt hec ropg rowthp rofiles,in cludinga scends peed,m aximum,d escend speed, accumulative total before maximum and accumulative total after maximum. At th e m eanti me,th eti mes erieso fw interw heatyi elda re processedu singa li nearu pwardt rendf unctioni n1 980t o2 002 to reduce the tendency of the yield. The weather yield is the diference of the actual yield and the trend yield. The weather yield will be predicted with remote sensing indicators.The weather yield and corresponding indicators are regressed. Only those indicators with high correlation coefficient are selected. The wheat yield are the summary of weather yield and the trend yield. The m od elw asu sedt op redictw interw heaty ieldi nA merica.T hed iferencei sa bout一11.4% to7 .01% byc omparing with USDA NASS data. And the relative coefficient between predicting yield and NASS yield is 0.89.

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