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摘要： 多源低空间分辨率遥感数据在空间上的一致性对于其在全球变化研究中的集成使用有非常重要的意义。对此,以公认几何精度较高的MODIS数据为基准,对NOAA/AVHRR、FY-3/VIRR、FY-3/MERSI、FY-2/VISSR这4类国内外常用的低空间分辨率传感器的L1B数据进行了一系列相对几何精度评价和多项式相对几何校正的实验。相对几何精度评价的结果表明:MODIS数据与这4类L1B数据在几何精度上的偏差都比较大。在此基础上,选取少量均匀分布的控制点并采用不同阶数的多项式几何校正模型对多源数据进行空间一致性校正。校正结果表明:低阶的多项式几何校正模型就能对各种待校正数据的几何精度有显著的提升,使其与基准数据在空间上达到一致,满足全球变化研究对低分辨率多源遥感数据在空间一致性上的需求。 关键词： 多源遥感数据;  几何精度评价;  几何校正;  空间一致     Abstract: Low-resolution remote sensing data are kinds of very important data source to global change research,which is with of high temporal resolution and large coverage.However,multi\|source low\|resolution data have their own characteristics,such as geographical coverage,data accumulated time,which could not satisfy the demands of global change research by single low\|resolution data.Therefore,it is necessary to combine multi\|source large\|scale low resolution remote sensing data together being mutually complementary to meet its requirement.It is very necessary to make the multi\|source remote sensing data be consistent with geo\|location firstly,so a series of analysis and experiments of geometric correction was carried out.MOD09A1,as a kind of standard data product with higher accuracy of geo\|location,is used as the base data.Relative geometric accuracy evaluation between the base data and L1B data of NOAA/AVHRR,FY\|3/VIRR,FY\|3/MERSI,FY\|2/VISSR was done respectively.The result shows that the difference in geo\|location between the base data and the L1B data mentioned is significant,which is not good for the combination of them.Meanwhile,multi\|order polynomial geometric correction based on sparse and evenly distributed Ground Control Points(GCPs) of the data mentioned was carried out,since the selection of GCPs on low spatial resolution data was difficult.The result indicates that low\|order polynomial geometric correction could make a remarkable improvement on geometric accuracy of the multi\|source remote sensing data and be consistent in geo\|location with the MODIS base data,which would meet the requirement of the combination of multi\|source remote sensing data in global change research.

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