宽频带遥感数据窄带光谱指数估算的精度

S. Stankevich
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摘要

窄带光谱指数在遥感的各种应用中具有相当大的信息量和重要性——用于评估植被、土壤、水体和其他地表构造的状况。然而,直接测量窄带光谱指数需要高光谱成像。但是大多数现代多光谱航空成像系统都是宽带的。因此,直接从宽带遥感数据计算窄带指数是不可能的。本文讨论了利用窄带和宽带指数本身、近波段源宽带和窄带信号相互关系统计模型以及地表反射率准连续光谱从宽带向窄带转换统计模型对宽带遥感数据进行窄带光谱指数恢复的方法。对宽频带多光谱卫星图像恢复窄带光谱指数的实验精度进行了估计。考虑了从可见光到短波红外光谱范围内最复杂的3个窄带光谱指数,即转化叶绿素吸收反射率指数(TCARI)、优化土壤调整植被指数(OSAVI)和归一化氮差指数(NDNI)。对上述三种窄带光谱指数恢复方法进行了分析。结果表明,回归恢复后的信号在谱带内效果最差,光谱平移方法效果最好。因此,建议采用基于光谱平移的方法进行实际实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accuracy of narrow-band spectral indices estimation by wide-band remote sensing data
Narrow-band spectral indices are quite informative and important in various applications of remote sensing – to assess the condition of vegetation, soils, water bodies and other land surface formations. However, direct measurement of narrow-band spectral indices requires hyperspectral imaging. But most of modern multispectral aerospace imaging systems are wide-band. Accordingly, it is not possible to calculate the narrow-band index directly from wide-band remote sensing data. This paper discusses approaches to the narrow-band spectral indices restoration by wide-band remote sensing data using statistical models of interrelations of narrow- and wide-band indices itself, of source wide-band and narrow-band signals in close spectral bands, as well as of land surface reflectance quasi-continuous spectra translation from wide bands to narrow ones.The experimental accuracy estimation of narrow-band spectral indices restoration by wide-band multispectral satellite image is performed. Three most complicated narrow-band spectral indices, which covering a range of spectrum from visible to short-wave infrared, were considered, namely – the transformed chlorophyll absorption in reflectance index (TCARI), the optimized soil-adjusted vegetation index (OSAVI) and the normalized difference nitrogen index (NDNI). All three mentioned methods for narrow-band spectral indices restoration are analyzed. The worst result is demonstrated for regression-restored signals in spectral bands, and the best result is for the spectra translation method. Therefore, the method on the basis of spectra translation is recommended for practical implementation.
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