Spectral index selection method for remote moisture sensing under challenging illumination conditions.
Spectral index selection method for remote moisture sensing under challenging illumination conditions.
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DOI：
10.1038/s41598-022-18801-9
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发表时间：
2022-08-25
影响因子：
4.6
通讯作者：
Bourgenot, Cyril
中科院分区：
文献类型：
--
作者：
Graham, Christopher;Girkin, John;Bourgenot, Cyril
由于大气变化条件和光谱吸收，利用短波红外光谱中的被动太阳照明进行遥感会受到光谱带中强烈强度变化的影响。越来越需要对这些影响不敏感的更鲁棒的光谱分析方法来提高现场数据分析的准确性，并将系统的使用扩展到“非理想”照明条件。详细介绍了一种计算高光谱图像分析方法（命名为HIAM），用于获得最佳的反射率指数，用于遥感土壤水分含量。通过对干湿土壤高光谱图像的直方图分析，对比度和波长配对的测试，找到一个合适的光谱指数恢复土壤水分含量。在实验室和实地条件下对当地土壤样本的测量已被用于证明该指数对不同光照条件的鲁棒性，而公开可用的数据库已被用于测试选定的土壤类别。在这两种情况下，水分恢复RMS误差优于5%。由于该方法与材料类型无关，因此该方法也有可能应用于各种生物和人造样品。
Remote sensing using passive solar illumination in the Short-Wave Infrared spectrum is exposed to strong intensity variation in the spectral bands due to atmospheric changing conditions and spectral absorption. More robust spectral analysis methods, insensitive to these effects, are increasingly required to improve the accuracy of the data analysis in the field and extend the use of the system to “non ideal” illumination condition. A computational hyperspectral image analysis method (named HIAM) for deriving optimal reflectance indices for use in remote sensing of soil moisture content is detailed and demonstrated. Using histogram analysis of hyperspectral images of wet and dry soil, contrast ratios and wavelength pairings were tested to find a suitable spectral index to recover soil moisture content. Measurements of local soil samples under laboratory and field conditions have been used to demonstrate the robustness of the index to varying lighting conditions, while publicly available databases have been used to test across a selection of soil classes. In both cases, the moisture was recovered with RMS error better than 5%. As the method is independent of material type, this method has the potential to also be applied across a variety of biological and man-made samples.
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影响因子：
4.6
作者：
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通讯作者：
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作者：
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通讯作者：
Bourgenot, Cyril
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作者：
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