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Spectral index selection method for remote moisture sensing under challenging illumination conditions

Graham, Christopher; Girkin, John; Bourgenot, Cyril

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Abstract

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.

Citation

Graham, C., Girkin, J., & Bourgenot, C. (2022). Spectral index selection method for remote moisture sensing under challenging illumination conditions. Scientific Reports, 12(1), Article 14555. https://doi.org/10.1038/s41598-022-18801-9

Journal Article Type Article
Acceptance Date Aug 19, 2022
Online Publication Date Aug 25, 2022
Publication Date 2022
Deposit Date Aug 26, 2022
Publicly Available Date Aug 26, 2022
Journal Scientific Reports
Publisher Nature Research
Peer Reviewed Peer Reviewed
Volume 12
Issue 1
Article Number 14555
DOI https://doi.org/10.1038/s41598-022-18801-9

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http://creativecommons.org/licenses/by/4.0/

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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.







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