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

Graham, Christopher; Girkin, John; Bourgenot, Cyril

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
• Electronic ISSN: 2045-2322
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 1
• Article Number: 14555
• DOI: https://doi.org/10.1038/s41598-022-18801-9
• Public URL: https://durham-repository.worktribe.com/output/1193174