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Article

A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil

1
Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, 1348 Louvain-La-Neuve, Belgium
2
Remote Sensing Unit, Vlaams Instituut voor Technologisch Onderzoek (VITO), Boeretang 200, 2400 Mol, Belgium
3
FNRS Belgium, Rue d’Egmont 5, 1000 Bruxelles, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Kim Calders, Bas van Wesemael, Trissevgeni Stavrakou, Jean-Christophe Schyns, Joost Vandenabeele, Hans Lievens and Dimitry van der Zande
Remote Sens. 2022, 14(3), 537; https://doi.org/10.3390/rs14030537
Received: 29 December 2021 / Revised: 17 January 2022 / Accepted: 21 January 2022 / Published: 23 January 2022
(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)
The recent developments in the performance and miniaturization of uncrewed aircraft systems (UAS) and multispectral imaging sensors provide new tools for the assessment of the spatial and temporal variability of soil properties at sub-meter resolution and at relatively low costs, in comparison to traditional chemical analysis. The accuracy of multispectral data is nevertheless influenced by the anisotropic behaviour of natural surfaces, framed in the general theory of the bidirectional reflectance distribution function (BRDF). Accounting for BRDF effects in multispectral data is paramount before formulating any scientific interpretation. This study presents a semi-empirical spectral normalization methodology for UAS-based multispectral imaging datasets of bare soils to account for the effects of the BRDF, based on the application of an anisotropy factor (ANIF). A dataset of images from 15 flights over bare soil fields in the Belgian loam belt was used to calibrate a model relating the ANIF to a wide range of illumination geometry conditions by using only two angles: relative sensor-pixel-sun zenith and relative sensor-pixel-sun azimuth. The employment of ANIF-corrected images for multispectral orthomosaic generation with photogrammetric software provided spectral maps free of anisotropic-related artefacts in most cases, as assessed by several ad hoc indexes, and was also tested on an independent validation set. Most notably, the standard deviation in the measured reflectance of the same georeferenced point by different pictures decreased from 0.032 to 0.023 (p < 0.05) in the calibration dataset and from 0.037 to 0.030 in the validation dataset. The validation dataset, however, showed the presence of some systematic errors, the causes of which require further investigation. View Full-Text
Keywords: soil monitoring; proximal sensing; UAS; multispectral imagery; soil anisotropy; BRDF soil monitoring; proximal sensing; UAS; multispectral imagery; soil anisotropy; BRDF
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MDPI and ACS Style

Crucil, G.; Zhang, H.; Pauly, K.; Van Oost, K. A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil. Remote Sens. 2022, 14, 537. https://doi.org/10.3390/rs14030537

AMA Style

Crucil G, Zhang H, Pauly K, Van Oost K. A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil. Remote Sensing. 2022; 14(3):537. https://doi.org/10.3390/rs14030537

Chicago/Turabian Style

Crucil, Giacomo, He Zhang, Klaas Pauly, and Kristof Van Oost. 2022. "A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil" Remote Sensing 14, no. 3: 537. https://doi.org/10.3390/rs14030537

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