Abstract
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.
Highlights
Diffuse reflectance soil spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis, as it is rapid, timely, less expensive, non-destructive, and allows for the simultaneous characterization of various soil properties [1]
The primary objective of this article is to elaborate a semi-empirical correction methodology for multispectral imaging datasets to account for the effects of the bidirectional reflectance distribution function (BRDF), based on the calculation of an anisotropy factor (ANIF) calibrated from unmanned aircraft systems (UAS)-based hemisphericaldirectional [6,14] bare soil reflectance measurements
10) almost was almost the same the calibration and validation datasets, the overall spectral stability was lower for the calibration and validation datasets, the overall spectral stability was in theinvalidation dataset
Summary
Diffuse reflectance soil spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis, as it is rapid, timely, less expensive, non-destructive, and allows for the simultaneous characterization of various soil properties [1]. The recent developments in unmanned aircraft systems (UAS) [3] and technological advancements in the performance and miniaturization of lightweight spectrophotometers [4] provide a toolset for the flexible assessment of the spatial and temporal variability of soil properties at a high resolution (
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
