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The Bidirectional Reflectance Distribution Function (BRDF) quantifies the distribution of the spectral reflectance of a target surface at various viewing and illumination angles. In-field measurement of the BRDF of vegetation canopies improves the characterization of off-nadir measurements and informs radiative transfer models of canopy reflectance, where the Lambertian assumption does not hold. However, current field goniometers are unable to measure BRDF efficiently, especially for tall vegetation across the growing season because of the limitations of clearance, field accessibility, and flexibility of the sensor field of view. In this study, we explored the potential of using a large-scale cable-suspended field phenotyping system to quantify BRDF of the canopy reflectance at selected bands and Vegetation Indices (VIs) of maize and soybean canopies. The system performance benefited from the following conditions: no crop damage, full-season field accessibility of tall canopies, automatic measurement, and accurate positioning. Correlation analysis shows that a strong correlation exists among the reflectance, VIs, and the Green Pixel Fraction. The hemispheric distributions of the spectral reflectance at selected bands and VIs were quantified at multiple dates. Hot spots were observed in the backscatter direction at visible and near-infrared (NIR) bands at the largest sensor zenith angle around the Solar Principal Plane (SPP). In contrast, cold spots of the Normalized Difference Vegetation Index (NDVI) and its related VIs were observed in the backscatter direction around solar zenith angles. The row effect was found for the maize canopy at the NIR band and the Near-infrared reflectance of vegetation (NIR v). NDVI had the lowest anisotropy index values among investigated bands and VIs. This system could be further leveraged to generate rapid and detailed BRDF data at a high spatiotemporal resolution using multiple sensors. [Display omitted] • An aerial field goniometer was brought forward to quantify canopy BRDF. • Its advantages were validated, like full-season accessibility for tall crop. • BRDF characteristics of selected reflectance and indicis were collected. • Canopy anisotropy were quantified for different dates and sensor angles. [ABSTRACT FROM AUTHOR]

Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets. [ABSTRACT FROM AUTHOR]