Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

Abstract

Abstract:Reflectance anisotropy is a signal that contains information on the optical and structural properties of a surface and can be studied by performing multi-angular reflectance measurements that are often done using cumbersome goniometric measurements. In this paper we describe an innovative and fast method where we use a hyperspectral pushbroom spectrometer mounted on a multirotor unmanned aerial vehicle (UAV) to perform such multi-angular measurements. By hovering the UAV above a surface while rotating it around its vertical axis, we were able to sample the reflectance anisotropy within the field of view of the spectrometer, covering all view azimuth directions up to a 30 • view zenith angle. We used this method to study the reflectance anisotropy of barley, potato, and winter wheat at different growth stages. The reflectance anisotropy patterns of the crops were interpreted by analysis of the parameters obtained by fitting of the Rahman-Pinty-Verstraete (RPV) model at a 5-nm interval in the 450-915 nm range. To demonstrate the results of our method, we firstly present measurements of barley and winter wheat at two different growth stages. On the first measuring day, barley and winter wheat had structurally comparable canopies and displayed similar anisotropic reflectance patterns. On the second measuring day the anisotropy of crops differed significantly due to the crop-specific development of grain heads in the top layer of their canopies. Secondly, we show how the anisotropy is reduced for a potato canopy when it grows from an open row structure to a closed canopy. In this case, especially the backward scattering intensity was strongly diminished due to the decrease in shadowing effects that were caused by the potato rows that were still present on the first measuring day. The results of this study indicate that the presented method is capable of retrieving anisotropic reflectance characteristics of vegetation canopies and that it is a feasible alternative for field goniometer measurements.

Optimizing Radiometric Processing and Feature Extraction of Drone Based Hyperspectral Frame Format Imagery for Estimation of Yield Quantity and Quality of a Grass Sward

“…In the case of the NIR band, the BRDF had a bowl shape which is the expected behaviour (Figure 6b). The results regarding the behaviour of different spectral ranges are consistent with previous results by Roosjen et al (2016). …”

“…For example, previous studies by Roosjen et al (2016) showed that the anisotropy of barley, winter wheat and potato in two measurement dates was different, and could potentially be used as a signal in operational remote sensing. Recently, Roosjen et al (2018) used the PROSAIL radiative transfer tool to estimate the leaf area index and the leaf chlorophyll content using the nadir and multi-angular data collected by the UAV.…”

Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment

“…Figure 13 shows the estimated BRDF parameters and standard deviations of all bands for the full dataset and for the f34 dataset. The parameters of the neighboring bands correlated while the greatest difference appeared between the visible and NIR spectral ranges, which we had expected [51]. Some differences also appeared between the neighboring bands, which can partially be attributed to uncertainties in the sensor calibration and band quality.…”

“…The plots were quite noisy because observations were not filtered. The modeled anisotropy (Equation (5); Figure 12b,e) followed the expected behavior [51]. The sensor is viewing the well-illuminated side of the canopies in the backward direction, which results in a higher reflectance, whereas in the forward scattering direction, the shadowed side of the canopies is viewed, which results in a lower reflectance.…”

“…Another option is to utilize the anisotropy as an additional feature in the remote sensing process [51,53,54]. For example, Roosjen et al [53] used commercial software to calculate orthophotos for each overlapping image and to model the anisotropy using the reflectance values obtained from the images.…”