Abstract
Analysis of the spectral response of vegetation using optical sensors for non-destructive remote monitoring represents a key element for crop monitoring. Considering the wide presence on the market of unmanned aerial vehicle (UAVs) based commercial solutions, the need emerges for clear information on the performance of these products to guide the end-user in their choice and utilization for precision agriculture applications. This work aims to compare two UAV based commercial products, represented by DJI P4M and SENOP HSC-2 for the acquisition of multispectral and hyperspectral images, respectively, in vineyards. The accuracy of both cameras was evaluated on 6 different targets commonly found in vineyards, represented by bare soil, bare-stony soil, stony soil, soil with dry grass, partially grass covered soil and canopy. Given the importance of the radiometric calibration, four methods for multispectral images correction were evaluated, taking in account the irradiance sensor equipped on the camera (M1–M2) and the use of an empirical line model (ELM) based on reference reflectance panels (M3–M4). In addition, different DJI P4M exposure setups were evaluated. The performance of the cameras was evaluated by means of the calculation of three widely used vegetation indices (VIs), as percentage error (PE) with respect to ground truth spectroradiometer measurements. The results highlighted the importance of reference panels for the radiometric calibration of multispectral images (M1–M2 average PE = 21.8–100.0%; M3–M4 average PE = 11.9–29.5%). Generally, the hyperspectral camera provided the best accuracy with a PE ranging between 1.0% and 13.6%. Both cameras showed higher performance on the pure canopy pixel target, compared to mixed targets. However, this issue can be easily solved by applying widespread segmentation techniques for the row extraction. This work provides insights to assist end-users in the UAV spectral monitoring to obtain reliable information for the analysis of spatio-temporal variability within vineyards.
Highlights
The first result presented in this paper aims at a comparison of the spectral signatures acquired in flight by the unmanned aerial vehicles (UAVs) equipped with the hyperspectral camera SENOP HSC-2 and the validation data acquired on the ground with the reference spectroradiometer GER 3700
Through a visual evaluation of the spectral signatures of the 8 panels presented in Figure 5 it can be observed that both the spectroradiometer (Figure 5a) and hyperspectral camera (Figure 5b) show the same trends, with a slight overestimation by the hyperspectral camera in the visible region (500–700 nm)
The data acquired on the 3 OptoPolymer panels (Figure 5c) and on the 5 Senop panels (Figure 5d) were separately analysed, and both provided an excellent coefficient of determination (R2 = 0.99), good results in terms of absolute values with an root-mean-square error (RMSE) of 12,905.34 and 10,082.95 × 10−10 W/cm2 /nm/sr for the OptoPolymer and Senop panels, respectively
Summary
The spectral canopy response to solar radiation analysed through calculation of a wide range of vegetation indices (VIs) is the basis of remote sensing applications in agriculture. Both structural aspects, biochemical composition, physiological processes and foliar symptoms influence the ways in which vegetation reflects light in different regions of the electromagnetic spectrum [1,2,3]. Spectral analysis provides important information on the vegetative state and needs of crops, optimal acquisition of the spectral data must consider the peculiarities of each crop, since there are structure and characteristics that influence the spectral response. Among different kinds of crops, Remote Sens.
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