Investigating the Potential of a Newly Developed UAV-based VNIR/SWIR Imaging System for Forage Mass Monitoring

• Published in: Journal of photogrammetry, remote sensing and geoinformation science remote sensing and geoinformation science, 2020-12, Vol.88 (6), p.493-507
• Main Authors: Jenal, Alexander, Lussem, Ulrike, Bolten, Andreas, Gnyp, Martin Leon, Schellberg, Jürgen, Jasper, Jörg, Bongartz, Jens, Bareth, Georg
• Format: Article
• Language: English
• Subjects: Aerospace Technology and Astronautics; Astronomy; Computer Imaging; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Observations and Techniques; Original Article; Pattern Recognition and Graphics; Remote Sensing/Photogrammetry; Signal,Image and Speech Processing; Vision
• ISSN: 2512-2789; 2512-2819
• DOI: 10.1007/s41064-020-00128-7

Remote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system to derive well-established vegetation indices (VIs) efficiently. This study investigates the potential of such a multi-camera system with a novel approach to extend spectral sensitivity from visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) (400–1700 nm) for estimating forage mass from an aerial carrier platform. The system test was performed in a grassland fertilizer trial in Germany near Cologne in late July 2019. Within 37 min, a spectral response in four different wavelength bands in the NIR and SWIR range was acquired during two consecutive flights. Spectral image data were calibrated to reflectance using two different methods. The resulting reflectance data sets were processed to orthomosaics for each wavelength band. From these orthomosaics for both calibration methods, the four-band NIR/SWIR GnyLi VI and the two-band NIR/SWIR Normalized Ratio Index (NRI), were calculated. During both UAV flights, spectral ground truth data were recorded with a spectroradiometer on 12 plots in total for validation of camera-based spectral data. The camera and spectroradiometer data sets were directly compared in resulting reflectance and further analyzed with simple linear regression (SLR) models to predict dry matter (DM) yield. In the camera-based SLRs, the NRI performed best with R 2 of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with R 2 of 0.71 and 0.73 (RMSE: 0.19 and 0.18). These results clearly indicate the potential of the camera system for applications in forage mass monitoring.