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

Abstract

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 $$R^2$$ R 2 of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with $$R^{2}$$ 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.

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

“…This approach allowed biomass (fresh and dry), crop moisture (CM), and N C to be estimated for the 72 test plots (18 per row). This step is possible because numerous studies have already proven the robustness of using CH as an estimator for biomass [49][50][51][52][53][54][55] and using biomass as an estimator for N C and N uptake [15,38,56]. With the derived data for dry biomass and N C , the N uptake could be calculated in line with Lemaire et al [57] for the 72 test plots.…”

“…This study's evaluation applies a novel VNIR/SWIR multi-camera system [23], cam-SWIR. In a permanent grassland trial, Jenal et al [38] successfully tested this prototype for its suitability in estimating forage mass. The system's modular two-part design means it can be integrated into various aerial remote sensing platforms, focusing on UAV-based multi-rotor vehicles (see Figure 5).…”

“…Section 2.5 describes the spectral workflow that transforms the raw DN image data sets into plot-wise VI reflectance data sets. For more details on the performance of the two VIs, see also [9,15,[23][24][25]27,38]. (IV) Regression models (RM) were created for the destructive field sampling plots (18 plots per date: n = 108) from (I) the UAV-derived crop height data and from (II) the manual and destructive sampling data, resulting in four regression models for biomass (fresh and dry), crop moisture, and N C .…”

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

“…vegetative plant parts (40); inflorescence emergence (50); flowering (60); development of fruit (70); ripening (80); senescence (90). The early stages (BBCH lower than 30) included only weed studies but covered 60% of them.…”

“…After weighing, the sample is dried in an oven to determine the amount of dry biomass [67]. Cut and dry is the most common reference data collection method in biomass estimation studies, and it has been used in various recent studies [69][70][71][72][73]. The method provides objective reference data, but the cutting phase can include lot of variation.…”