Short-wave infrared (SWIR) imaging systems with unmanned aerial vehicles (UAVs) are rarely used for remote sensing applications, like for vegetation monitoring. The reasons are that in the past, sensor systems covering the SWIR range were too expensive, too heavy, or not performing well enough, as, in contrast, it is the case in the visible and near-infrared range (VNIR). Therefore, our main objective is the development of a novel modular two-channel multispectral imaging system with a broad spectral sensitivity from the visible to the short-wave infrared spectrum (approx. 400 nm to 1700 nm) that is compact, lightweight and energy-efficient enough for UAV-based remote sensing applications. Various established vegetation indices (VIs) for mapping vegetation traits can then be set up by selecting any suitable filter combination. The study describes the selection of the individual components, starting with suitable camera modules, the optical as well as the control and storage parts. Special bandpass filters are used to select the desired wavelengths to be captured. A unique flange system has been developed, which also allows the filters to be interchanged quickly in order to adapt the system to a new application in a short time. The characterization of the system was performed in the laboratory with an integrating sphere and a climatic chamber. Finally, the integration of the novel modular VNIR/SWIR imaging system into a UAV and a subsequent first outdoor test flight, in which the functionality was tested, are described.

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R²: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R²: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.

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.

Flooding is one of the most common natural disasters, causing emergencies and significant damage to infrastructure. Amid crises, entities such as the World Food Program employ remotely sensed images, usually via drones, to analyze the situation quickly and plan life-saving interventions. Suitable Computer Vision (CV) tools are necessary to assist experts of first responder teams with the analysis of the image data, increasing their productivity and enabling strategic resource allocation. Many state-of-the-art CV-tools use supervised Deep Learning (DL) techniques, for which labeled training data is needed. In this paper, we present the BlessemFlood21 dataset to support the development of DL-based CV-tools for the task of advanced flood analysis in non-coastal river flood scenarios. In particular, we address the problem of segmenting water from RGB images. The presented resulting dataset consists of high-definition, georeferenced RGB images labeled with precise water masks. The water masks are derived using a proposed semi-automatic human-in-the-loop strategy based on the use of additionally imaged NIR data together with a classical Random Forest approach. We assess the resulting dataset and provide a baseline for future advancements in flood mapping by training and evaluating three different state-of-the-art segmentation models (UNet++, DeepLabV3+, SegFormer-B5). Further, we showcase the potential use of the dataset with several experiments. In particular, we compare with the Floodnet dataset and, we employ the segmentation models trained on our dataset together with a Digital Elevation Model to showcase the potential to estimate flood water levels.

Over the Hahnöfer Nebenelbe, a part of the Elbe estuary near Hamburg, Germany, a combined aerial survey with an unmanned aerial system (UAV) and a gyrocopter was conducted to acquire information about the water surface temperatures. The water temperature in the estuary is important for biological processes and living conditions of riverine organisms. This study aimed to develop a workflow that allows for comparing and analysing surface temperatures acquired by two different remote sensing systems. The thermal infrared (TIR) datasets were compared with in situ measurements gathered during the data acquisition, where both TIR datasets showed a varying bias. Potential error sources regarding the absolute and relative accuracy were investigated and modelled based on the available measurements, including emissivity, atmosphere, skin effect at the water surface, camera flat field correction and calibration. The largest effects on the observed TIR water temperature had the camera calibration and the modelled atmospheric effects. After the correction steps, both datasets could be combined to create a multitemporal representation of the temperature pattern and profiles over the survey area’s wadden flats.

This article introduces a novel stereo-camera system for the measurement of solar-induced chlorophyll fluorescence (SIF) at 760 nm. The instrument uses optical interference filters to gain high background radiation-suppression in the telluric oxygen absorption band at 760 nm, to measure the weak SIF signal. Featuring spatially high-resolution images and a lightweight setup, the system was built for ground- and drone-based field applications. The technical setup of the device and the used methodology are presented as well as a theoretical performance simulation, indicating a maximal reduction of background radiation by a factor of five. Experimental results show that steady-state fluorescence can be measured with signal to noise ratios between five and ten, depending on saturation level of the sensor and aperture settings of the lens. Intensity changes lower than 0.2 mWm ^-2 sr ^-1 nm ^-1 , emitted by a calibrated light emitting diode (LED) reference panel, can be reliably distinguished under direct sun illumination. The system’s capability to detect fast changes in photosynthetic dynamics, with both high spatial and temporal resolution, is demonstrated by a video sequence of a leaf during dark-light transition. In a static, platform-based operation, the classification of fluorescent and non-fluorescent surfaces under natural conditions is presented.