The electric propulsion system NanoFEEP was integrated and tested in orbit on the UWE-4 satellite, which marks the first successful demonstration of an electric propulsion system on board a 1U CubeSat. In-orbit characterization measurements of the heating process of the propellant and the power consumption of the propulsion system at different thrust levels are presented. Furthermore, an analysis of the thrust vector direction based on its effect on the attitude of the spacecraft is described. The employed heater liquefies the propellant for a duration of 30 min per orbit and consumes 103 ± 4 mW. During this time, the respective thruster can be activated. The propulsion system including one thruster head, its corresponding heater, the neutralizer and the digital components of the power processing unit consume 8.5 ± 0.1 mW $·\mathsf{\mu }$ A⁻¹ + 184 ± 8.5 mW and scales with the emitter current. The estimated thrust directions of two thruster heads are at angles of 15.7 ± 7.6^∘ and 13.2 ± 5.5^∘ relative to their mounting direction in the CubeSat structure. In light of the very limited power on a 1U CubeSat, the NanoFEEP propulsion system renders a very viable option. The heater of subsequent NanoFEEP thrusters was already improved, such that the system can be activated during the whole orbit period.

Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UAS) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (Linear Model; Random Forests, RF; Gradient Boosting Machines, GBM) and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors, but was not available in our study. Therefore, we tested the added value of this structural information with in-situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in Southern Germany to obtain in-situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized and all model set-ups were run with a six-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor-predictor set combinations with average (avg) R²_cv of 0.48, RMSE_{cv, avg} of 53.0 g m² and rRMSE_{cv, avg} of 15.9 % for DM, and with R²_{cv, avg} of 0.40, RMSE_{cv, avg} of 0.48 wt.% and rRMSE_{cv, avg} of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms and predictor sets notably improved the model performance. The best model performance for the estimation of DM (R²_cv = 0.67, RMSE_cv = 41.9 g m², rRMSE_cv = 12.6 %) was achieved with a RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of a RF model with all predictors and SEQ sensor data (R²_cv = 0.47, RMSE_cv = 0.45 wt.%, rRMSE_cv = 14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating ML algorithm improved the model performance substantially, which shows the importance of this step.