The design of photobioreactors for microalgae cultivation aims to achieve an architecture that allows the most efficient photosynthetic growth. The availability of light at wavelengths that are important for photosynthesis is therefore particularly crucial for reactor design. While testing different reactor types in practice is expensive, simulations could effectively limit the range of material and reactor design options. In this study, procedural three-dimensional modelling together with ray tracing was used to create virtual models of a conventional glass photobioreactor lit from the outside and a steel photobioreactor with embedded light sources. The measured transmittance and reflectance of Chlorella vulgaris culture were used as a basis for light interaction simulation, and spectral images of the same species were used to validate the simulation results. This type of simulation could have the potential for comparing different reactor architectures, geometries and light attenuation to facilitate the transition to large-scale cultivation. Our results show that the proposed simulator is usable in photobioreactor geometry design as well as in the estimation of available illumination on wavelengths where microalgae have strong absorption peaks, but the handling of light scattering still needs improvement. To the authors’ best knowledge, this is the first attempt, not focused on a specific use case, to build a general photobioreactor design tool capable of estimating hyperspectral light attenuation in microalgae suspension. All software code and used datasets are made available for the reader as open source.

Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors’ mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry–Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measure emission angle dependent reflectance from a multi-view inferred point cloud. We determined the intrinsic and extrinsic camera parameters through calibration, used global and local registration algorithms to combine point clouds from different viewpoints, created a dense point cloud and determined the angle dependent reflectances from it. The method could successfully combine the 3D point cloud data and hyperspectral data from different viewpoints of a reference colorchecker board. The point cloud registrations gained $0.29$–$0.36$ fitness for inlier point correspondences and RMSE was approx. 2, which refers a quite reliable registration result. The RMSE of the measured reflectances between the front view and side views of the targets varied between $0.01$ and $0.05$ on average and the spectral angle between $1.5$ and $3.2$ degrees. The results suggest that changing emission angle has very small effect on the surface reflectance intensity and spectrum shapes, which was expected with the used colorchecker.

In recent decades, remote sensing of vegetation by hyperspectral imaging has been of great interest. An important part in interpreting the remotely sensed spectral data is played by simulators, which approximate the connection between plants’ biophysical and biochemical properties and detected spectral response. We introduce improvements and new features to recently published hyperspectral leaf model HyperBlend. We present two methods for increasing simulation speed of the model up to 200 times faster with slight decrease in simulation accuracy. We integrate the well-known PROSPECT leaf model into HyperBlend allowing us to use the PROSPECT parametrization for leaf simulation. For the first time, we show that HyperBlend generalizes well and can be used to accurately simulate a wide variety of plant leaf spectra. HyperBlend is available as an open-source Python project under MIT license in a GitHub repository available at: https://github.com/silmae/hyperblend.

The calibration stability of miniature spectral imagers based on a piezolectrically tunable Fabry-Perot interferometer (FPI) has not been previously examined in long-term use. These instruments are increasingly launched to space, for example on the Kuva Space’s Hyperfield-fleet of Earth observation satellites and with the ESA Hera asteroid mission. Difficulty of re-calibration in a space environment means that prior knowledge of possible calibration drift is invaluable. In this study, we examined the performance of an FPI imager that was used for nine years in a hospital environment. We analyzed images gathered during the use of the imager, looking for systematic errors. A degree of drift in the results was found, but we could not determine whether it was caused by imager degradation or changes in the measurement setup. We also performed new characterization measurements for the imager. Based on the measurement results, the FPI control circuits’ response to setpoint values sent to them appears to have changed. This error could be caused by a leak in the hermetic seal around the FPI. Repeated measurements showed similar results, indicating that the imager performance is consistent.

In October 2024, European Space Agency’s Hera mission will be launched, targeting the binary asteroid Didymos. Hera will host the Juventas and Milani CubeSats, the first CubeSats to orbit close to a small celestial body performing scientific and technological operations. The primary scientific payload of the Milani CubeSat is the SWIR, NIR, and VIS imaging spectrometer ASPECT. The Milani mission objectives include mapping the global composition and the characterization of the binary asteroid surface. Onboard data processing and evaluation steps will be applied due to the limited data budget for the downlink to Earth and to perform the technological demonstration of a novel semi-autonomous hyperspectral imaging mission. Before downloading, the image data is evaluated in terms of sharpness and coverage and processed by compression. The challenges and their proposed solutions for the data processing part of the mission are investigated through studies. Since most noise contributors are unknown until Milani is activated, different noises are studied based on previous missions and derived from hyperspectral images taken in a laboratory environment mimicking the real-life situation. The hyperspectral camera technology in the laboratory is similar to the one used in the ASPECT imager payload. Both ASPECT and the imagers utilized in our measurements are based on employing a Fabry-Pérot interferometer as an adjustable transmission filter. The imagers are also designed and built by the same party, the Technical Research Centre of Finland (VTT). Best performing denoising techniques for each noise type are discussed on the one hand for the entire datacubes and on the other hand for the spatial domain only since the mission includes images taken only at specific wavebands. The advantage of applying denoising for the whole datacube comes from the internal dependencies between the wavebands, allowing efficient processing. A trade-off study for several noise reduction algorithms is presented. The goal is to implement efficient image processing algorithms with low computational complexity, securing the successful execution of the mission.