The HYPSO-1 satellite, a 6U CubeSat carrying a hyperspectral imager, was launched on 13 January 2022, with the Goal of imaging ocean color in support of marine research. This article describes the development and current status of the mission and payload operations, including examples of agile planning, captures with low revisit time and time series acquired during a campaign. The in-orbit performance of the hyperspectral instrument is also characterized. The usable spectral range of the instrument is in the range of 430 nm to 800 nm over 120 bands after binning during nominal captures. The spatial resolvability is found empirically to be below 2.2 pixels in terms of Full-Width at Half-Maximum (FWHM) at 565 nm. This measure corresponds to an inherent ground resolvable resolution of 142 m across-track for close to nadir capture. In the across-track direction, there are 1216 pixels available, which gives a swath width of 70 km. However, the 684 center pixels are used for nominal captures. With the nominal pixels used in the across-track direction, the nadir swath-width is 40 km. The spectral resolution in terms of FWHM is estimated to be close to 5 nm at the center wavelength of 600 nm, and the Signal-to-Noise Ratio (SNR) is evaluated to be greater than 300 at 450 nm to 500 nm for Top-of-Atmosphere (ToA) signals. Examples of images from the first months of operations are also shown.

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

Space environment with changing temperatures and vacuum can affect the performance of optics instruments onboard satellites. Thermal models and tests are typically done to understand the optics performance within large space projects, but less often in nanosatellites projects. It is even more rarer for an optics payload inside a CubeSat platform, made by a third provider, to do functional tests on their optics during space environment test campaign. In this research, an in-house made vacuum chamber with the possibility to warm up (TVAC) the devices under tests, and wall-through transparency for optics experiments is set-up. In parallel, a thermal model of the HYPerspectral Small satellite for ocean Observation (HYPSO) Hyperspectral Imager (HSI) is developed. The HSI, which is a transmissive grating hyperspectral instrument ranged in the visible to near infrared wavelength, has been tested in TVAC. As thermal control is based on heating the device under test, a new method for fitting the thermal models inside vacuum chambers with only heating capability is proposed. Finally, the TVAC set-up and the thermal model fitting method have been demonstrated to be appropriate to validate the HSI thermal model, and to characterize the optics performance of HSI in vacuum and in the range of temperatures found inside the in-orbit HYPSO-1 CubeSat.

Monitoring ocean color is a crucial tool in understanding marine ecosystems and their health, as it provides quantitative information on chlorophyll-a (chla) concentration, absorption by colored dissolved organic matter (CDOM). Ocean color data can be used to characterize phytoplankton pigment groups from the ocean, by using a few color bands (multispectral imaging). Hyperspectral sensors have the potential to distinguish pigment groups in more detail. This capability is necessary for detailed monitoring of local ecosystems and improving predictions of algae blooms. As part of the European Copernicus Programme, the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) will provide routine hyperspectral observations globally over land and coastal zones in support to European Union policies for the management of natural resources, ecosystem services and societal benefits. CHIME primarily focuses on land applications, however, secondary applications include, among others, detection of floating debris and water quality monitoring in inland and coastal waters.Radiative transfer modelling (RTM) is a useful tool for determining the expected level of detail in which phytoplankton pigment groups can be distinguished with a new observing system (e.g. satellite mission). The forward RTM simulation uses assumed absorption and scattering properties of phytoplankton in each depth layer to calculate light progression and the reflectance spectrum. Since these properties are difficult to determine and vary with factors such as depth [1] and the community that the pigment groups appear in [2], it can lead to inaccurate model output.During an observation campaign in September 2024, we sampled at two different locations in Trondheimsfjorden: one located in the middle of the fjord, to compare results with satellite data while minimizing land-mixing, and the other located at the critical Gaula river outlet, which is important for sediment and nutrient transport into the fjord. We measured among others phytoplankton accessory pigments and taxonomy, CDOM, the surface reflectance spectrum, and the downwelling irradiance in the water column. This data allows us to compare the model output to the observation while we tune absorption and scattering properties. An optimization process lets us tune them to minimize the deviation in the output, thereby resulting in estimates of absorption and scattering properties.The forward RTM exercise was set up to simulate the reflectance of the water surface and the downwelling irradiance in the water column. Measured chla and CDOM concentrations were used as inputs. In the optimization exercise, the sum of squares of the difference between the measured vertical downwelling irradiance profile and the simulated vertical downwelling irradiance profile is minimized, by tuning the absorption and scattering coefficients. For testing, the measured reflectance spectrum and RTM simulated reflectance spectrum were compared.The final absorption and scattering coefficients were compared to measured absorption properties [3] and simulated absorption and scattering properties [2]. These simulations can better determine the sensitivity of future hyperspectral missions (e.g. CHIME) to distinguishing phytoplankton pigment groups.[1] Sundarabalan et al., 2013. Journal of Quantitative Spectroscopy and Radiative Transfer. https://doi.org/10.1016/j.jqsrt.2013.01.016[2] Lain et al., 2023. Scientific Data. https://doi.org/10.1038/s41597-023-02310-z [3] Johnsen et al., 2007. Journal of Phycology. https://doi.org/10.1111/j.1529-8817.2007.00422.x