# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 1 Introduction  
Remote sensing data are critical to the scientific community's investigations of the state of the Earth, the natural processes surrounding us, and the impacts of human activity. The dynamics of physical, geological, chemical, and biological processes have been productively explored through the collection of spectral information with high resolution. However, the temporal and spatial extent of coverage has been quite limited. Several questionscan be unraveled by collecting more continuous measurements of the visible and Near-Infrared (NIR) regions of the electromagnetic spectrum [1; 2; 3; 4] in areas of interest. Dynamic oceanographic events such as phytoplankton blooms, including Harmful Algal Blooms (HABs), or characterization of riverine plumes rich in Colored Dissolved Organic Matter (CDOM) for water quality monitoring can favorably be observed through remote sensing from space due to their large extent and dynamic nature [5; 6; 7]. These, and other ocean color phenomena, can be mapped using optical instruments, since both visible and NIR wavelengths interact significantly with optically active constituents in the water column. The retrieval of bio-geophysical parameters, such as chlorophyll-a concentration, is a primary objective of ocean color remote sensing. However, it is complicated by the optical path from the ocean surface to the sensor. Only a tiny portion of the light measured by the satellite sensor comes from below the ocean surface. Commonly, less than 20% of the total signal [7; 8; 9] originates from the ocean. In addition, the signal is obscured by sea-surface reflections and atmospheric effects. In coastal environments, the water column usually includes dissolved and suspended matter. In shallow areas, the reflected light or signal from the bottom may be detectable, in addition to adjacency effects from light reflected by nearby land. These factors complicate the retrieval of the ocean color parameters related to biological indicators [10]. Scientific objectives set strict requirements for the radiometric performance of optical ocean color remote sensing instruments in order to ensure that the desired environmental parameters can be detected [11]. Examples of such requirements include:  
* A Signal-to-Noise Ratio (SNR) above 200 in the VIS-NIR wavelengths eases parameter retrieval in ocean color applications due to the relatively weak returned light signal [8]. However, promising parameter retrieval has still been achieved with poorer noise characteristics [7];
* A finer spectral resolution better resolves constituents in the water [1; 7; 8; 12; 13]. To identify phytoplankton functional types, a resolution of 5 nm is required [11];
* A collection of spectral bands with no more than 5 nm band-pass is desirable to resolve the spectral features of interest in ocean scenes [6; 7; 8; 12; 14]. These values are crucial when defining system requirements for an ocean-color remote sensing instrument. For smaller water systems, e.g., lakes, the ability to resolve spatial features becomes relevant as well [1; 7], and can pose a challenging trade-off between spatial and spectral resolution, as well as spectral range, to mention a few. With these scientifically motivated requirements in mind, and the Goal of making oceanographic observations in support of marine research, the HYPerspectral Small satellite for ocean Observation (HYPSO) mission was developed at Norwegian University of Science and Technology (NTNU) [5]. The HYPSO mission will consist of several small satellites in sun-synchronous Low-Earth Orbit (LEO), starting with the first, the CubeSat HYPSO-1. This satellite program is part of a significant effort towards oceanographic data collection at NTNU Centre for Autonomous Marine Operations and Systems (AMOS), supporting autonomous agents such as Unmanned Aerial Vehicles (UAVs), Autonomous Surface Vehicles (ASVs), Autonomous Underwater Vehicles (AUVs) and buoys in their missions to collect data for ocean-related research.