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The focus of study is to build near-IR capability onto Si CCDs for Time-Delay-Integration (TDI) applications, with the main requirement being charge domain summation, which allows the lowest noise performance on these types of devices. Currently there is no product that exists to do this. Si CCDs are a well-established technology which, whilst being an “old” technology, are still world leading in certain applications up to a wavelength of ~1μm.

The “Detection of Ocean Litter Plastics with Hyper-to-multispectral Infrared Neural Networks” (DOLPHINN) study explored a novel image processing technique to improve the use of space assets to map the distribution of plastic across the globe. The DOLPHINN project objective was to determine the feasibility of using a hyperspectral-to-multispectral association machine learning approach to improve the capability of space-based short wave infrared (SWIR) multispectral assets to detect, quantify, and track plastic litter in waters and on shorelines.

Increasingly demanding space-based applications require a large primary mirror diameter, on which the optical instrument resolution depends. However, the mass and volume of a monolithic primary mirror would dramatically increase along with its size, which, in addition to cost and technical issues, is also limited by the available volumen in the launch vehicle fairing.

Earth observation by satellites provides essential datasets for a wide range of commercial, societal and scientific applications. Satellites have been observing the state of the environment since the launch of the radiation balance instrument on Explorer 8 in October 1959, and over the last 60 years the range of observation types and applications has grown exponentially. Today operational long-term sustained Earth observation programmes, exemplified by the Copernicus Sentinels, give a wide range of users reliable environmental information services.