Monitoring water contaminants is of paramount importance, ensuring public health and environmental well-being. Turbidity, a key parameter, poses a significant problem, affecting water quality. Its accurate assessment is crucial for safeguarding ecosystems and human consumption, demanding meticulous attention and action. For this, our study pioneers a novel approach to monitor the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with high-resolution data from Sentinel-2 Level-2A. Traditional methods are labor-intensive while CatBoost offers an efficient solution, excelling in predictive accuracy. Leveraging atmospherically corrected Sentinel-2 data through the Google Earth Engine (GEE), our study contributes to scalable and precise Turbidity monitoring. A specific tabular dataset derived from Hong Kong contaminants monitoring stations enriches our study, providing region-specific insights. Results showcase the viability of this integrated approach, laying the foundation for adopting advanced techniques in global water quality management.

The next generation of spacecraft technology is anticipated to enable novel applications, including onboard processing, machine learning, and decentralized operational scenarios. Although several of these applications have been previously investigated, the real-world operational limitations associated with actual mission scenarios have been only superficially addressed. Here, we present an open-source Python module called PASEOS, capable of modeling operational scenarios involving one or multiple spacecraft. It considers several physical phenomena, including thermal, power, bandwidth, and communications constraints, and the impact of radiation on spacecraft. PASEOS can be run as a high-performance-oriented numerical simulation and/or in a real-time mode on edge hardware. We demonstrate these capabilities in three scenarios: one in real-time simulation on a Unibap iX-10 100 satellite processor, another in a simulation modeling an entire constellation performing tasks over several hours, and one training a machine learning model in a decentralized setting. While we demonstrate tasks in Earth orbit, PASEOS also allows deep space scenarios. Our results show that PASEOS can model the described scenarios efficiently and thus provide insight into operational considerations. We show this by measuring runtime and overhead as well as by investigating the constellation's modeled temperature, battery status, and communication windows. By running PASEOS on an actual satellite processor, we showcase how PASEOS can be directly included in hardware demonstrators for future missions. Overall, we provide the first solution to holistically model the physical constraints spacecraft encounter in space. The PASEOS module is available online with extensive documentation, enabling researchers to incorporate it into their studies quickly.

Satellite data transmission is a crucial bottleneck for Earth observation applications. To overcome this problem, we propose a novel solution that trains a neural network on board multiple satellites to compress raw data and only send down heavily compressed previews of the images while retaining the possibility of sending down selected losslessly compressed data. The neural network learns to encode and decode the data in an unsupervised fashion using distributed machine learning. By simulating and optimizing the learning process under realistic constraints such as thermal, power and communication limitations, we demonstrate the feasibility and effectiveness of our approach. For this, we model a constellation of three satellites in a Sun-synchronous orbit. We use real raw, multispectral data from Sentinel-2 and demonstrate the feasibility on space-proven hardware for the training. Our compression method outperforms JPEG compression on different image metrics, achieving better compression ratios and image quality. We report key performance indicators of our method, such as image quality, compression ratio and benchmark training time on a Unibap iX10-100 processor. Our method has the potential to significantly increase the amount of satellite data collected that would typically be discarded (e.g., over oceans) and can potentially be extended to other applications even outside Earth observation. All code and data of the method are available online to enable rapid application of this approach.

While novel artificial intelligence and machine learning techniques are evolving and disrupting established terrestrial technologies at an unprecedented speed, their adaptation onboard satellites is seemingly lagging. A major hindrance in this regard is the need for high-quality annotated data for training such systems, which makes the development process of machine learning solutions costly, time-consuming, and inefficient. This paper presents “the OPS-SAT case”, a novel data-centric competition that seeks to address these challenges. The powerful computational capabilities of the European Space Agency’s OPS-SAT satellite are utilized to showcase the design of machine learning systems for space by using only the small amount of available labeled data, relying on the widely adopted and freely available open-source software. The generation of a suitable dataset, design and evaluation of a public data-centric competition, and results of an onboard experimental campaign by using the competition winners’ machine learning model directly on OPS-SAT are detailed. The results indicate that adoption of open standards and deployment of advanced data augmentation techniques can retrieve meaningful onboard results comparatively quickly, simplifying and expediting an otherwise prolonged development period.