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Recent advances in Earth Observation (EO) placed Citizen Science (CS) in the highest position, declaring their essential provision of information in every discipline that serves the SDGs, and the 2050 climate neutrality targets. However, so far, none of the published literature reviews has investigated the models and tools that assimilate these data sources. Following this gap of knowledge, we synthesised this scoping systematic literature review (SSLR) with a will to cover this limitation and highlight the benefits and the future directions that remain uncovered. Adopting the SSLR guidelines, a double and two-level screening hybrid process found 66 articles to meet the eligibility criteria, presenting methods, where data were fused and evaluated regarding their performance, scalability level and computational efficiency. Subsequent reference is given on EO-data, their corresponding conversions, the citizens' participation digital tools, and Data Fusion (DF) models that are predominately exploited. Preliminary results showcased a preference in the multispectral satellite sensors, with the microwave sensors to be used as a supplementary data source. Approaches such as the "brute-force approach" and the super-resolution models indicate an effective way to overcome the spatio-temporal gaps and the so far reliance on commercial satellite sensors. Passive crowdsensing observations are foreseen to gain a greater audience as, described in, most cases as a low-cost and easily applicable solution even in the unprecedented COVID-19 pandemic. Immersive platforms and decentralised systems should have a vital role in citizens' engagement and training process. Reviewing the DF models, the majority of the selected articles followed a data-driven method with the traditional algorithms to still hold significant attention. An exception is revealed in the smaller-scale studies, which showed a preference for deep learning models. Several studies enhanced their methods with the active-, and transfer-learning approaches, constructing a scalable model. In the end, we strongly support that the interaction with citizens is of paramount importance to achieve a climate-neutral Earth. [ABSTRACT FROM AUTHOR]

The monitoring of water ecosystems requires consistent and accurate sensor measurements, usually provided from traditional in-situ environmental monitoring systems. Such infrastructure, however, is expensive, hard to maintain and available only in limited areas that had been affected by extreme phenomena and require continuous monitoring. Due to climate change, the monitoring of larger areas and extended water ecosystems is imperative, raising the question of whether this monitoring can be disengaged from the in-situ monitoring systems. Due to climate change and extreme weather phenomena, more citizens are affected by environmental issues and become aware of the need to contribute to their monitoring. As a result, they are willing to offer their time to support the collection of scientific data. Collecting such data from volunteers, with no technical knowledge and while using low-cost equipment such as smart phones and portable sensors, raises the question of data quality and consistency. We present here a novel integrated toolbox that can support the organization of crowd-sourcing activities, ensure the engagement of the participants, the data collection in a consistent way, enforce extensive data quality controls and provide to local authorities and scientists access to the collected information in a uniform way, through widely accepted standards. [ABSTRACT FROM AUTHOR]