Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Adoption of Responsible Research and Innovation in Citizen Observatories

An ethos of Responsible Research and Innovation (RRI) has been promoted in the last decade, especially within European research. The broad objective is to ensure that research and innovation activities align with society’s needs and expectations. In parallel, citizen observatories seek to mainstream citizen science as a valid paradigm for scientific investigation but additionally as a model for increasing societal participation in local democracy and policy definition. This paper explores how precepts of RRI have permeated research in citizen observatories. The methodology adopted is that of a scoping review. Results confirm a relatively simple adoption of RRI principles. However, the adoption is uneven and shallow, perhaps reflecting the ongoing evolution of both RRI and the citizen observatory model. It is recommended that the diverse actors charged with the definition, design, validation, and deployment of citizen observatories unambiguously integrate, promote, and report on how the RRI principles are reflected in their activities.

Data Fusion in Earth Observation and the Role of Citizen as a Sensor: A Scoping Review of Applications, Methods and Future Trends

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.