This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system’s capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement.

Community and citizen science on climate change-influenced topics offers a way for participants to actively engage in understanding the changes and documenting the impacts. As in broader climate change education, a focus on the negative impacts can often leave participants feeling a sense of powerlessness. In large scale projects where participation is primarily limited to data collection, it is often difficult for volunteers to see how the data can inform decision making that can help create a positive future. In this paper, we propose and test a method of linking community and citizen science engagement to thinking about and planning for the future through scenarios story development using the data collected by the volunteers. We used a youth focused wild berry monitoring program that spanned urban and rural Alaska to test this method across diverse age levels and learning settings. Using qualitative analysis of educator interviews and youth work samples, we found that using a scenario stories development mini-workshop allowed the youth to use their own data and the data from other sites to imagine the future and possible actions to sustain berry resources for their communities. This process allowed youth to exercise key cognitive skills for sustainability, including systems thinking, futures thinking, and strategic thinking. The analysis suggested that youth would benefit from further practicing the skill of envisioning oneself as an agent of change in the environment. Educators valued working with lead scientists on the project and the experience for youth to participate in the interdisciplinary program. They also identified the combination of the berry data collection, analysis and scenarios stories activities as a teaching practice that allowed the youth to situate their citizen science participation in a personal, local and cultural context. The majority of the youth groups pursued some level of stewardship action following the activity. The most common actions included collecting additional years of berry data, communicating results to a broader community, and joining other community and citizen science projects. A few groups actually pursued solutions illustrated in the scenario stories. The pairing of community and citizen science with scenario stories development provides a promising method to connect data to action for a sustainable and resilient future.

Youth-focused community and citizen science (CCS) is increasingly used to promote science learning and to increase the accessibility of the tools of scientific research among historically marginalized and underserved communities. CCS projects are frequently categorized according to their level of public participation and their distribution of power between professional scientists and participants from collaborative and co-created projects to projects where participants have limited roles within the science process. In this study, we examined how two different CCS models, a contributory design and a co-created design, influenced science self-efficacy and science interest among youth CCS participants. We administered surveys and conducted post-program interviews with youth participation in two different CCS projects in Alaska, the Winterberry Project and Fresh Eyes on Ice, each with a contributory and a co-created model. We found that youth participating in co-created CCS projects reflected more often on their science self-efficacy than did youth in contributory projects. The CCS program model did not influence youths’ science interest, which grew after participating in both contributory and co-created projects. Our findings suggest that when youth have more power and agency to make decisions in the science process, as in co-created projects, they have greater confidence in their abilities to conduct science. Further, participating in CCS projects excites and engages youth in science learning, regardless of the CCS program design.