Remote sensing technologies have developed over the last few decades since the first Earth observation satellite. Remote sensing plays a huge role in the understanding of the Earth’s ecosystems, for global climate change research, in assessing the interactions of humans and the environment, etc. This is of particular importance in understanding changes such as global climate change, environmental pollution, air pollution, changes to the carbon cycle, the destruction of forests, melting sea ice, and natural disasters. Long timeseries remote sensing data is a challenge for data processing and analysis yet enriches the data resources available for understanding earth system processes and climate change on longer timescales. Data mining of remote sensing data is important and new modern techniques, such as the development of Artificial Intelligence and geoscience language models, provide a new perspective for remote sensing data analysis.
This Research Topic mainly focuses on new theories and methods for using Artificial Intelligence and big data on remote sensing datasets for: e.g., carbon storage estimation, understanding the dynamics of the carbon cycle, forest surveys and monitoring, biodiversity monitoring, evaluating the functioning of forest ecosystems, fire forecasting and warning, pest and disease monitoring, assessing the dynamics of sea ice at the poles, monitoring permafrost regions, etc. We also encourage papers on the technologies of big data mining of remotely sensed long timeseries data.
Potential topics of interest include, but are not limited to:
• Theories, methods, and algorithms of artificial intelligence for quantitative remote sensing
• Application of artificial intelligence and big data technologies to monitoring the Earth's ecosystems
• Long timeseries remote sensing data mining for Earth processes
• Carbon cycle modeling and climate change research using remote sensing
• New remote sensing platforms or sensors for Earth ecosystems research
• Geoscience language models for earth science research
• Application of remote sensing observations
• Environmental monitoring and catastrophe analysis, forecasting and warning
Keywords: Artificial Intelligence, Big Data, Quantitative Remote Sensing, Climate Changes, Carbon Cycle, Modeling and Simulation, Digital Twins
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
