Scope

The Data Fusion and Assimilation section of Frontiers in Remote Sensing will publish high-quality research across all aspects of spatial data fusion and assimilation techniques and applications based on various sources of spatial data (remotely sensed, field data, or synthesised data) that vary in spatial resolution, spatio-temporal coverage, sensor origins, and perspectives. The section focuses on the fusion and assimilation of data, both statistical and machine learning (including traditional classical based methods and recent deep learning techniques) methods.

Areas covered by this journal shall include, but are not limited to:

• Data Fusion

• Data Assimilation

• Data Sciences

• Data Mining

• Explainable Artificial Intelligence (XAI)

• Machine Learning

• Artificial Intelligence for Data Fusion or Data Assimilation

• Spatial Data Analysis

• Spatial Data Modelling

• Sensors Fusion

• Data Fusion Applications: Urban, Agriculture, Forestry, Hydrology, Natural Hazards, Water Resources, Natural Resources, etc.

• Data Assimilation Application: Climate, Health, Atmosphere, Ocean, Environment, etc.

• Data Visualizations Techniques for Data Fusion

Research submitted to this section must provide insights into data fusion or data assimilation, technology or applications. Fundamental works, methodological advances and application-oriented studies are all welcome. Review or summary articles on established or upcoming data fusion / assimilation techniques may be accepted if they demonstrate academic rigor and relevance. Reports that do not deal with spatial data or methods/applications do not fall within the scope of this section and should be submitted to more specialized journals.

Submission

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