Spatial Data for a Sustainable World: Remote Sensing in Geoscientific Research

Dear Colleagues,

In an era of accelerating environmental change and intensifying anthropogenic pressures, the demand for accurate, timely, and multi-scale observations of Earth systems has become increasingly critical. Spatial data form the backbone of contemporary geographical and geoscientific research, enabling systematic investigation of environmental processes, landscape dynamics, and complex human–environment interactions. Among these data sources, remote sensing plays a pivotal role by providing consistent, repeatable, and synoptic observations across terrestrial, marine, atmospheric, and cryospheric domains.

Rapid advances in sensor technologies and data acquisition platforms have substantially expanded both the scope and resolution of spatial data. High-resolution geophysical imaging, synthetic aperture radar (SAR), hyperspectral and thermal sensors, and airborne and terrestrial lidar systems now offer unprecedented insights into surface and subsurface processes. These diverse data streams support integrated analyses that deepen our understanding of ecosystem functioning, land-use and land-cover change, hydrological dynamics, urbanization, and climate-related hazards. In parallel, remote sensing has become increasingly central to the exploration, assessment, and monitoring of energy resources, including renewable energy potential mapping, geothermal exploration, subsurface characterization, and the environmental impacts of energy infrastructure.

As societies pursue pathways toward decarbonization and sustainable development, spatial data and remote sensing are playing a critical role in supporting the global energy transition. Remote sensing at different temporal and spatial scales enables monitoring of renewable resources, biomass dynamics, and hydrologic systems, as well as the detection of emissions, land disturbance, and environmental risks associated with energy exploitation. These capabilities are essential for evaluating energy system performance, supporting responsible resource management, and informing evidence-based policy decisions.

At the same time, the growing volume, velocity, and variety of spatial data present significant methodological and computational challenges. Effective exploitation of these datasets requires advanced analytical approaches, including geospatial and geophysical modelling, machine learning, and multi-source data fusion, often implemented within cloud-based and high-performance computing environments. The integration of remotely sensed data within in situ measurements and socio-environmental information is fostering new interdisciplinary research frameworks and reshaping the practice of geographical inquiry.

This Special Issue, titled “Spatial Data for a Sustainable World: Remote Sensing in Geoscientific Research”, will bring together contributions that highlight methodological innovations, emerging applications, and conceptual advances in the use of spatial data for surface and subsurface analysis and monitoring. By showcasing cutting-edge research across environmental, climatic, and energy-related domains, this collection aims to strengthen the links between spatial data science, sustainability science, and energy transition studies, and to inspire continued innovation in geoscientific research.

Dr. Maurizio Milano
Guest Editor

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• remote sensing
• spatial data science
• environmental sustainability
• energy transition and monitoring
• geospatial modelling