GeoAI for Environmental Monitoring

The increasing availability of geospatial (GEO) data, coupled with advancements in artificial intelligence (AI) has revolutionized environmental monitoring and biodiversity conservation. Long-term satellite missions (e.g., Landsat, MODIS, Resourcesat) and new-generation sensors such as hyperspectral, LiDAR, and microwave remote sensing provide unprecedented opportunities for high-resolution spatial and temporal analyses. AI-driven techniques, including deep learning and data fusion/assimilation approaches, have enhanced the accuracy of land, water, and vegetation monitoring, making geoinformatics a critical tool in environmental sciences. The growing role of commercial satellite providers, open-access data platforms and unmanned aerial vehicle (UAV) imaging has further transformed remote sensing applications. As AI continues to evolve, integrating remote sensing with intelligent algorithms presents novel opportunities for natural resource management, climate change mitigation, biodiversity conservation, and hydrological modelling.

This Research Topic welcomes original contributions at the intersection of remote sensing, artificial intelligence (AI), and environmental monitoring. The focus is on integrating AI techniques—such as machine learning (ML), deep learning (DL), and data fusion with satellite and airborne remote sensing for improved monitoring of land, water, and vegetation dynamics. We welcome articles addressing pressing challenges, including high-dimensional data processing, model generalization across diverse ecosystems, and real-time predictive analytics.

This Research Topic welcomes research spanning regional to global scales, emphasizing practical applications in ecosystem resilience, sustainable development, and extreme weather event monitoring. We particularly encourage articles that validate AI models using field data, UAV-based sensing, and high-resolution satellite imagery. By fostering interdisciplinary collaborations, this Research Topic aims to advance AI-driven geoinformatics for climate action, biodiversity conservation, and precision environmental monitoring.

Focused specifically on environmental applications through GeoAI-integrated remote sensing datasets, we welcome submissions spanning multiple scales and ecosystems, supporting practical advancements in ecological assessments and conservation practices. We particularly encourage theoretical, methodological, and applied contributions addressing, though not limited to, these core themes:

• AI-Driven Carbon and Biomass Monitoring: Advanced biomass estimation, carbon flux modeling, and ecosystem productivity assessment.

• Phenology and Climate Dynamics: Long-term vegetation phenology modeling, drought impact assessment, and forecasting of extreme climate events.

• Data Fusion and AI Innovations: Multi-sensor integration (optical, SAR, LiDAR), explainable AI, and real-time environmental monitoring.

• Smart Agroforestry Management: AI tools for real-time monitoring, pest control, and resource optimisation in agroforestry systems

We encourage contributions that clearly articulate data sources, methodology, and scalability, ensuring reproducibility and transparency in AI-assisted research. Authors should adhere to ethical AI guidelines and best practices in geospatial data analysis.

The editors of this issue come with more than 6 to 15 years of research experience in land system studies, conducting studies from Asia, Europe, and the United States.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence (AI), Environmental Sciences, Earth Sciences, Remote Sensing, Drone, geoai

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.