Pandey et al. (2026) Editorial: Advanced geospatial data analytics for environmental sustainability: current practices and future prospects

Identification

• Journal: Frontiers in Remote Sensing
• Year: 2026
• Date: 2026-01-07
• Authors: Manish Pandey, Varun Mishra, Maya Kumari, Saeid Janizadeh, Romulus Dumitru Costache
• DOI: 10.3389/frsen.2025.1761905

Research Groups

• Marwadi University Research Center (MURC), Marwadi University, Rajkot, Gujarat, India
• Department of Civil Engineering, Faculty of Engineering and Technology, Marwadi University, Rajkot, Gujarat, India
• Amity Institute of Geoinformatics and Remote Sensing (AIGIRS), Amity University, Noida, India
• Amity School of Natural Resources and Sustainable Development (ASNRSD), Amity University, Noida, India
• Department of Civil, Environmental, and Construction Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, United States
• Faculty of Civil Engineering, Transilvania University of Braşov (UUNITBV), Braşov, Romania
• National Institute of Hydrology and Water Management, Bucharest, Romania
• Danube Delta National Institute for Research and Development, Tulcea, Romania

Short Summary

This editorial synthesizes current practices and future prospects of advanced geospatial data analytics for environmental sustainability, highlighting how integrated geospatial and computational methods address pressing environmental challenges and support Sustainable Development Goals. It categorizes current applications and introduces seven research papers from the associated Research Topic.

Objective

• To provide an overview of current practices and future prospects of advanced geospatial data analytics for environmental sustainability.
• To highlight how integrated geospatial and computational methods can strengthen environmental sustainability, reflecting a shift toward data-driven, interdisciplinary, and anticipatory approaches.

Study Configuration

• Spatial Scale: Discusses applications ranging from local (e.g., specific basins, cities, regional forests) to global scales, covering diverse environmental contexts.
• Temporal Scale: Examines current practices and projects future trends over the next decade, with examples of analyses spanning multiple years (e.g., 2001-2022).

Methodology and Data

• Models used: The editorial discusses various models and techniques employed in the field, including deep learning (TabNet, TabTransformer, Multilayer Perceptron), machine learning (CatBoost, AdaBoost, Random Forest, Support Vector Machines, CNN), multi-criteria analysis (AHP-GIS), ecosystem service modeling (InVEST), statistical regression (Geographically Weighted Regression), and technological frameworks (Fog Computing).
• Data sources: The editorial discusses data sources such as satellite remote sensing (Landsat, Sentinel, MODIS, Planet Labs multispectral imagery, Sentinel-1 SAR), Geographic Information Systems (GIS), LiDAR, Unmanned Aerial Vehicles (UAVs), in situ observations, Digital Elevation Models (DEMs), climate data, and citizen-science inputs.

Main Results

• Current geospatial data analytics for environmental sustainability are categorized into three major areas: monitoring via Earth Observation (EO), predictive modeling for risk management, and biodiversity and ecosystem assessment.
• EO is foundational for monitoring land use/land cover changes, biomass dynamics, water and soil resources, and agricultural parameters, utilizing various spectral indices (e.g., NDBI, NDVI, EVI, NDMI, dNBR, NDCI).
• Predictive modeling integrates EO-derived variables with machine learning and statistical models to assess environmental risks such as floods, droughts, wildfires, and landslides.
• EO technologies are central to biodiversity and ecosystem conservation, supporting habitat suitability models, wildlife monitoring, and ecosystem service mapping.
• The Research Topic compiles seven papers demonstrating cutting-edge applications across four themes: Geospatial Artificial Intelligence (GeoAI) for environmental modeling (e.g., groundwater recharge, soil moisture), remote sensing for ecosystem and hazard monitoring (e.g., wildfire hazard, harmful algal blooms), geospatial planning and sustainability assessment (e.g., waste disposal, habitat quality), and technological innovations in environmental sensing infrastructure (e.g., Fog Computing).
• Future directions emphasize the integration of Geospatial Big Data, AI, and real-time sensing systems to support Sustainable Development Goals, including global Explainable AI (XAI), edge intelligence for remote observatories, operational-scale modeling for smart cities and conservation, and the potential of quantum computing.

Contributions

• Provides a comprehensive synthesis and categorization of current practices in advanced geospatial data analytics for environmental sustainability, offering a structured overview of diverse applications and methodologies.
• Identifies key challenges in the field and proposes future research directions, including the integration of emerging technologies like GeoAI, Big Data, real-time sensing, Explainable AI (XAI), and Fog Computing, thereby guiding future interdisciplinary research and policy.
• Introduces and contextualizes seven cutting-edge research papers within the associated Research Topic, showcasing practical applications across various environmental domains.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Citation

@article{Pandey2026Editorial,
  author = {Pandey, Manish and Mishra, Varun and Kumari, Maya and Janizadeh, Saeid and Costache, Romulus Dumitru},
  title = {Editorial: Advanced geospatial data analytics for environmental sustainability: current practices and future prospects},
  journal = {Frontiers in Remote Sensing},
  year = {2026},
  doi = {10.3389/frsen.2025.1761905},
  url = {https://doi.org/10.3389/frsen.2025.1761905}
}