Guliyeva et al. (2026) Geospatial Technologies for Flood and Drought Management: A Review of Earth Observation Data, Procedures, and their Operational Effectiveness

Identification

• Journal: Aerotecnica Missili & Spazio
• Year: 2026
• Date: 2026-02-02
• Authors: Sona Guliyeva, Piero Boccardo
• DOI: 10.1007/s42496-026-00309-4

Research Groups

• SDG11lab, Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Italy

Short Summary

This review synthesizes the current state of Earth Observation (EO) data and procedures for operational flood and drought management, introducing the Operational EO Integration Framework (OEI-F) to systematically align EO data types, integration approaches, spatial scales, and response substages. It highlights the pivotal role of EO in strengthening climate adaptation and multi-hazard resilience while identifying persistent challenges and offering strategic recommendations.

Objective

• To determine how Earth Observation (EO) datasets and procedures can be operationally integrated to support different phases of flood and drought response, and what impact they have across spatial scales and decision-making contexts.
• Catalogue the principal EO datasets and procedures.
• Assess their operational efficacy across different phases of response.
• Identify key technical, organizational, and policy gaps.
• Evaluate integrative approaches versus single-sensor or traditional methods.
• Recommend actions to strengthen multi-hazard use, interoperability, and institutional uptake.

Study Configuration

• Spatial Scale: Global, regional, national, local, basin-scale, urban, field-level, building-level (ranging from sub-meter to tens of kilometers).
• Temporal Scale: Daily, near-real-time, sub-daily, multi-day, weekly, monthly, seasonal, long-term (spanning from hours to years).

Methodology and Data

• Models used: Random Forest, Convolutional Neural Networks (CNN), transformers, Extreme Gradient Boosting (XGBoost), LISFLOOD, Global Land Data Assimilation System (GLDAS), HEC-RAS, various machine learning and deep learning algorithms.
• Data sources:
  • Satellite: Optical (MODIS, AVHRR, VIIRS, Landsat 7/8/9, Sentinel-2, PlanetScope, Pléiades, Gaofen-1), Thermal Infrared (MODIS, VIIRS, Sentinel-3 SLSTR, Landsat TIRS, ASTER TIR, ECOSTRESS, GOES), Active Microwave (Sentinel-1, RADARSAT-2, TerraSAR-X, COSMO-SkyMed, ICEYE), Passive Microwave (SMAP, SMOS, AMSR-2, GPM, ASCAT, GRACE/GRACE-FO), Laser Altimetry (ICESat-2, GEDI), Radar Altimetry (SWOT).
  • Airborne/Ground-based: Unmanned Aerial Vehicle (UAV) imagery and LiDAR, airborne LiDAR, mobile LiDAR systems (MMS), in-situ soil moisture networks, rain gauges, weather radars.
  • Reanalysis: ERA5.
  • Databases: Scopus, Web of Science, Emergency Events Database (EM-DAT).

Main Results

• The Operational EO Integration Framework (OEI-F) was developed, systematically aligning EO data types, integration approaches, spatial scales, and response substages for effective multi-hazard management.
• EO data from optical, thermal infrared, active/passive microwave, and LiDAR sensors provide complementary information for monitoring hydrological and environmental variables across all disaster response phases (early situation assessment, emergency decision support, damage assessment, and recovery planning).
• Multi-source data fusion and advanced analytics (e.g., machine learning, deep learning) significantly enhance the timeliness, accuracy, and robustness of flood and drought monitoring, often outperforming single-sensor approaches.
• EO-derived indicators (hydrological, biophysical, meteorological) have evolved from single-variable metrics to composite and predictive indices, supporting early warning and impact assessment.
• Global, regional, and national/local EO services demonstrate complementary roles, transforming large data volumes into timely, decision-ready information for various operational needs.
• Persistent challenges include data interoperability, institutional uptake, uncertainty quantification in AI models, and the need for integrated monitoring of compound hazards like flash floods and flash droughts.

Contributions

• Introduction of the Operational EO Integration Framework (OEI-F), a novel, explicitly operational, and multi-hazard framework that systematically aligns EO data families, integration levels, spatial scales, and response substages, addressing fragmentation in existing literature.
• Comprehensive synthesis of the current state of EO-based flood and drought management, focusing on operational integration across different response phases and spatial scales, unlike previous reviews that often focused on individual hazards or sensor types.
• Identification of key technical, organizational, and policy gaps hindering the full operational exploitation of EO, and provision of strategic recommendations to enhance its effectiveness.
• Evaluation of multi-source data fusion, EO-derived indicators, and service-oriented platforms, highlighting their advancements and limitations in supporting climate adaptation and multi-hazard resilience.

Funding

• Italian Ministry of University and Research (MUR) (PhD scholarship grant no. 4652, Program I.4.1 "PNRR Public Administration", Cycle 39)
• Italian Space Agency (ASI) and MUR (Space It Up project, contract n. 2024-5-E.0 – CUP n. I53D24000060005)

Citation

@article{Guliyeva2026Geospatial,
  author = {Guliyeva, Sona and Boccardo, Piero},
  title = {Geospatial Technologies for Flood and Drought Management: A Review of Earth Observation Data, Procedures, and their Operational Effectiveness},
  journal = {Aerotecnica Missili & Spazio},
  year = {2026},
  doi = {10.1007/s42496-026-00309-4},
  url = {https://doi.org/10.1007/s42496-026-00309-4}
}