Skip to content
Review Open access

Floods From Space: How Remote Sensing, AI , and Cloud Platforms Are Reshaping Disaster Risk Reduction

Jul 2026 · Journal of Flood Risk Management · Vol 19 · 0 citations · 49 references

TL;DR

It is concluded that future flood resilience increasingly depends on the coordinated integration of space‐borne observations, advanced analytics, and operational decision‐support architectures.

Abstract

Flooding remains one of the most damaging climate‐related hazards globally, yet flood remote sensing has historically developed largely along sensor‐specific pathways. This review addresses the need for a systematic and decision‐oriented synthesis of how space‐borne remote sensing has evolved toward integrated flood monitoring and disaster risk reduction frameworks. Moving beyond conventional sensor inventories, the study examines sensor integration pathways, temporal evolution, and decision relevance. Four research questions guide the analysis: (RQ1) how space‐borne sensors have been applied and advanced in flood studies, (RQ2) how multi‐sensor fusion architectures have evolved, (RQ3) what limitations persist, and (RQ4) what future directions are emerging. Following PRISMA guidelines, 176 peer‐reviewed studies published between 2001 and 2024 were systematically analysed. Quantitative synthesis indicates a marked post‐2018 increase in multi‐sensor approaches, alongside a growing share of studies adopting fusion frameworks. Synthetic Aperture Radar (SAR)‐centred integration systems represent a dominant share of recent applications; most commonly combined with precipitation products and digital elevation models. Analytical synthesis further indicates that SAR plays a central role in flood detection, while precipitation and topographic data provide key hydrological drivers and terrain constraints. These findings indicate a transition from static flood mapping toward process‐aware, multi‐sensor, and algorithm‐driven flood intelligence systems. The review also highlights the growing role of artificial intelligence and cloud‐based platforms in enabling scalable, near‐real‐time flood analysis. It concludes that future flood resilience increasingly depends on the coordinated integration of space‐borne observations, advanced analytics, and operational decision‐support architectures. This synthesis provides a unifying framework to guide next‐generation flood monitoring and disaster risk reduction under accelerating climate change.

Read PDF

Similar papers

Open access Sep 2026

Comparative Analysis of Urban Flood Mapping Using Sentinel‐1 SAR Imagery and Machine Learning

Combining temporal change‐detection signals with landscape features using ML improved flood‐mapping performance, with average F1 scores of about 0.75 and a range of 0.58 in dense urban areas to 0.93 in peri‐urban regions.

M. A. Al Mehedi, Virginia Smith, Peleg Kremer · 0 citations
Case report Aug 2026

Hydrologic Science Opportunities to Better Understand Floods: A Community Perspective

We synthesize community priorities for advancing flood science and flood decision support over the next decade. We argue that useful information for flood monitoring and forecasting require integrated observations and models that quantify where water is, how much is stored, how fast it moves, how watershed conditions p...

A. Getirana, P. Passalacqua, C. David · 0 citations
Open access Sep 2026

Toward Climate‐Resilient Societies: Remote Sensing and GIS for Adaptation and Mitigation

The global climate crisis has transcended theoretical prediction to become a present reality, with developing nations positioned at the devastating forefront of its impacts. Among these, Pakistan stands as a critical case study; despite contributing less than 1% of global greenhouse gas emissions, it consistently ranks...

Kayoko Yamamoto · 0 citations
Open access Sep 2026

பனிப்பாறை சரிவால் ஏற்படும் திடீர் வெள்ளங்களுக்கு AI அடிப்படையிலான பல்பேரிடர் முன்னெச்சரிக்கை கட்டமைப்பு: 2026 நேபாள பேரிடரை மையமாகக் கொண்ட Remote Sensing மற்றும் Digital Twin ஆய்வு

This framework combines risk prediction, GIS risk mapping, and early warning layers by using Transformer/LSTM deep learning models to integrate six distinct data sources to address rising Glacial Lake Outburst Floods and avalanche-induced flash floods in the climate-sensitive Himalayas.

C.Sakthimurugan, D. Mahalingam, K. Santhanam et al. · 0 citations
Open access Aug 2026

AIoT-enabled urban platform for flood detection and impact mapping: towards near-real-time spatial decision support in disaster management

Flooding is one of the most pervasive and destructive natural hazards, with its frequency and intensity expected to worsen under climate change. While advances in geospatial analytics, Internet of Things infrastructures, and artificial intelligence have enhanced urban data ecosystems, existing smart city platforms rema...

Sk Tahsin Hossain, Tan Yigitcanlar, Zhao-Hui Lin et al. · 0 citations
Open access Sep 2026

Remote Sensing Hydrology Revisited: Advances, Persistent Challenges, and a Science‐Driven Agenda for the Next Decade

In the WRR 50th Anniversary Special Collection, Lettenmaier et al. (2015, https://doi.org/10.1002/2015WR017616) provided a timely account of how satellite remote sensing evolved from a niche capability in hydrology into a mainstream driver of hydrologic discovery, as the community shifted from imagery toward validated...

Yang Hong, D. Lettenmaier, E. Foufoula-Georgiou et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.