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Satellite Remote Sensing for Meteorological Disaster Monitoring and Forecasting
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Satellite Remote Sensing for Meteorological Disaster Monitoring and Forecasting

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5774

Special Issue Editors

Dr. Dimitrios Katsanos

E-Mail Website
Guest Editor
Institute for Environmental Research and Sustainable Development (IERSD), National Observatory of Athens (NOA), National Observatory of Athens, Athens, Greece
Interests: precipitation science; remote sensing of precipitation; storm and atmospheric electrical activity; numerical weather forecasting models
Special Issues, Collections and Topics in MDPI journals
Dr. Adrianos Retalis

E-Mail Website
Co-Guest Editor
Institute for Environmental Research & Sustainable Development (IERSD), National Observatory of Athens (NOA), 15236 Athens, Greece
Interests: remote sensing; GIS; natural hazards; land cover/use change; natural disasters and extremes; spatial data analysis; climate change; air quality; desertification; soil erosion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite remote sensing has changed meteorological science by allowing detailed and solid observation of Earth’s atmospheric and surface conditions. The climate change-driven increase in meteorological disasters like floods, droughts, heatwaves and wildfires makes remote sensing fundamental for monitoring, analyzing and forecasting such events. Satellite technology advancements provide crucial knowledge for disaster management, enhancing understanding and improving early warning systems. State-of-the-art monitoring and forecasting techniques are needed to mitigate possible impacts.

The aim of this Special Issue is to present recent developments in the use of satellite remote sensing for the monitoring and forecasting of meteorological disasters. It is expected to address core areas of geospatial science and environmental monitoring. Invited contributions may cover innovative techniques, data integration methods, and case studies that highlight the application of remote sensing for real-time disaster tracking, early warning, and post-disaster assessment.

In this Special Issue, original research articles and reviews are welcome. To provide a versatile survey of satellite remote sensing for meteorological disaster monitoring and forecasting, we invite submissions across a range of topics, including (but not limited to) the following:

  • Techniques for integrating satellite data with ground-based observations and model outputs;
  • Advanced data assimilation methods to improve meteorological disaster forecasting;
  • Real-time systems for early detection of meteorological hazards;
  • Recent satellite missions (e.g., GOES, Sentinel) designed for atmospheric monitoring;
  • Algorithm for automating disaster detection and tracking;
  • Studies linking satellite observation to climate-induced changes in disaster patterns;
  • Remote sensing for evaluating the impacts of meteorological disasters;
  • Techniques for assessing recovery and resilience in affected areas;
  • Novel remote sensing technologies (e.g., radar) that enhance disaster monitoring capabilities;
  • Applications of AI and machine learning in analyzing satellite data for forecasting.

Dr. Dimitrios Katsanos
Dr. Adrianos Retalis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • meteorological hazards
  • extreme Precipitation
  • flash floods
  • early warning systems
  • droughts
  • wildfires
  • heat waves
  • meteorological disaster management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

24 pages, 26775 KB  
Article
Robust Synthesis Weather Radar from Satellite Imagery: A Light/Dark Classification and Dual-Path Processing Approach
by Wei Zhang, Hongbo Ma, Yanhai Gan, Junyu Dong, Renbo Pang, Xiaojiang Song, Cong Liu and Hongmei Liu
Remote Sens. 2025, 17(21), 3609; https://doi.org/10.3390/rs17213609 - 31 Oct 2025
Viewed by 1105
Abstract
Weather radar reflectivity plays a critical role in precipitation estimation and convective storm identification. However, due to terrain limitations and the uneven spatial distribution of radar stations, oceanic regions have long suffered from a lack of radar observations, resulting in extensive monitoring gaps. [...] Read more.
Weather radar reflectivity plays a critical role in precipitation estimation and convective storm identification. However, due to terrain limitations and the uneven spatial distribution of radar stations, oceanic regions have long suffered from a lack of radar observations, resulting in extensive monitoring gaps. Geostationary meteorological satellites have wide-area coverage and near-real-time observation capability, offering a viable solution for synthesizing radar reflectivity in these regions. Most previous synthesis studies have adopted fixed time-window data partitioning, which introduces significant noise into visible-light observations under large-scale, low-illumination conditions, thereby degrading synthesis quality. To address this issue, we propose an integrated deep-learning method that combines illumination-based classification and reflectivity synthesis to enhance the accuracy of radar reflectivity synthesis from geostationary meteorological satellites. This approach integrates a classification network with a synthesis network. First, visible-light observations from the Himawari-8 satellite are classified based on illumination conditions to separate valid signals from noise; then, noise-free infrared observations and multimodal fused data are fed into dedicated synthesis networks to generate composite reflectivity products. In experiments, the proposed method outperformed the baseline approach in regions with strong convection (≥35 dBZ), with a 9.5% improvement in the critical success index, a 7.5% increase in the probability of detection, and a 6.1% reduction in the false alarm rate. Additional experiments confirmed the applicability and robustness of the method across various complex scenarios. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Meteorological Disaster Monitoring and Forecasting)
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37 pages, 4865 KB  
Article
Coupling Deep Abstract Networks and Metaheuristic Optimization Algorithms for a Multi-Hazard Assessment of Wildfire and Drought
by Jinping Liu, Qingfeng Hu, Panxing He, Lei Huang and Yanqun Ren
Remote Sens. 2025, 17(17), 3090; https://doi.org/10.3390/rs17173090 - 4 Sep 2025
Cited by 2 | Viewed by 1487
Abstract
This study employed Deep Abstract Networks (DANets), independently and in combination with the Whale Optimization Algorithm (WOA), to generate high-resolution susceptibility maps for drought and wildfire hazards in the Oroqen Autonomous Banner in Inner Mongolia. Presence samples included 309 wildfire points from MODIS [...] Read more.
This study employed Deep Abstract Networks (DANets), independently and in combination with the Whale Optimization Algorithm (WOA), to generate high-resolution susceptibility maps for drought and wildfire hazards in the Oroqen Autonomous Banner in Inner Mongolia. Presence samples included 309 wildfire points from MODIS active fire data and 200 drought points derived from a custom Standardized Drought Condition Index. DANets-WOA models showed clear performance improvements over their solitary counterparts. For drought susceptibility, RMSE was reduced from 0.28 to 0.21, MAE from 0.17 to 0.11, and AUC improved from 85.7% to 88.9%. Wildfire susceptibility mapping also improved, with RMSE decreasing from 0.39 to 0.36, MAE from 0.32 to 0.28, and AUC increasing from 78.9% to 85.1%. Loss function plots indicated improved convergence and reduced overfitting following optimization. A pairwise z-statistic analysis revealed significant differences (p < 0.05) in susceptibility classifications between the two modeling approaches. Notably, the overlap of drought and wildfire susceptibilities within the forest–steppe transitional zone reflects a climatically and ecologically tense corridor, where moisture stress, vegetation gradients, and human land-use converge to amplify multi-hazard risk beyond the sum of individual threats. The integration of DANets with the WOA demonstrates a robust and scalable framework for dual hazard modeling. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Meteorological Disaster Monitoring and Forecasting)
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22 pages, 17237 KB  
Article
Spatiotemporal Evolution and Intensification of Extreme Precipitation Events in Mainland China from 1961 to 2022
by Weimeng Gan, Hao Guo, Ying Cao, Wei Wang, Na Yao, Yunqian Wang and Philippe De Maeyer
Remote Sens. 2025, 17(12), 2037; https://doi.org/10.3390/rs17122037 - 13 Jun 2025
Cited by 4 | Viewed by 1992
Abstract
Under the context of global warming, extreme precipitation events have become more frequent and intense, causing substantial environmental and societal impacts. Using the daily gridded precipitation dataset (CHM_PRE) for mainland China, this study investigates the spatiotemporal evolution of extreme precipitation events from 1961 [...] Read more.
Under the context of global warming, extreme precipitation events have become more frequent and intense, causing substantial environmental and societal impacts. Using the daily gridded precipitation dataset (CHM_PRE) for mainland China, this study investigates the spatiotemporal evolution of extreme precipitation events from 1961 to 2022, focusing on regional disparities in frequency, event duration, and total precipitation. Events are further categorized based on peak frequency and the timing of peak intensity to reveal their distinct spatial and temporal characteristics. The results indicate the following: (1) From 1961 to 2022, the frequency of extreme precipitation events across most regions of mainland China exhibited a statistically significant increasing trend, especially in Xinjiang, where the average annual frequency rose by 56 times from 1961–1980 to 2000–2022. (2) Event durations show a general trend, with the most pronounced decline in Southwest China, where the overall duration and the duration above the 90th percentile decreased at Sen’s slopes of −6.4 × 10−2 and −6 × 10−3 days·year−1, respectively. (3) Event intensity has increased, especially in Southeast China, with peak and daily intensities rising at 2.9 × 10−2 mm·year−1 and 1.9 × 10−2 mm·day−1·year−1. (4) Short-duration events dominate Xinjiang, averaging 102 times per year and accounting for 35.58% of all events. (5) Events of varying durations display clear spatial differences: 2-day events are most frequent in Xinjiang, the Tibetan Plateau, Northwest China, and Northern China, while 4-day events are concentrated in Southeast China. Moreover, a nonlinear positive correlation between event duration and total precipitation volume suggests a complex interplay between precipitation persistence and intensity. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Meteorological Disaster Monitoring and Forecasting)
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