Special Issue "Data Fusion for Remote Sensing of Fires and Floods in the Sentinels Era"

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

Deadline for manuscript submissions: 30 December 2021.

Special Issue Editors

Dr. Gloria Bordogna
E-Mail Website
Guest Editor
CNR IREA, via Bassini 15, 20133 Milano, Italy
Interests: fuzzy logic and soft computing for the representation and management of imprecision and uncertainty of textual and geographic information; volunteered geographic information user-driven quality assessment in citizen science; crowdsourced information spatiotemporal analytics; information retrieval on the web; flexible query languages for information retrieval and geographic information systems; ill-defined environmental knowledge representation and management; multisource geographic information fusion and synthesis
Special Issues and Collections in MDPI journals
Dr. Daniela Stroppiana
E-Mail Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment, Italian National Research Council, (IREA-CNR), 7-00185 Roma, Italy
Interests: monitoring natural ecosystems with Earth observation data; mapping fires and burned areas; fire severity; integration of optical and SAR data for fire monitoring; time series analysis; processing and classification of multi-spectral UAV data; methods and protocols for validation thematic products
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues

This Special Issue aims to collect scientific contributions proposing multisource Data Fusion methods for the remotely sensed mapping, monitoring and assessment of fires and floods in the Sentinels Era.

Natural hazards such as fires and floods are phenomena with a large spatial dimension and impact. Mapping, monitoring and assessment can be carried out using satellite remote image platforms. Sentinel missions have brought a new era for operational monitoring, providing reliable and up-to-date information for ecosystem monitoring and managing.

Climate change poses further challenges for the exacerbation of the impacts and effects of extreme events on the ecosystems; in this framework, remotely sensed data can support environmental analyses and monitoring by making particular use of Earth Observation Sentinel missions.

Besides Sentinel, from a broader perspective, Earth Observation systems orbiting the Earth encompass several missions, providing data with distinct spectral, spatial and temporal granularities. Algorithms and methods that are able to fully exploit the complementarity of these systems are necessary to provide robust and operational semantic information that is easily interpretable by humans.

Moreover, the integration of remotely sensed data with data collected by ground/in situ measurements calls for new methods and applications of data fusion to enhance and improve traditional approaches to the remote sensing of natural resources.

This Special Issue aims to collect a broad set of scientific contributions proposing Data Fusion methods for remotely sensed mapping, monitoring and assessment of fires and floods by the use of multisource sensors and data, including both Sentinel and alternative remote-sensing data, in situ sensors and human sensors. Topics of interest include both theoretic and applicative themes:

Dr. Gloria Bordogna
Dr. Daniela Stroppiana
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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 2400 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.


  • Multisource and multi-scale image data fusion from Sentinel missions
  • Multi-mission data fusion (Sentinel and not Sentinel missions)
  • Multiscale, multispectral and multi-temporal remote-sensing data fusion
  • Pixel-level, attribute-level, feature-level and object-level remote-sensing data fusion
  • Quality enhancement by remote-sensing data fusion
  • Uncertainty reduction by remote-sensing data fusion
  • Concurrent and complementary remote-sensing data fusion
  • Numeric and symbolic remote-sensing data fusion
  • Soft fusion strategies in remote-sensing
  • Remote-sensing data fusion for susceptibility, vulnerability, hazard and risk keyword.

Published Papers (1 paper)

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Mapping Outburst Floods Using a Collaborative Learning Method Based on Temporally Dense Optical and SAR Data: A Case Study with the Baige Landslide Dam on the Jinsha River, Tibet
Remote Sens. 2021, 13(11), 2205; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112205 - 04 Jun 2021
Cited by 1 | Viewed by 1077
Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping [...] Read more.
Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping using remote sensing images in recent years, but little attention has been devoted to outburst flood mapping. The short-duration nature of these events and observation constraints from cloud cover have significantly challenged outburst flood mapping. This study used the outburst flood of the Baige landslide dam on the Jinsha River on 3 November 2018 as an example to propose a new flood mapping method that combines optical images from Sentinel-2, synthetic aperture radar (SAR) images from Sentinel-1 and a Digital Elevation Model (DEM). First, in the cloud-free region, a comparison of four spectral indexes calculated from time series of Sentinel-2 images indicated that the normalized difference vegetation index (NDVI) with the threshold of 0.15 provided the best separation flooded area. Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness’s changes caused by the flood. We carried out the flood range prediction model based on the random forest algorithm. Training samples consisted of 13 feature vectors obtained from the Hue-Saturation-Value color space, backscattering coefficient differences/ratio, DEM data, and a label set from the flood range prepared from Sentinel-2 images. Finally, a field investigation and confusion matrix tested the prediction accuracy of the end-of-flood map. The overall accuracy and Kappa coefficient were 92.3%, 0.89 respectively. The full extent of the outburst floods was successfully obtained within five days of its occurrence. The multi-source data merging framework and the massive sample preparation method with SAR images proposed in this paper, provide a practical demonstration for similar machine learning applications using remote sensing. Full article
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