Special Issue "Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery"

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

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editors

Dr. Fabio Castaldi
E-Mail Website
Guest Editor
ILVO- Flanders research institute for agriculture, fisheries and food, Technology and Food Science - Agricultural Engineering. Burg. Van Gansberghelaan 115 bus 1 - 9820 Merelbeke, Belgium
Interests: soil imaging spectroscopy; multi and hyperspectral remote sensing; precision agriculture; Earth Observation; geostatistics; sustainable agriculture; soil mapping; SOC; data fusion
Dr. Anne Gobin
E-Mail Website
Co-Guest Editor
Flemish institute for technological research –VITO, Boeretang 200, Mol, Belgium
Interests: agri-environmental modeling; land use dynamics; time series of high resolution satellite data; climate impacts on soil–crop–atmosphere systems
Dr. Simone Pascucci
E-Mail Website
Co-Guest Editor
CNR IMAA, Tito Scalo (PZ), 85050, Italy
Interests: multi and hyperspectral remote sensing for environmental and agricultural applications; imaging spectroscopy; airborne flight campaigns; sensor calibration and validation; ground segment
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Special Issue Information

Dear Colleagues,

The increasing amount of freely available satellite data is attracting new users outside the scientific community. Currently, we are experiencing a democratization of the Earth Observation (EO) data, which is largely due to the Copernicus Sentinel and NASA Landsat missions, as well as to the recent and rapid development of unmanned aircraft systems. At the same time, this plethora of satellite data offers new possibilities and challenges for EO scientists and experts. On one hand, the short revisit time (high temporal resolution) of the new generation satellite imagers allows the enhancement of multi-temporal analysis; on the other hand, the large variability of remote sensing data raises the issue of the implementation of data fusion techniques for big data. This variability concerns the type of sensor (optical, SAR, thermal, LIDAR, etc.), as well as the platform on which the sensor is placed (spaceborne, airborne, UAV). Multi-sensor data fusion techniques allow data from different sources to be combined, enriching and enhancing EO time series and, consequently, improving multi-temporal analysis.

This Special Issue will present a collection of valuable and rigorous research works that advance current knowledge on the multi-temporal and multi-source analysis of remote sensed imagery.

Specific topics include, but are not limited to

  • Multi-temporal image pre-processing and harmonization;
  • Implementation of multi-sensor and multi-temporal data fusion techniques;
  • Multi-temporal image analysis for the monitoring of dynamic factors, trend analysis, classification, clustering, and regression.

The above-listed topics can be applied to several dynamic applications (agriculture, geomorphology, soil, marine and freshwater environments, forest, land use change, biodiversity, climate change, environmental disasters, etc.). Any kind of sensor data (optical, SAR, LIDAR, TIR, etc.), as well as any kind of spectral, radiometric, spatial, or temporal resolution can be considered. The choice of papers for publication will be based on quality, soundness, and rigor of research.

Dr. Fabio Castaldi
Dr. Anne Gobin
Dr. Simone Pascucci
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 1800 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

  • Sentinel
  • Landsat
  • UAV
  • multi-temporal analysis
  • data fusion
  • time series
  • long- and short-term monitoring
  • multi-sensor
  • big data

Published Papers (2 papers)

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Research

Open AccessArticle
Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation
Remote Sens. 2019, 11(22), 2612; https://doi.org/10.3390/rs11222612 - 07 Nov 2019
Abstract
In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five [...] Read more.
In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications. Full article
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Open AccessArticle
Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery
Remote Sens. 2019, 11(19), 2304; https://doi.org/10.3390/rs11192304 - 03 Oct 2019
Abstract
A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is [...] Read more.
A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms. Full article
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