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Feature Papers in Remote Sensors 2024–2025

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 8901

Special Issue Editor

Prof. Dr. Assefa M. Melesse

E-Mail Website
Guest Editor
Department of Earth and Environment, AHC-5-390, Florida International University, 11200 SW 8th Street, Miami, FL, USA
Interests: remote sensing; watershed modeling; climate change impact; sediment dynamics; river basin management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Sensors journal section on Remote Sensors is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions as well as recommendations from the EBMs.

The purpose of this Special Issue is to publish a set of papers that represent insightful and influential original research articles or reviews by our section’s EBMs, discussing key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition book after the deadline and will be widely promoted.

We would also like to take this opportunity to call on more scholars to join the section on Remote Sensors so that we can collaborate to further develop this exciting field of research. Potential topics include, but are not limited to, the following:

  • Sensors:
    • Altimeters;
    • Cameras;
    • Lidar;
    • Radar;
    • Radiometers;
    • Topographic sensors;
    • Hyperspectral and multispectral sensors;
    • Seismometers and geophones;
    • Polarimeters.
  • Devices, platforms, and systems:
    • Aircrafts;
    • Autonomous vehicles;
    • Satellites;
    • Autonomous underwater vehicles;
    • Unmanned aerial vehicles

Prof. Dr. Assefa M. Melesse
Guest Editor

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 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. Sensors 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 2600 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

  • remote sensors
  • remote sensing
  • remote devices, platforms, and systems

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

Published Papers (6 papers)

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Research

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18 pages, 4853 KiB  
Article
Exploring the Potential of a Normalized Hotspot Index in Supporting the Monitoring of Active Volcanoes Through Sea and Land Surface Temperature Radiometer Shortwave Infrared (SLSTR SWIR) Data
by Alfredo Falconieri, Francesco Marchese, Emanuele Ciancia, Nicola Genzano, Giuseppe Mazzeo, Carla Pietrapertosa, Nicola Pergola, Simon Plank and Carolina Filizzola
Sensors 2025, 25(6), 1658; https://doi.org/10.3390/s25061658 - 7 Mar 2025
Viewed by 428
Abstract
Every year about fifty volcanoes erupt on average, posing a serious threat for populations living in the neighboring areas. To mitigate the volcanic risk, many satellite monitoring systems have been developed. Information from the medium infrared (MIR) and thermal infrared (TIR) bands of [...] Read more.
Every year about fifty volcanoes erupt on average, posing a serious threat for populations living in the neighboring areas. To mitigate the volcanic risk, many satellite monitoring systems have been developed. Information from the medium infrared (MIR) and thermal infrared (TIR) bands of sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) is commonly exploited for this purpose. However, the potential of daytime shortwave infrared (SWIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3 satellites in supporting the near-real-time monitoring of thermal volcanic activity has not been fully evaluated so far. In this work, we assess this potential by exploring the contribution of a normalized hotspot index (NHI) in the monitoring of the recent Home Reef (Tonga Islands) eruption. By analyzing the time series of the maximum NHISWIR value, computed over the Home Reef area, we inferred information about the waxing/waning phases of lava effusion during four distinct subaerial eruptions. The results indicate that the first eruption phase (September–October 2022) was more intense than the second one (September–November 2023) and comparable with the fourth eruptive phase (June–August 2024) in terms of intensity level; the third eruption phase (January 2024) was more difficult to investigate because of cloudy conditions. Moreover, by adapting the NHI algorithm to daytime SLSTR SWIR data, we found that the detected thermal anomalies complemented those in night-time conditions identified and quantified by the operational Level 2 SLSTR fire radiative power (FRP) product. This study demonstrates that NHI-based algorithms may contribute to investigating active volcanoes located even in remote areas through SWIR data at 500 m spatial resolution, encouraging the development of an automated processing chain for the near-real-time monitoring of thermal volcanic activity by means of night-time/daytime Sentinel-3 SLSTR data. Full article
(This article belongs to the Special Issue Feature Papers in Remote Sensors 2024–2025)
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17 pages, 5497 KiB  
Article
High Spatiotemporal Resolution Monitoring of Water Body Dynamics in the Tibetan Plateau: An Innovative Method Based on Mixed Pixel Decomposition
by Yuhang Jing and Zhenguo Niu
Sensors 2025, 25(4), 1246; https://doi.org/10.3390/s25041246 - 18 Feb 2025
Viewed by 344
Abstract
The Tibetan Plateau, known as the “Third Pole” and the “Water Tower of Asia”, has experienced significant changes in its surface water due to global warming. Accurately understanding and monitoring the spatiotemporal distribution of surface water is crucial for ecological conservation and the [...] Read more.
The Tibetan Plateau, known as the “Third Pole” and the “Water Tower of Asia”, has experienced significant changes in its surface water due to global warming. Accurately understanding and monitoring the spatiotemporal distribution of surface water is crucial for ecological conservation and the sustainable use of water resources. Among existing satellite data, the MODIS sensor stands out for its long time series and high temporal resolution, which make it advantageous for large-scale water body monitoring. However, its spatial resolution limitations hinder detailed monitoring. To address this, the present study proposes a dynamic endmember selection method based on phenological features, combined with mixed pixel decomposition techniques, to generate monthly water abundance maps of the Tibetan Plateau from 2000 to 2023. These maps precisely depict the interannual and seasonal variations in surface water, with an average accuracy of 95.3%. Compared to existing data products, the water abundance maps developed in this study provide better detail of surface water, while also benefiting from higher temporal resolution, enabling effective capture of dynamic water information. The dynamic monitoring of surface water on the Tibetan Plateau shows a year-on-year increase in water area, with an increasing fluctuation range. The surface water abundance products presented in this study not only provide more detailed information for the fine characterization of surface water but also offer a new technical approach and scientific basis for timely and accurate monitoring of surface water changes on the Tibetan Plateau. Full article
(This article belongs to the Special Issue Feature Papers in Remote Sensors 2024–2025)
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31 pages, 9251 KiB  
Article
Seasonal Land Use and Land Cover Mapping in South American Agricultural Watersheds Using Multisource Remote Sensing: The Case of Cuenca Laguna Merín, Uruguay
by Giancarlo Alciaturi, Shimon Wdowinski, María del Pilar García-Rodríguez and Virginia Fernández
Sensors 2025, 25(1), 228; https://doi.org/10.3390/s25010228 - 3 Jan 2025
Viewed by 1077
Abstract
Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover [...] Read more.
Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers. The methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification and performing accuracy assessments. Results indicate a low significance of microwave inputs relative to optical features. Short-wave infrared bands and transformations such as the Normalised Vegetation Index, Land Surface Water Index and Enhanced Vegetation Index demonstrate the highest importance. Accuracy assessments indicate that performance in mapping various classes is optimal, particularly for rice paddies, which play a vital role in the country’s economy and highlight significant environmental concerns. However, challenges persist in reducing confusion between classes, particularly regarding natural vegetation features versus seasonally flooded vegetation, as well as post-agricultural fields/bare land and herbaceous areas. Random Forests and Gradient-Boosting Trees exhibited superior performance compared to Support Vector Machines. Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. These initiatives should consider various data combinations, including additional indices and texture metrics derived from the Grey-Level Co-Occurrence Matrix. Full article
(This article belongs to the Special Issue Feature Papers in Remote Sensors 2024–2025)
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16 pages, 7320 KiB  
Article
Use of Low-Cost Sensors to Study Atmospheric Particulate Matter Concentrations: Limitations and Benefits Discussed through the Analysis of Three Case Studies in Palermo, Sicily
by Filippo Brugnone, Luciana Randazzo and Sergio Calabrese
Sensors 2024, 24(20), 6621; https://doi.org/10.3390/s24206621 - 14 Oct 2024
Cited by 1 | Viewed by 1164
Abstract
The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM2.5 and PM10 fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results [...] Read more.
The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM2.5 and PM10 fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results obtained from two systems equipped with the same sensor model were compared. Excellent linear correlation was observed between the results, with differences in measurements falling within instrumental accuracy. Two instruments equipped with different sensors, models Novasense SDS011 and Plantower PMSA003, were placed at the same site. These were complemented by a weather station to measure meteorological parameters. Upon comparing the atmospheric particulate matter concentrations measured by the two instruments, it was observed that there was a good linear correlation for PM2.5 and a poor linear correlation for PM10. Additionally, the PMSA003 sensor appeared to consistently record higher concentrations than the SDS011 sensor. During periods influenced by natural sources and/or anthropogenic activities at the regional and/or local scale, i.e., the dispersal of Saharan sands, forest fires, and local events using fireworks, abnormal concentrations of atmospheric particulate matter were detected. Despite the inherent limitations in precision and accuracy, both low-cost instruments were able to identify periods with abnormal concentrations of atmospheric particulate matter, regardless of their source or type. Full article
(This article belongs to the Special Issue Feature Papers in Remote Sensors 2024–2025)
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20 pages, 21107 KiB  
Article
Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy)
by Giovanni Gualtieri, Lorenzo Brilli, Federico Carotenuto, Alice Cavaliere, Tommaso Giordano, Simone Putzolu, Carolina Vagnoli, Alessandro Zaldei and Beniamino Gioli
Sensors 2024, 24(12), 3946; https://doi.org/10.3390/s24123946 - 18 Jun 2024
Cited by 3 | Viewed by 1393
Abstract
Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 [...] Read more.
Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full “heating season” (15 October 2022–15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = −4.8 and −2.9 μg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 μg/m3) and slightly under-estimated PM10 concentrations (MB = −0.4 μg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68–0.75 vs. 0.59–0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs. Full article
(This article belongs to the Special Issue Feature Papers in Remote Sensors 2024–2025)
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14 pages, 5946 KiB  
Technical Note
Characterizing and Implementing the Hamamatsu C12880MA Mini-Spectrometer for Near-Surface Reflectance Measurements of Inland Waters
by Andreas Jechow, Jan Bumberger, Bert Palm, Paul Remmler, Günter Schreck, Igor Ogashawara, Christine Kiel, Katrin Kohnert, Hans-Peter Grossart, Gabriel A. Singer, Jens C. Nejstgaard, Sabine Wollrab, Stella A. Berger and Franz Hölker
Sensors 2024, 24(19), 6445; https://doi.org/10.3390/s24196445 - 5 Oct 2024
Viewed by 3878
Abstract
In recent decades, inland water remote sensing has seen growing interest and very strong development. This includes improved spatial resolution, increased revisiting times, advanced multispectral sensors and recently even hyperspectral sensors. However, inland waters are more challenging than oceanic waters due to their [...] Read more.
In recent decades, inland water remote sensing has seen growing interest and very strong development. This includes improved spatial resolution, increased revisiting times, advanced multispectral sensors and recently even hyperspectral sensors. However, inland waters are more challenging than oceanic waters due to their higher complexity of optically active constituents and stronger adjacency effects due to their small size and nearby vegetation and built structures. Thus, bio-optical modeling of inland waters requires higher ground-truthing efforts. Large-scale ground-based sensor networks that are robust, self-sufficient, non-maintenance-intensive and low-cost could assist this otherwise labor-intensive task. Furthermore, most existing sensor systems are rather expensive, precluding their employability. Recently, low-cost mini-spectrometers have become widely available, which could potentially solve this issue. In this study, we analyze the characteristics of such a mini-spectrometer, the Hamamatsu C12880MA, and test it regarding its application in measuring water-leaving radiance near the surface. Overall, the measurements performed in the laboratory and in the field show that the system is very suitable for the targeted application. Full article
(This article belongs to the Special Issue Feature Papers in Remote Sensors 2024–2025)
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