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Innovative Approaches in Earth Remote Sensing Technology
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Innovative Approaches in Earth Remote Sensing Technology

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 4564

Special Issue Editors

Dr. Isaac Ramos

E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: RF design; real and synthetic aperture microwave radiometer; remote sensing; CubeSats
Special Issues, Collections and Topics in MDPI journals
Dr. Mehmet Ogut

E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: smart concepts and novel approaches to RF microwave instrumentation including AI radiometers and photonic spectrometers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce a Special Issue dedicated to the latest advancements in remote sensing technology and its applications. Remote sensors have revolutionized our understanding of Earth's climate and the complex interactions between the ocean, land, and atmosphere. This progress is owed to exceptional developments in sensor design, data processing techniques, and transformative technologies like miniaturization, enhanced communication, networking capabilities, machine learning, and artificial intelligence.

The aim of this Special Issue is to highlight cutting-edge concepts of remote sensing instruments that leverage these transformative technologies. Whether deployed on satellites, airborne platforms, or ground-based systems, these new remote sensing technologies have the potential to be utilized in various fields, such as passive/active sensing, microwave or millimeter-wave sensing, optical sensing, or their combinations.

We invite authors to contribute their original research on remote sensing technology advancements in any of the mentioned fields. Submissions covering various levels, including subsystems, system design, mission-level innovations, and system-of-systems approaches, are welcome. We also encourage studies that analyze performance improvements in terms of spatial, radiometric, spectral, or temporal resolutions, particularly in scientific applications.

This Special Issue provides a valuable platform for researchers and practitioners to share their knowledge, insights, and breakthroughs in the rapidly evolving field of remote sensing. We eagerly anticipate your contributions, which will undoubtedly enhance our understanding of our dynamic planet.

Dr. Isaac Ramos
Dr. Mehmet Ogut
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 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 sensing
  • artificial intelligence
  • smart sensors
  • RF sensors
  • optical sensing

Published Papers (5 papers)

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Research

17 pages, 14008 KiB  
Article
Fusion of Dense Airborne LiDAR and Multispectral Sentinel-2 and Pleiades Satellite Imagery for Mapping Riparian Forest Species Biodiversity at Tree Level
by Houssem Njimi, Nesrine Chehata and Frédéric Revers
Sensors 2024, 24(6), 1753; https://doi.org/10.3390/s24061753 - 8 Mar 2024
Viewed by 597
Abstract
Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion [...] Read more.
Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the feature level and the decision level. At the feature level, LiDAR 2D attributes were derived and combined with multispectral imagery vegetation indices. At the decision level, LiDAR data were used for 3D tree crown delimitation, providing unique trees or groups of trees. The segmented tree crowns were used as a support for an object-based species classification at tree level. Data augmentation techniques were used to improve the training process, and classification was carried out with a random forest classifier. The workflow was entirely automated using a Python script, which allowed the assessment of four different fusion configurations. The best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a kappa of 0.66, thanks to red edge-based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages, helping to discriminate the species. Full article
(This article belongs to the Special Issue Innovative Approaches in Earth Remote Sensing Technology)
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16 pages, 3069 KiB  
Article
Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions
by Florian Huber, Stefan Schulz and Volker Steinhage
Sensors 2024, 24(5), 1604; https://doi.org/10.3390/s24051604 - 29 Feb 2024
Viewed by 596
Abstract
Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST [...] Read more.
Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors’ view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data. Full article
(This article belongs to the Special Issue Innovative Approaches in Earth Remote Sensing Technology)
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17 pages, 11678 KiB  
Article
A Statistical Approach for the Integration of Multi-Temporal InSAR and GNSS-PPP Ground Deformation Measurements
by Ahmet Delen, Fusun Balik Sanli, Saygin Abdikan, Ali Hasan Dogan, Utkan Mustafa Durdag, Taylan Ocalan, Bahattin Erdogan, Fabiana Calò and Antonio Pepe
Sensors 2024, 24(1), 43; https://doi.org/10.3390/s24010043 (registering DOI) - 20 Dec 2023
Viewed by 1153
Abstract
Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul [...] Read more.
Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between −4.86 mm/yr and −23.58 mm/yr and −9.50 and −27.77 mm/yr for InSAR and GNSS, respectively. Full article
(This article belongs to the Special Issue Innovative Approaches in Earth Remote Sensing Technology)
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17 pages, 6575 KiB  
Article
A Method for Point Cloud Accuracy Analysis Based on Intensity Information
by Siyuan Li, Dehua Zheng, Dongjie Yue, Chuang Hu and Xinjiang Ma
Sensors 2023, 23(22), 9135; https://doi.org/10.3390/s23229135 - 12 Nov 2023
Viewed by 851
Abstract
Three-dimensional laser scanning has emerged as a prevalent measurement method in numerous high-precision applications, and the precision of the obtained data is closely related to the intensity information. Comprehending the association between intensity and point cloud accuracy facilitates scanner performance assessment, optimization of [...] Read more.
Three-dimensional laser scanning has emerged as a prevalent measurement method in numerous high-precision applications, and the precision of the obtained data is closely related to the intensity information. Comprehending the association between intensity and point cloud accuracy facilitates scanner performance assessment, optimization of data acquisition strategies, and evaluation of point cloud precision, thereby ensuring data reliability for high-precision applications. In this study, we investigated the correlation between point cloud accuracy and two distinct types of intensity information. In addition, we presented methods for assessing point cloud accuracy using these two forms of intensity information, along with their applicable scopes. By examining the percentage intensity, we analyzed the reflectance properties of the scanned object’s surface employing the Lambertian model. Our findings indicate that the Lambertian circle fitting radius is inversely correlated with the scanner’s ranging error at a constant scanning distance. Experimental outcomes substantiate that modifying the surface characteristics of the object enables the attainment of higher-precision point cloud data. By constructing a model associating the raw reflectance intensity with ranging errors, we developed a single-point error ellipsoid model to assess the accuracy of individual points within the point cloud. The experiments revealed that the ranging error model based on the raw intensity is solely applicable to point cloud data unaffected by specular reflectance properties. Moreover, the devised single-point error ellipsoid model accurately evaluates the measurement error of individual points. Both analytical methods can be utilized to evaluate the performance of the scanner as well as the accuracy of the acquired point cloud data, providing reliable data support for various high-precision applications. Full article
(This article belongs to the Special Issue Innovative Approaches in Earth Remote Sensing Technology)
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16 pages, 11440 KiB  
Article
The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
by Joan Francesc Munoz-Martin, Xavier Bosch-Lluis, Omkar Pradhan, Shannon T. Brown, Pekka P. Kangaslahti, Alan B. Tanner, Mehmet Ogut, Sidharth Misra and Boon H. Lim
Sensors 2023, 23(20), 8554; https://doi.org/10.3390/s23208554 - 18 Oct 2023
Viewed by 994
Abstract
This manuscript presents the Microwave Temperature and Humidity Profiler (MTHP), a dual-band spectroradiometer designed for measuring multi-incidence angle temperature and humidity atmospheric profiles from an aircraft platform. The MTHP bands are at 60 GHz for measuring the oxygen complex lines, therefore at this [...] Read more.
This manuscript presents the Microwave Temperature and Humidity Profiler (MTHP), a dual-band spectroradiometer designed for measuring multi-incidence angle temperature and humidity atmospheric profiles from an aircraft platform. The MTHP bands are at 60 GHz for measuring the oxygen complex lines, therefore at this band, MTHP has a hyperspectral radiometer able to provide 2048 channels over an 8 GHz bandwidth, and 183 GHz for measuring water vapor, which only uses four channels since this absorption band’s spectral richness is simpler. The MTHP builds upon the Microwave Temperature Profiler (MTP) with the inclusion of the hyperspectral radiometer. The instrument’s design, components, and calibration methods are discussed in detail, with a focus on the three-point calibration scheme involving internal calibration loads and static air temperature readings. Preliminary results from the Technological Innovation into Iodine and GV aircraft Environmental Research (TI3GER) campaign are presented, showcasing the instrument’s performance during flights across diverse geographical regions. The manuscript presents successful antenna temperature measurements at 60 GHz and 183 GHz. The hyperspectral measurements are compared with a simulated antenna temperature using the Atmospheric Radiative Transfer Simulator (ARTS) showing an agreement better than R2 > 0.88 for three of the flights analyzed. Additionally, the manuscript draws attention to potential Radio Frequency Interference (RFI) effects observed during a specific flight, underscoring the instrument’s sensitivity to external interference. This is the first-ever airborne demonstration of a broadband and hyperspectral multi-incidence angle 60 GHz measurement. Future work on the MTHP could result in an improved spatial resolution of the atmospheric temperature vertical profile and, hence, help in estimating the Planetary Boundary Layer (PBL) with better accuracy. The MTHP and its hyperspectral multi-incidence angle at 60 GHz have the potential to be a valuable tool for investigating the PBL’s role in atmospheric dynamics, offering insights into its impact on Earth’s energy, water, and carbon cycles. Full article
(This article belongs to the Special Issue Innovative Approaches in Earth Remote Sensing Technology)
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