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Advanced Remote Sensing Technologies in Ocean Observations

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

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 7264

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Special Issue Editors

Dr. Difeng Wang

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Guest Editor

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Interests: ocean remote sensing; sea surface temperature; ecology; environmental monitoring; radiative transfer; radiometry; remote sensing system

Dr. Jochen Horstmann

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Guest Editor

Helmholtz-Zentrum Hereon, Max Planck Str.1, 21502 Geesthacht, Germany
Interests: radar and video based sensors; ocean surface; subsurface processes

Dr. Piera Jaume

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Guest Editor

EMBIMOS Research Group, Department of Physical and Technological Oceanography, Institute of Marine Sciences, CSIC, 37-49 Passeig Marítim de la Barceloneta, E-08003 Barcelona, Spain
Interests: improving the observation systems of the environment; either developing new technologies (sensors, instruments or observation platforms) or advanced data processing methods

Special Issue Information

Dear Colleagues,

The ocean plays an important role in global climate and weather changes, and the use of ocean remote sensing can enable us to achieve the effective observation of the marine environment and climate. The continuous development of marine remote sensing in various countries means that these technologies are playing an increasingly important role in many fields, such as marine disaster prevention and mitigation, environmental protection, marine ecology, marine rights protection, and resource development.

You are invited to contribute to this Special Issue to present advanced remote sensing technologies for use in ocean observations. Topics may include, but are not limited to:

Sensor design and the application of new ocean color satellites, microwave satellites, radar satellites, and laser satellites used in the ocean;
Monitoring technology for marine ecology and dynamic environment, marine pollution, marine disasters, and marine resources;
The fusion application of active and passive remote sensing;
Multi-source, multi-spectrum, multi-temporal, and multi-scale data processing technology systems;
The application of deep learning and AI technology in ocean remote sensing.

Dr. Difeng Wang
Dr. Jochen Horstmann
Dr. Piera Jaume
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.

Published Papers (5 papers)

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Research

16 pages, 5199 KiB

Open AccessArticle

Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites

by Leyang Li, Guixing Cao, Jun Liu and Xiaohao Cai

Sensors 2023, 23(23), 9479; https://doi.org/10.3390/s23239479 - 28 Nov 2023

Viewed by 670

Abstract

The existing image matching methods for remote sensing scenes are usually based on local features. The most common local features like SIFT can be used to extract point features. However, this kind of methods may extract too many keypoints on the background, resulting in low attention to the main object in a single image, increasing resource consumption and limiting their performance. To address this issue, we propose a method that could be implemented well on resource-limited satellites for remote sensing images ship matching by leveraging line features. A keypoint extraction strategy called line feature based keypoint detection (LFKD) is designed using line features to choose and filter keypoints. It can strengthen the features at corners and edges of objects and also can significantly reduce the number of keypoints that cause false matches. We also present an end-to-end matching process dependent on a new crop patching function, which helps to reduce complexity. The matching accuracy achieved by the proposed method reaches 0.972 with only 313 M memory and 138 ms testing time. Compared to the state-of-the-art methods in remote sensing scenes in extensive experiments, our keypoint extraction method can be combined with all existing CNN models that can obtain descriptors, and also improve the matching accuracy. The results show that our method can achieve ∼50% test speed boost and ∼30% memory saving in our created dataset and public datasets. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies in Ocean Observations)

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18 pages, 2741 KiB

Open AccessArticle

Use of Sentinel-3 OLCI Images and Machine Learning to Assess the Ecological Quality of Italian Coastal Waters

by Chiara Lapucci, Andrea Antonini, Emanuele Böhm, Emanuele Organelli, Luca Massi, Alberto Ortolani, Carlo Brandini and Fabio Maselli

Sensors 2023, 23(22), 9258; https://doi.org/10.3390/s23229258 - 18 Nov 2023

Cited by 1 | Viewed by 1407

Abstract

Understanding and monitoring the ecological quality of coastal waters is crucial for preserving marine ecosystems. Eutrophication is one of the major problems affecting the ecological state of coastal marine waters. For this reason, the control of the trophic conditions of aquatic ecosystems is needed for the evaluation of their ecological quality. This study leverages space-based Sentinel-3 Ocean and Land Color Instrument imagery (OLCI) to assess the ecological quality of Mediterranean coastal waters using the Trophic Index (TRIX) key indicator. In particular, we explore the feasibility of coupling remote sensing and machine learning techniques to estimate the TRIX levels in the Ligurian, Tyrrhenian, and Ionian coastal regions of Italy. Our research reveals distinct geographical patterns in TRIX values across the study area, with some regions exhibiting eutrophic conditions near estuaries and others showing oligotrophic characteristics. We employ the Random Forest Regression algorithm, optimizing calibration parameters to predict TRIX levels. Feature importance analysis highlights the significance of latitude, longitude, and specific spectral bands in TRIX prediction. A final statistical assessment validates our model’s performance, demonstrating a moderate level of error (MAE of 0.51) and explanatory power (R² of 0.37). These results highlight the potential of Sentinel-3 OLCI imagery in assessing ecological quality, contributing to our understanding of coastal water ecology. They also underscore the importance of merging remote sensing and machine learning in environmental monitoring and management. Future research should refine methodologies and expand datasets to enhance TRIX monitoring capabilities from space. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies in Ocean Observations)

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21 pages, 4344 KiB

Open AccessArticle

Improved the Impact of SST for HY-2A Scatterometer Measurements by Using Neural Network Model

by Jing Wang, Xuetong Xie, Ruru Deng, Jiayi Li, Yuming Tang, Yeheng Liang and Yu Guo

Sensors 2023, 23(10), 4825; https://doi.org/10.3390/s23104825 - 17 May 2023

Viewed by 1044

Abstract

The variation of sea surface temperature (SST) can change the backscatter coefficient measured by a scatterometer, resulting in a decrease in the accuracy of the sea surface wind measurement. This study proposed a new approach to correct the effect of SST on the backscatter coefficient. The method focuses on the Ku-band scatterometer HY-2A SCAT, which is more sensitive to SST than C-band scatterometers, can improve the wind measurement accuracy of the scatterometer without relying on reconstructed geophysical model function (GMF), and is more suitable for operational scatterometers. Through comparisons to WindSat wind data, we found that the Ku-band scatterometer HY-2A SCAT wind speeds are systemically lower under low SST and higher under high SST conditions. We trained a neural network model called the temperature neural network (TNNW) using HY-2A data and WindSat data. TNNW-corrected backscatter coefficients retrieved wind speed with a small systematic deviation from WindSat wind speed. In addition, we also carried out a validation of HY-2A wind and TNNW wind using European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data as a reference, and the results showed that the retrieved TNNW-corrected backscatter coefficient wind speed is more consistent with ECMWF wind speed, indicating that the method is effective in correcting SST impact on HY-2A scatterometer measurements. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies in Ocean Observations)

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16 pages, 32403 KiB

Open AccessArticle

Back-Projected Signal-Based Self-Interferometric Phase Analysis Technique for Sea Surface Observation Using a Single Scatterometer System

by Ji-hwan Hwang and Duk-jin Kim

Sensors 2023, 23(6), 3049; https://doi.org/10.3390/s23063049 - 12 Mar 2023

Viewed by 1192

Abstract

This manuscript presents a self-interferometric phase analysis technique for sea surface observation using a single scatterometer system. The self-interferometric phase is proposed to complement the imprecise analysis results due to the very meager signal strength measured at a high incident angle of more than 30°, which is a vulnerability of the existing analysis method using the Doppler frequency based on the backscattered signal strength. Moreover, compared to conventional interferometry, it is characterized by the phase-based analysis using consecutive signals from a single scatterometer system without any auxiliary system or channel. To apply the interferometric signal process on the moving sea surface observation, it is necessary to secure a reference target; however, this is hard to solve in practice. Hence, we adopted the back-projection algorithm to project the radar signals onto a fixed reference position above the sea surface, where the theoretical model for extracting the self-interferometric phase was derived from the radar-received signal model applying the back-projection algorithm. The observation performance of the proposed method was verified using the raw data collected at the Ieodo Ocean Research Station in Republic of Korea. In the observation result for wind velocity at the high incident angles of 40° and 50°, the self-interferometric phase analysis technique shows a better performance of a correlation coefficient of more than about 0.779 and an RMSE (root-mean-square error) of about 1.69 m/s compared to the existing method of a correlation coefficient of less than 0.62 and RMSE of more than 2.46 m/s. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies in Ocean Observations)

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17 pages, 30345 KiB

Open AccessArticle

On the Effect of Interferences on X-Band Radar Wave Measurements

by Pavel Chernyshov, Katrin Hessner, Andrey Zavadsky and Yaron Toledo

Sensors 2022, 22(10), 3818; https://doi.org/10.3390/s22103818 - 18 May 2022

Cited by 1 | Viewed by 2142

Abstract

X-band radars are in growing use for various oceanographic purposes, providing spatial real-time information about sea state parameters, surface elevations, currents, and bathymetry. Therefore, it is very appealing to use such systems as operational aids to harbour management. In an installation of such a remote sensing system in Haifa Port, consistent radially aligned spikes of brightness randomly distributed with respect to azimuth were identified. These streak noise patterns were found to be interfering with the common approach of oceanographic analysis. Harbour areas are regularly frequented with additional electromagnetic transmissions from other ship and land-based radars, which may serve as a source of such interference. A new approach is proposed for the filtering of such undesirable interference patterns from the X-band radar images. It was verified with comparison to in-situ measurements of a nearby wave buoy. Regardless of the actual source of the corresponding pseudo-wave energy, it was found to be crucial to apply such filtration in order to improve the performance of the standard oceanographic parameter retrieval algorithm. This results in better estimation of the mean sea state parameters towards lower values of the significant wave height. For the commercial WaMoSII system this enhancement was clearly apparent in the improvement of the built-in quality control criteria marks. The developed prepossessing procedure improves the robustness of the directional spectra estimation practically eliminating pseudo-wave energy components. It also extends the system’s capability to measure storm events earlier on, a fact that is of high importance for harbour operational decision making. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies in Ocean Observations)

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