Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics
Impact Factor:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
Twin Satellites HY-1C/D Reveal the Local Details of Astronomical Tide Flooding into the Qiantang River, China
Remote Sens. 2024, 16(9), 1507; https://doi.org/10.3390/rs16091507 (registering DOI) - 24 Apr 2024
Abstract
This article extracts the Qiantang River tidal bore, analyzing the water environment characteristics in front of the tidal line of the Qiantang River tidal bore and behind it. The Qiantang River tidal bore Index (QRI) was established using HY-1C, HY-1D, and Gao Fen-1
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This article extracts the Qiantang River tidal bore, analyzing the water environment characteristics in front of the tidal line of the Qiantang River tidal bore and behind it. The Qiantang River tidal bore Index (QRI) was established using HY-1C, HY-1D, and Gao Fen-1 wide field-of-view (GF-1 WFV) satellite data to precisely determine the location and details of the Qiantang River tidal bore. Comparative analyses of the changes on the two sides of the Qiantang River tidal bore were conducted. The results indicate the following: (1) QRI enhances the visibility of tidal bore lines, accentuating their contrast with the surrounding river water, resulting in a more vivid character. QRI proves to be an effective extraction method, with potential applicability to similar tidal lines in different regions. (2) Observable roughness changes occur at the tidal bore location, with smoother surface textures observed in front of the tidal line compared to those behind it. There is a discernible increase in suspended sediment concentration (SSC) as the tidal bore passes through. (3) This study reveals the mechanism of water environment change induced by the Qiantang River tidal bore, emphasizing its significance in promoting vertical water body exchange as well as scouring the bottom sediments. This effect increases SSC and surface roughness.
Full article
(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment II)
Open AccessArticle
Maximum Likelihood Deconvolution of Beamforming Images with Signal-Dependent Speckle Fluctuations
by
Yuchen Zheng, Xiaobin Ping, Lingxuan Li and Delin Wang
Remote Sens. 2024, 16(9), 1506; https://doi.org/10.3390/rs16091506 (registering DOI) - 24 Apr 2024
Abstract
Ocean Acoustic Waveguide Remote Sensing (OAWRS) typically utilizes large-aperture linear arrays combined with coherent beamforming to estimate the spatial distribution of acoustic scattering echoes. The conventional maximum likelihood deconvolution (DCV) method uses a likelihood model that is inaccurate in the presence of multiple
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Ocean Acoustic Waveguide Remote Sensing (OAWRS) typically utilizes large-aperture linear arrays combined with coherent beamforming to estimate the spatial distribution of acoustic scattering echoes. The conventional maximum likelihood deconvolution (DCV) method uses a likelihood model that is inaccurate in the presence of multiple adjacent targets with significant intensity differences. In this study, we propose a deconvolution algorithm based on a modified likelihood model of beamformed intensities (M-DCV) for estimation of the spatial intensity distribution. The simulated annealing iterative scheme is used to obtain the maximum likelihood estimation. An approximate expression based on the generalized negative binomial (GNB) distribution is introduced to calculate the conditional probability distribution of the beamformed intensity. The deconvolution algorithm is further simplified with an approximate likelihood model (AM-DCV) that can reduce the computational complexity for each iteration. We employ a direct deconvolution method based on the Fourier transform to enhance the initial solution, thereby reducing the number of iterations required for convergence. The M-DCV and AM-DCV algorithms are validated using synthetic and experimental data, demonstrating a maximum improvement of 73% in angular resolution and a sidelobe suppression of 15 dB. Experimental examples demonstrate that the imaging performance of the deconvolution algorithm based on a linear small-aperture array consisting of 16 array elements is comparable to that obtained through conventional beamforming using a linear large-aperture array consisting of 96 array elements. The proposed algorithm is applicable for Ocean Acoustic Waveguide Remote Sensing (OAWRS) and other sensing applications using linear arrays.
Full article
(This article belongs to the Topic Advances in Underwater Acoustics and Aeroacoustics)
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Open AccessArticle
Enhancing Crop Mapping through Automated Sample Generation Based on Segment Anything Model with Medium-Resolution Satellite Imagery
by
Jialin Sun, Shuai Yan, Thomas Alexandridis, Xiaochuang Yao, Han Zhou, Bingbo Gao, Jianxi Huang, Jianyu Yang and Ying Li
Remote Sens. 2024, 16(9), 1505; https://doi.org/10.3390/rs16091505 (registering DOI) - 24 Apr 2024
Abstract
Crop mapping using satellite imagery is crucial for agriculture applications. However, a fundamental challenge that hinders crop mapping progress is the scarcity of samples. The latest foundation model, Segment Anything Model (SAM), provides an opportunity to address this issue, yet few studies have
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Crop mapping using satellite imagery is crucial for agriculture applications. However, a fundamental challenge that hinders crop mapping progress is the scarcity of samples. The latest foundation model, Segment Anything Model (SAM), provides an opportunity to address this issue, yet few studies have been conducted in this area. This study investigated the parcel segmentation performance of SAM on commonly used medium-resolution satellite imagery (i.e., Sentinel-2 and Landsat-8) and proposed a novel automated sample generation framework based on SAM. The framework comprises three steps. First, an image optimization automatically selects high-quality images as the inputs for SAM. Then, potential samples are generated based on the masks produced by SAM. Finally, the potential samples are subsequently subjected to a sample cleaning procedure to acquire the most reliable samples. Experiments were conducted in Henan Province, China, and southern Ontario, Canada, using six proven effective classifiers. The effectiveness of our method is demonstrated through the combination of field-survey-collected samples and differently proportioned generated samples. Our results indicated that directly using SAM for parcel segmentation remains challenging, unless the parcels are large, regular in shape, and have distinct color differences from surroundings. Additionally, the proposed approach significantly improved the performance of classifiers and alleviated the sample scarcity problem. Compared to classifiers trained only by field-survey-collected samples, our method resulted in an average improvement of 16% and 78.5% in Henan and Ontario, respectively. The random forest achieved relatively good performance, with weighted-average F1 of 0.97 and 0.996 obtained using Sentinel-2 imagery in the two study areas, respectively. Our study contributes insights into solutions for sample scarcity in crop mapping and highlights the promising application of foundation models like SAM.
Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Open AccessArticle
Accuracy Assessment and Comparison of National, European and Global Land Use Land Cover Maps at the National Scale—Case Study: Portugal
by
Cidália C. Fonte, Diogo Duarte, Ismael Jesus, Hugo Costa, Pedro Benevides, Francisco Moreira and Mário Caetano
Remote Sens. 2024, 16(9), 1504; https://doi.org/10.3390/rs16091504 (registering DOI) - 24 Apr 2024
Abstract
The free availability of Sentinel-1 and 2 imageries enables the production of high resolution (10 m) global Land Use Land Cover (LULC) maps by a wide range of institutions, which often make them publicly available. This raises several issues: Which map should be
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The free availability of Sentinel-1 and 2 imageries enables the production of high resolution (10 m) global Land Use Land Cover (LULC) maps by a wide range of institutions, which often make them publicly available. This raises several issues: Which map should be used for each type of application? How accurate are these maps? What is the level of agreement between them? This motivated us to assess the thematic accuracy of six LULC maps for continental Portugal with 10 m spatial resolution with reference dates between 2017 and 2020, using the same method and the same reference database, in a bid to make the results comparable. The overall accuracy and the per class user’s and producer’s accuracy are compared with the ones reported by the map producers, at the national, European, or global level, according to their availability. The nomenclatures of the several maps were then analyzed and compared to generate a harmonized nomenclature to which all maps were converted into. The harmonized products were compared directly with a visual analysis and the proportion of regions equally classified was computed, as well as the area assigned per product to each class. The accuracy of these harmonized maps was also assessed considering the previously used reference database. The results show that there are significant differences in the overall accuracy of the original products, varying between 42% and 72%. The differences between the user’s and producer’s accuracy per class are very large for all maps. When comparing the obtained results with the ones reported by the map producers for Portugal, Europe or globally (depending on what is available) the results obtained in this study have lower accuracy metrics values for all maps. The comparison of the harmonized maps shows that they agree in 83% of the study area, but there are differences in terms of detail and area of the classes, mainly for the class “Built up” and “Bare land”.
Full article
(This article belongs to the Section Earth Observation Data)
Open AccessArticle
A Chlorophyll-a Concentration Inversion Model Based on Backpropagation Neural Network Optimized by an Improved Metaheuristic Algorithm
by
Xichen Wang, Jianyong Cui and Mingming Xu
Remote Sens. 2024, 16(9), 1503; https://doi.org/10.3390/rs16091503 - 24 Apr 2024
Abstract
Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks
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Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy “Levy flight” and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm’s limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters.
Full article
(This article belongs to the Special Issue Remote Sensing Retrievals of Optical Properties in Inland Waters and the Coastal Ocean)
Open AccessArticle
A Hierarchical Heuristic Architecture for Unmanned Aerial Vehicle Coverage Search with Optical Camera in Curve-Shape Area
by
Lanjun Liu, Dechuan Wang, Jiabin Yu, Peng Yao, Chen Zhong and Dongfei Fu
Remote Sens. 2024, 16(9), 1502; https://doi.org/10.3390/rs16091502 - 24 Apr 2024
Abstract
This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera
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This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%.
Full article
(This article belongs to the Special Issue Space-Air-Ground-Ocean Integrated Sensing and Information Transmission)
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Open AccessArticle
Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification
by
Sicong Li, Ning Li, Min Jing, Chen Ji and Liang Cheng
Remote Sens. 2024, 16(9), 1501; https://doi.org/10.3390/rs16091501 - 24 Apr 2024
Abstract
Although deep neural networks have made significant progress in tasks related to remote sensing image scene classification, most of these tasks assume that the training and test data are independently and identically distributed. However, when remote sensing scene classification models are deployed in
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Although deep neural networks have made significant progress in tasks related to remote sensing image scene classification, most of these tasks assume that the training and test data are independently and identically distributed. However, when remote sensing scene classification models are deployed in the real world, the model will inevitably encounter situations where the distribution of the test set differs from that of the training set, leading to unpredictable errors during the inference and testing phase. For instance, in the context of large-scale remote sensing scene classification applications, it is difficult to obtain all the feature classes in the training phase. Consequently, during the inference and testing phases, the model will categorize images of unidentified unknown classes into known classes. Therefore, the deployment of out-of-distribution (OOD) detection within the realm of remote sensing scene classification is crucial for ensuring the reliability and safety of model application in real-world scenarios. Despite significant advancements in OOD detection methods in recent years, there remains a lack of a unified benchmark for evaluating various OOD methods specifically in remote sensing scene classification tasks. We designed different benchmarks on three classical remote sensing datasets to simulate scenes with different distributional shift. Ten different types of OOD detection methods were employed, and their performance was evaluated and compared using quantitative metrics. Numerous experiments were conducted to evaluate the overall performance of these state-of-the-art OOD detection methods under different test benchmarks. The comparative results show that the virtual-logit matching methods without additional training outperform the other types of methods on our benchmarks, suggesting that additional training methods are unnecessary for remote sensing image scene classification applications. Furthermore, we provide insights into OOD detection models and performance enhancement in real world. To the best of our knowledge, this study is the first evaluation and analysis of methods for detecting out-of-distribution data in remote sensing. We hope that this research will serve as a fundamental resource for future studies on out-of-distribution detection in remote sensing.
Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)
Open AccessArticle
Study on the Impact of Urban Morphologies on Urban Canopy Heat Islands Based on Relocated Meteorological Stations
by
Tao Shi, Yuanjian Yang and Ping Qi
Remote Sens. 2024, 16(9), 1500; https://doi.org/10.3390/rs16091500 - 24 Apr 2024
Abstract
This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization.
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This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation.
Full article
(This article belongs to the Special Issue Climate and Environmental Changes Monitored by Satellite Remote Sensing III)
Open AccessArticle
A Broadband Information Metasurface-Assisted Target Jamming System for Synthetic Aperture Radar
by
Hua Li, Zhenning Li, Kaiyu Liu, Kaijiang Xu, Chao Luo, You Lv and Yunkai Deng
Remote Sens. 2024, 16(9), 1499; https://doi.org/10.3390/rs16091499 - 24 Apr 2024
Abstract
In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an
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In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system’s ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas.
Full article
Open AccessArticle
Surface Displacement Evaluation of Canto Do Amaro Onshore Oil Field, Brazil, Using Persistent Scatterer Interferometry (PSI) and Sentinel-1 Data
by
Lenon Silva de Oliveira, Fabio Furlan Gama, Edison Crepani, José Claudio Mura and Delano Menecucci Ibanez
Remote Sens. 2024, 16(9), 1498; https://doi.org/10.3390/rs16091498 - 24 Apr 2024
Abstract
This study aims to investigate the occurrence of surface displacements in the Canto do Amaro (CAM) onshore oil field, situated in Rio Grande do Norte, Brazil, using Sentinel-1 data. The persistent scatterer interferometry (PSI) technique was used to perform the analysis based on
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This study aims to investigate the occurrence of surface displacements in the Canto do Amaro (CAM) onshore oil field, situated in Rio Grande do Norte, Brazil, using Sentinel-1 data. The persistent scatterer interferometry (PSI) technique was used to perform the analysis based on 42 Sentinel-1 images, acquired from 23 July 2020 to 21 December 2021. Moreover, information regarding the structural geology of the study area was collected by referencing existing literature datasets. Additionally, a study of the water, gas, and oil production dynamics in the research site was conducted, employing statistical analysis of publicly available well production data. The PSI points results were geospatially correlated with the closest oil well production data and the structural geology information. The PSI results indicate displacement rates from −20.93 mm/year up to 14.63 mm/year in the CAM region. However, approximately 90% of the deformation remained in the range of −5.50 mm/year to 4.95 mm/year, indicating low levels of ground displacement in the designated research area. No geospatial correlation was found between the oil production data and the zones of maximum deformation. In turn, ground displacement demonstrates geospatial correlation with geological structures such as strike-slip and rift faults, suggesting a tectonic movement processes. The PSI results provided a comprehensive overview of ground displacement in the Canto do Amaro field, with millimeter-level accuracy and highlighting its potential as a complementary tool to field investigations.
Full article
(This article belongs to the Special Issue Monitoring Geohazard from Synthetic Aperture Radar Interferometry)
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Open AccessArticle
Optimizing Optical Coastal Remote-Sensing Products: Recommendations for Regional Algorithm Calibration
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Rafael Simão, Juliana Távora, Mhd. Suhyb Salama and Elisa Fernandes
Remote Sens. 2024, 16(9), 1497; https://doi.org/10.3390/rs16091497 - 24 Apr 2024
Abstract
The remote sensing of turbidity and suspended particulate matter (SPM) relies on atmospheric corrections and bio-optical algorithms, but there is no one method that has better accuracy than the others for all satellites, bands, study areas, and purposes. Here, we evaluated different combinations
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The remote sensing of turbidity and suspended particulate matter (SPM) relies on atmospheric corrections and bio-optical algorithms, but there is no one method that has better accuracy than the others for all satellites, bands, study areas, and purposes. Here, we evaluated different combinations of satellites (Landsat-8, Sentinel-2, and Sentinel-3), atmospheric corrections (ACOLITE and POLYMER), algorithms (single- and multiband; empirical and semi-analytical), and bands (665 and 865 nm) to estimate turbidity and SPM in Patos Lagoon (Brazil). The region is suitable for a case study of the regionality of remote-sensing algorithms, which we addressed by regionally recalibrating the coefficients of the algorithms using a method for geophysical observation models (GeoCalVal). Additionally, we examined the results associated with the use of different statistical parameters for classifying algorithms and introduced a new metric (GoF) that reflects performance. The best performance was achieved via POLYMER atmospheric correction and the use of single-band algorithms. Regarding SPM, the recalibrated coefficients yielded a better performance, but, for turbidity, a tradeoff between two statistical parameters occurred. Therefore, the uncertainties in the atmospheric corrections and algorithms used were analyzed based on previous studies. In the future, we suggest the use of in situ radiometric data to better evaluate atmospheric corrections, radiative transfer modeling to bridge data gaps, and multisensor data merging for compiling climate records.
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(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle
Adaptive Resource Scheduling Algorithm for Multi-Target ISAR Imaging in Radar Systems
by
Huan Yao, Hao Lou, Dan Wang, Yijun Chen and Ying Luo
Remote Sens. 2024, 16(9), 1496; https://doi.org/10.3390/rs16091496 - 24 Apr 2024
Abstract
Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing
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Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target’s motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target’s effective rotation angle and the equivalent rotation angular velocity and then define the target’s optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm.
Full article
(This article belongs to the Special Issue Target Detection, Tracking and Imaging Based on Radar)
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Open AccessArticle
Evolution and Built-Up Age Dependency of Urban Thermal Environment
by
Yuanyuan Li, Shuguang Liu, Maochou Liu, Rui Guo, Yi Shi, Xi Peng and Shuailong Feng
Remote Sens. 2024, 16(9), 1495; https://doi.org/10.3390/rs16091495 - 24 Apr 2024
Abstract
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The urban heat island (UHI) represents an anthropogenic modification to the earth’s surface, and its relationship with urban development, built-up age dependency in particular, is poorly understood. We integrated global artificial impervious areas to analyze the impacts of built-up age and urban development
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The urban heat island (UHI) represents an anthropogenic modification to the earth’s surface, and its relationship with urban development, built-up age dependency in particular, is poorly understood. We integrated global artificial impervious areas to analyze the impacts of built-up age and urban development intensity (UDI) on land surface temperatures (LSTs) in Hefei, the capital of Anhui Province of China, from 2000 to 2019. A key finding was that the built-up areas with different built-up ages were strongly associated with LST, and this relationship does not change significantly over time, suggesting temporal stability of spatial patterns of LSTs. This finding puts forward a challenge to the application of the classic concept of space-for-time in LST studies because the premise of space-for-time is that spatial and temporal variation are equivalent. This result reveals the vital importance of annual development activities on the urban thermal environment. Another highlighted result was LST sensitivity to UDI, an effective measure of the impact of urbanization on LST, which increased significantly from 0.255 °C per 10% UDI to 0.818 °C per 10% UDI. The more than doubling of LST sensitivity to UDI should be a major concern for city administration. These findings have crucial theoretical and practical significance for the regulation of LSTs and UHI.
Full article
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Open AccessTechnical Note
Effects of Ice-Microstructure-Based Inherent Optical Properties Parameterization in the CICE Model
by
Yiming Zhang and Jiping Liu
Remote Sens. 2024, 16(9), 1494; https://doi.org/10.3390/rs16091494 - 24 Apr 2024
Abstract
The constant inherent optical properties (IOPs) for sea ice currently applied in sea ice models do not realistically represent the dividing of shortwave radiative fluxes in sea ice and the ocean below it. Here we implement a parameterization of variable IOPs based on
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The constant inherent optical properties (IOPs) for sea ice currently applied in sea ice models do not realistically represent the dividing of shortwave radiative fluxes in sea ice and the ocean below it. Here we implement a parameterization of variable IOPs based on ice microstructures in the Los Alamos sea ice model, version 6.0 (CICE6) and investigate its effects on the simulation of the dividing of shortwave radiation and sea ice in the Arctic. Our sensitivity experiments indicate that variable IOP parameterization results in strong seasonal variation for the IOP parameters, typically reaching the seasonal maximum in the boreal summer. With such large differences, variable IOP parameterization leads to increased absorbed solar radiation at the surface and in the interior of Arctic sea ice relative to constant IOPs, up to ~3 W/m2, but decreased solar radiation penetrating into the ocean, up to ~5–6 W/m2. The changes in the dividing of shortwave fluxes in sea ice and the ocean below it induced by the variable IOPs have significant influence on Arctic sea ice thickness by modulating surface and bottom melting and frazil ice formation (increasing surface melting by ~16% and reducing bottom melting by ~11% in summer).
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(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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Open AccessArticle
Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences
by
Hauke Hoppe, Peter Dietrich, Philip Marzahn, Thomas Weiß, Christian Nitzsche, Uwe Freiherr von Lukas, Thomas Wengerek and Erik Borg
Remote Sens. 2024, 16(9), 1493; https://doi.org/10.3390/rs16091493 - 23 Apr 2024
Abstract
Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2
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Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2 imagery. It proposes a new testing methodology that systematically analyzes the quality of the spatial transfer of trained models. In this study, the classification results of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Support Vector Machines (SVM), and a Majority Voting of all models and their spatial transferability are assessed. The proposed testing methodology comprises 18 test scenarios to investigate phenological, temporal, spatial, and quantitative (quantitative regarding available training data) influences. Results show that the model accuracies tend to decrease with increasing time due to the differences in phenological phases in different regions, with a combined F1-score of 82% (XGBoost) when trained on a single day, 72% (XGBoost) when trained on the half-season, and 61% when trained over the entire growing season (Majority Voting).
Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing II)
Open AccessArticle
Landsat 8 and 9 Underfly International Surface Reflectance Validation Collaboration
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Joshua Mann, Emily Maddox, Mahesh Shrestha, Jeffrey Irwin, Jeffrey Czapla-Myers, Aaron Gerace, Eon Rehman, Nina Raqueno, Craig Coburn, Guy Byrne, Mark Broomhall and Andrew Walsh
Remote Sens. 2024, 16(9), 1492; https://doi.org/10.3390/rs16091492 - 23 Apr 2024
Abstract
During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe
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During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe the opportunity of collecting surface in situ data to compare directly to Landsat 8 and Landsat 9 data. Ground validation teams identified surface targets that would yield reflectance and/or thermal values that could be used in Landsat Level 2 product validation and set out to collect at these locations using surface validation methodologies the teams developed. The values were collected from each team and compared directly with each other across each of the different bands of both Landsat 8 and 9. The results proved consistency across the Landsat 8 and 9 platforms and also agreed well in surface reflectance underestimation of the Coastal Aerosol, Blue, and SWIR2 bands.
Full article
(This article belongs to the Special Issue Landsat 9 Pre-launch, Commissioning, and Early On-Orbit Imaging Performance)
Open AccessArticle
Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites
by
Fan Yang, Qi Feng, Yadong Zhou, Wen Li, Xiaoyang Zhang and Baoyin He
Remote Sens. 2024, 16(9), 1491; https://doi.org/10.3390/rs16091491 - 23 Apr 2024
Abstract
Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP
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Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP retrieval model using Sentinel-2 observations and field-measured daily-scale water quality. The model was successfully applied to estimate the temporal and spatial variations of TP concentration in the middle reaches of the Yangtze River (MYR) from 2020 to 2023. Our results show: (1) the model accuracy of TP concentration retrieval with turbidity is significantly higher (R2 = 0.71, MAPE = 15.78%) than that for the model without turbidity (R2 = 0.62, MAPE = 16.38%); (2) the turbidity and TP concentration in the MYR is higher in summer and autumn than in winter and spring; and (3) the turbidity and total phosphorus (TP) concentration of the Yangtze River showed a significant increase after passing through Dongting Lake (p < 0.05).
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(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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Open AccessArticle
Revealing Subtle Active Tectonic Deformation: Integrating Lidar, Photogrammetry, Field Mapping, and Geophysical Surveys to Assess the Late Quaternary Activity of the Sava Fault (Southern Alps, Slovenia)
by
Petra Jamšek Rupnik, Jure Atanackov, Barbara Horn, Branko Mušič, Marjana Zajc, Christoph Grützner, Kamil Ustaszewski, Sumiko Tsukamoto, Matevž Novak, Blaž Milanič, Anže Markelj, Kristina Ivančič, Ana Novak, Jernej Jež, Manja Žebre, Miloš Bavec and Marko Vrabec
Remote Sens. 2024, 16(9), 1490; https://doi.org/10.3390/rs16091490 - 23 Apr 2024
Abstract
We applied an interdisciplinary approach to analyze the late Quaternary activity of the Sava Fault in the Slovenian Southern Alps. The Sava Fault is an active strike-slip fault, and part of the Periadriatic Fault System that accommodated the convergence of Adria and Europe.
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We applied an interdisciplinary approach to analyze the late Quaternary activity of the Sava Fault in the Slovenian Southern Alps. The Sava Fault is an active strike-slip fault, and part of the Periadriatic Fault System that accommodated the convergence of Adria and Europe. It is one of the longest faults in the Southern Alps. Using high-resolution digital elevation models from lidar and photogrammetric surveys, we were able to overcome the challenges of assessing fault activity in a region with intense surface processes, dense vegetation, and relatively low fault slip rates. By integrating remote sensing analysis, geomorphological mapping, structural geological investigations, and near-surface geophysics (electrical resistivity tomography and ground penetrating radar), we were able to find subtle geomorphological indicators, detect near-surface deformation, and show distributed surface deformation and a complex fault pattern. Using optically stimulated luminescence dating, we tentatively estimated a slip rate of 1.8 ± 0.4 mm/a for the last 27 ka, which exceeds previous estimates and suggests temporal variability in fault behavior. Our study highlights the importance of modern high-resolution remote sensing techniques and interdisciplinary approaches in detecting tectonic deformation in relatively low-strain rate environments with intense surface processes. We show that slip rates can vary significantly depending on the studied time window. This is a critical piece of information since slip rates are a key input parameter for seismic hazard studies.
Full article
(This article belongs to the Special Issue Multiplatform Remote Sensing Techniques for Active Tectonics, Seismotectonics, and Volcanic Hazard Assessment)
Open AccessArticle
A Long-Duration Glacier Change Analysis for the Urumqi River Valley, a Representative Region of Central Asia
by
Lin Wang, Shujing Yang, Kangning Chen, Shuangshuang Liu, Xiang Jin and Yida Xie
Remote Sens. 2024, 16(9), 1489; https://doi.org/10.3390/rs16091489 - 23 Apr 2024
Abstract
The increasing global warming trend has resulted in the mass loss of most glaciers. The Urumqi Vally, located in the dry and cold zone of China, and its widely dispersed glaciers are significant to the regional ecological environment, oasis economic development, and industrial
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The increasing global warming trend has resulted in the mass loss of most glaciers. The Urumqi Vally, located in the dry and cold zone of China, and its widely dispersed glaciers are significant to the regional ecological environment, oasis economic development, and industrial and agricultural production. This is representative of glaciers in Middle Asia and represents one of the world’s longest observed time series of glaciers, beginning in 1959. The Urumqi Headwater Glacier No. 1 (UHG-1) has a dominant presence in the World Glacier Monitoring Service (WGMS). This paper supplies a comprehensive analysis of past studies and future modeling of glacier changes in the Urumqi Valley. It has received insufficient attention in the past, and the mass balance of UHG-1 was used to verify that the geodetic results and the OGGM model simulation results are convincing. The main conclusions are: The area of 48.68 ± 4.59 km2 delineated by 150 glaciers in 1958 decreased to 21.61 ± 0.27 km2 delineated by 108 glaciers in 2022, with a reduction of 0.47 ± 0.04 km2·a−1 (0.96% a−1 in 1958–2022). The glacier mass balance by geodesy is −0.69 ± 0.11 m w.e.a−1 in 2000–2022, which is just deviating from the measured result (−0.66 m w.e.a−1), but the geodetic result in this paper can be enough to reflect the glacier changes (−0.65 ± 0.11 m w.e.a−1) of the URB in 2000–2022. The future loss rate of area and volume will undergo a rapid and then decelerating process, with the fastest and slowest inflection points occurring around 2035 and 2070, respectively. High temperatures and large precipitation in summer accelerate glacier loss, and the corresponding lag period of glacier change to climate is about 2–3 years.
Full article
(This article belongs to the Special Issue Remote Sensing of Cryosphere and Related Processes)
Open AccessArticle
A New Extended Target Detection Method Based on the Maximum Eigenvalue of the Hermitian Matrix
by
Yong Xu, Yongfeng Zhu and Zhiyong Song
Remote Sens. 2024, 16(9), 1488; https://doi.org/10.3390/rs16091488 - 23 Apr 2024
Abstract
In the field of radar target detection, the conventional approach is to employ the range profile energy accumulation method for detecting extended targets. However, this method becomes ineffective when dealing with non-stationary and non-uniform radar clutter scenarios, as well as long-distance targets with
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In the field of radar target detection, the conventional approach is to employ the range profile energy accumulation method for detecting extended targets. However, this method becomes ineffective when dealing with non-stationary and non-uniform radar clutter scenarios, as well as long-distance targets with weak radar cross sections (RCSs). In such cases, the signal-to-noise ratio (SNR) of the target echo is severely degraded, rendering the energy accumulation detection algorithm unreliable. To address this issue, this paper presents a new extended target detection method based on the maximum eigenvalue of the Hermitian matrix. This method utilizes a detection model that incorporates observed data and employs the likelihood ratio test (LRT) theory to derive the maximum eigenvalue detector at low SNR. Specifically, the detector constructs a matrix using a sliding window block with the available data and then computes the maximum eigenvalue of the covariance matrix. Subsequently, the maximum eigenvalue matrix is transformed into a one-dimensional eigenvalue image, enabling extended target detection through analogy with the energy accumulation detection method. Furthermore, this paper analyzes the proposed extended target detection method from both theoretical and experimental perspectives, validating it through field-measured data. The results obtained from the measured data demonstrate that the method effectively enhances the SNR in low SNR conditions, thereby improving target detection performance. Additionally, the method exhibits robustness across different scattering center targets.
Full article
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