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
Classification of Grapevine Varieties Using UAV Hyperspectral Imaging
Remote Sens. 2024, 16(12), 2103; https://doi.org/10.3390/rs16122103 (registering DOI) - 10 Jun 2024
Abstract
Classifying grapevine varieties is crucial in precision viticulture, as it allows for accurate estimation of vineyard row growth for different varieties and ensures authenticity in the wine industry. This task can be performed with time-consuming destructive methods, including data collection and analysis in
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Classifying grapevine varieties is crucial in precision viticulture, as it allows for accurate estimation of vineyard row growth for different varieties and ensures authenticity in the wine industry. This task can be performed with time-consuming destructive methods, including data collection and analysis in the laboratory. In contrast, unmanned aerial vehicles (UAVs) offer a markedly more efficient and less restrictive method for gathering hyperspectral data, even though they may yield data with higher levels of noise. Therefore, the first task is the processing of these data to correct and downsample large amounts of data. In addition, the hyperspectral signatures of grape varieties are very similar. In this study, we propose the use of a convolutional neural network (CNN) to classify seventeen different varieties of red and white grape cultivars. Instead of classifying individual samples, our approach involves processing samples alongside their surrounding neighborhood for enhanced accuracy. The extraction of spatial and spectral features is addressed with (1) a spatial attention layer and (2) inception blocks. The pipeline goes from data preparation to dataset elaboration, finishing with the training phase. The fitted model is evaluated in terms of response time, accuracy and data separability and is compared with other state-of-the-art CNNs for classifying hyperspectral data. Our network was proven to be much more lightweight by using a limited number of input bands (40) and a reduced number of trainable weights (560k parameters). Hence, it reduced training time (1 h on average) over the collected hyperspectral dataset. In contrast, other state-of-the-art research requires large networks with several million parameters that require hours to be trained. Despite this, the evaluated metrics showed much better results for our network (approximately 99% overall accuracy), in comparison with previous works barely achieving 81% OA over UAV imagery. This notable OA was similarly observed over satellite data. These results demonstrate the efficiency and robustness of our proposed method across different hyperspectral data sources.
Full article
(This article belongs to the Section Engineering Remote Sensing)
Open AccessArticle
Vertical Distribution of Optical Turbulence at the Peak Terskol Observatory and Mount Kurapdag
by
Artem Y. Shikhovtsev, Chun Qing, Evgeniy A. Kopylov, Sergey A. Potanin and Pavel G. Kovadlo
Remote Sens. 2024, 16(12), 2102; https://doi.org/10.3390/rs16122102 (registering DOI) - 10 Jun 2024
Abstract
Atmospheric turbulence characteristics are essential in determining the quality of astronomical images and implementing adaptive optics systems. In this study, the vertical distributions of optical turbulence at the Peak Terskol observatory (43.27472°N 42.50083°E, 3127 m a.s.l.) using the Era-5 reanalysis and scintillation measurements
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Atmospheric turbulence characteristics are essential in determining the quality of astronomical images and implementing adaptive optics systems. In this study, the vertical distributions of optical turbulence at the Peak Terskol observatory (43.27472°N 42.50083°E, 3127 m a.s.l.) using the Era-5 reanalysis and scintillation measurements are investigated. For the closest reanalysis grid node to the observatory, vertical profiles of the structural constant of the air refractive index turbulent fluctuations were obtained. The calculated vertical profiles are compared with the vertical distribution of turbulence intensity obtained from tomographic measurements with a Shack–Hartmann sensor. The atmospheric coherence length at the location of Terskol Peak was estimated. Using a combination of atmospheric models and paramaterization schemes of turbulence, profiles at Mt. Kurapdag were obtained. The values of atmospheric coherence length at Peak Terskol are compared with estimated values of this length at the ten astronomical sites, including Ali, Lenghu and Daocheng.
Full article
(This article belongs to the Special Issue Land-Atmosphere Interactions and Effects on the Climate of the Tibetan Plateau and Surrounding Regions III)
Open AccessArticle
X-Band Radar Detection of Small Garbage Islands in Different Sea State Conditions
by
Francesco Serafino and Andrea Bianco
Remote Sens. 2024, 16(12), 2101; https://doi.org/10.3390/rs16122101 - 10 Jun 2024
Abstract
This paper presents an assessment of X-band radar’s detection capability to monitor Small Garbage Islands (SGIs), i.e., floating aggregations of marine litter consisting chiefly of plastic, under changing sea states. For this purpose, two radar measurement campaigns were carried out with controlled releases
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This paper presents an assessment of X-band radar’s detection capability to monitor Small Garbage Islands (SGIs), i.e., floating aggregations of marine litter consisting chiefly of plastic, under changing sea states. For this purpose, two radar measurement campaigns were carried out with controlled releases at sea of SGI modules assembled in the laboratory. One campaign was carried out with a calm sea and almost no wind in order to determine the X-band radar system’s detection capabilities in an ideal scenario, while the other campaign took place with rough seas and wind. An analysis of the data acquired during the campaigns confirmed that X-band radar can detect small aggregations of litter floating on the sea surface. To demonstrate the radar’s ability to detect SGIs, a statistical analysis was carried out to calculate the probability of false alarm and the probability of detection for two releases at two different distances from the radar. For greater readability of this work, all of the results obtained are presented both in terms of radar intensity and in terms of the radar cross-section relating to both the targets and the clutter. Another interesting study that is presented in this article concerns the measurement of the speed of movement (drift) of the SGIs compared with the measurement of the speed of the surface currents provided at the same time by the radar. The study also identified the radar detection limits depending on the sea state and the target distance from the antenna.
Full article
(This article belongs to the Section Ocean Remote Sensing)
Open AccessArticle
Low-Sidelobe Imaging Method Utilizing Improved Spatially Variant Apodization for Forward-Looking Sonar
by
Lu Yan, Juan Yang, Feng Xu and Shengchun Piao
Remote Sens. 2024, 16(12), 2100; https://doi.org/10.3390/rs16122100 - 10 Jun 2024
Abstract
For two-dimensional forward-looking sonar imaging, high sidelobes significantly degrade the quality of sonar images. The cosine window function weighting method is often applied to suppress the sidelobe levels in the angular and range dimensions, at the expense of the main lobe resolutions. Therefore,
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For two-dimensional forward-looking sonar imaging, high sidelobes significantly degrade the quality of sonar images. The cosine window function weighting method is often applied to suppress the sidelobe levels in the angular and range dimensions, at the expense of the main lobe resolutions. Therefore, an improved spatially variant apodization imaging method for forward-looking sonar is proposed, to reduce sidelobes without degrading the main lobe resolution in angular-range dimensions. The proposed method is a nonlinear postprocessing operation in which the raw complex-valued sonar image produced by a conventional beamformer and matched filter is weighted by a spatially variant coefficient. To enhance the robustness of the spatially variant apodization approach, the array magnitude and phase errors are calibrated to prevent the occurrence of beam sidelobe increase prior to beamforming operations. The analyzed results of numerical simulations and a lake experiment demonstrate that the proposed method can greatly reduce the sidelobes to approximately −40 dB, while the main lobe width remains unchanged. Moreover, this method has an extremely simple computational process.
Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection (Second Edition))
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Open AccessArticle
Remote Sensing and Environmental Monitoring Analysis of Pigment Migrations in Cave of Altamira’s Prehistoric Paintings
by
Vicente Bayarri, Alfredo Prada, Francisco García, Carmen De Las Heras and Pilar Fatás
Remote Sens. 2024, 16(12), 2099; https://doi.org/10.3390/rs16122099 - 10 Jun 2024
Abstract
The conservation of Cultural Heritage in cave environments, especially those hosting cave art, requires comprehensive conservation strategies to mitigate degradation risks derived from climatic influences and human activities. This study, focused on the Polychrome Hall of the Cave of Altamira, highlights the importance
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The conservation of Cultural Heritage in cave environments, especially those hosting cave art, requires comprehensive conservation strategies to mitigate degradation risks derived from climatic influences and human activities. This study, focused on the Polychrome Hall of the Cave of Altamira, highlights the importance of integrating remote sensing methodologies to carry out effective conservation actions. By coupling a georeferenced Ground Penetrating Radar (GPR) with a 1.6 GHz central-frequency antenna along with photogrammetry, we conducted non-invasive and high-resolution 3D studies to map preferential moisture pathways from the surface of the ceiling to the first 50 cm internally of the limestone structure. In parallel, we monitored the dynamics of surface water on the Ceiling and its correlation with pigment and other substance migrations. By standardizing our methodology, we aim to increase knowledge about the dynamics of infiltration water, which will enhance our understanding of the deterioration processes affecting cave paintings related to infiltration water. This will enable us to improve conservation strategies, suggesting possible indirect measures to reverse active deterioration processes. Integrating remote sensing techniques with geospatial analysis will aid in the validation and calibration of collected data, allowing for stronger interpretations of subsurface structures and conditions. All of this puts us in a position to contribute to the development of effective conservation methodologies, reduce alteration risks, and promote sustainable development practices, thus emphasizing the importance of remote sensing in safeguarding Cultural Heritage.
Full article
(This article belongs to the Special Issue Remote, Proximal Sensing and Geophysics for Cultural Heritage Knowledge and Conservation (Second Edition))
Open AccessArticle
Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation
by
Zongpeng Li, Qian Cheng, Li Chen, Bo Zhang, Shuzhe Guo, Xinguo Zhou and Zhen Chen
Remote Sens. 2024, 16(12), 2098; https://doi.org/10.3390/rs16122098 - 10 Jun 2024
Abstract
Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative for precise agricultural management.
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Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative for precise agricultural management. This study aimed to enhance winter wheat yield predictions with UAV remote sensing and investigate its predictive capability across diverse environments. In this study, RGB and multispectral (MS) data were collected on 6 May 2020 and 10 May 2022 during the grain filling stage of winter wheat. Using the Pearson correlation coefficient method, we identified 34 MS features strongly correlated with yield. Additionally, we identified 24 texture features constructed from three bands of RGB images and a plant height feature, making a total of 59 features. We used seven machine learning algorithms (Cubist, Gaussian process (GP), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), Random Forest (RF)) and applied recursive feature elimination (RFE) to nine feature types. These included single-sensor features, fused sensor features, single-year data, and fused year data. This process yielded diverse feature combinations, leading to the creation of seven distinct yield prediction models. These individual machine learning models were then amalgamated to formulate a Bayesian Model Averaging (BMA) model. The findings revealed that the Cubist model, based on the 2020 and 2022 dataset, achieved the highest R2 at 0.715. Notably, models incorporating both RGB and MS features outperformed those relying solely on either RGB or MS features. The BMA model surpassed individual machine learning models, exhibiting the highest accuracy (R2 = 0.725, RMSE = 0.814 t·ha−1, MSE = 0.663 t·ha−1). Additionally, models were developed using one year’s data for training and another year’s data for validation. Cubist and GLM stood out among the seven individual models, delivering strong predictive performance. The BMA model, combining these models, achieved the highest R2 of 0.673. This highlights the BMA model’s ability to generalize for multi-year data prediction.
Full article
Open AccessArticle
The Generation of High-Resolution Mapping Products for the Lunar South Pole Using Photogrammetry and Photoclinometry
by
Pengying Liu, Xun Geng, Tao Li, Jiujiang Zhang, Yuying Wang, Zhen Peng, Yinhui Wang, Xin Ma and Qiudong Wang
Remote Sens. 2024, 16(12), 2097; https://doi.org/10.3390/rs16122097 - 10 Jun 2024
Abstract
High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at
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High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)’s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images’ features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km2. The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m.
Full article
(This article belongs to the Special Issue Remote Sensing and Photogrammetry Applied to Deep Space Exploration)
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Open AccessArticle
Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks
by
Mei Zhang, Daihao Yin, Zhen Li and Zhong Zhao
Remote Sens. 2024, 16(12), 2096; https://doi.org/10.3390/rs16122096 - 10 Jun 2024
Abstract
This study aims to establish a deep learning-based classification framework to efficiently and rapidly distinguish between coniferous and broadleaf forests across the Loess Plateau. By integrating the deep residual neural network (ResNet) architecture with transfer learning techniques and multispectral data from unmanned aerial
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This study aims to establish a deep learning-based classification framework to efficiently and rapidly distinguish between coniferous and broadleaf forests across the Loess Plateau. By integrating the deep residual neural network (ResNet) architecture with transfer learning techniques and multispectral data from unmanned aerial vehicles (UAVs) and Landsat remote sensing data, the effectiveness of the framework was validated through well-designed experiments. The study began by selecting optimal spectral band combinations, using the random forest algorithm. Pre-trained models were then constructed, and model performance was optimized with different training strategies, considering factors such as image size, sample quantity, and model depth. The results indicated substantial improvements in the model’s classification accuracy and efficiency for reasonable image dimensions and sample sizes, especially for an image size of 3 × 3 pixels and 2000 samples. In addition, the application of transfer learning and model fine-tuning strategies greatly enhanced the adaptability and universality of the model in different classification scenarios. The fine-tuned model achieved remarkable performance improvements in forest-type classification tasks, increasing classification accuracy from 85% to 93% in Zhengning, from 89% to 96% in Yongshou, and from 86% to 94% in Baishui, as well as exceeding 90% in all counties. These results not only confirm the effectiveness of the proposed framework, but also emphasize the roles of image size, sample quantity, and model depth in improving the generalization ability and classification accuracy of the model. In conclusion, this research has developed a technological framework for effective forest landscape recognition, using a combination of multispectral data from UAVs and Landsat satellites. This combination proved to be more effective in identifying forest types than was using Landsat data alone, demonstrating the enhanced capability and accuracy gained by integrating UAV technology. This research provides valuable scientific guidance and tools for policymakers and practitioners in forest management and sustainable development.
Full article
(This article belongs to the Section Remote Sensing and Geo-Spatial Science)
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Open AccessTechnical Note
Experimental Co-Polarimetric GPR Survey on Artificial Vertical Concrete Cracks by the Improved Time-Varying Centroid Frequency Scheme
by
Xuebing Zhang, Junxuan Pei, Xianda Sha, Xuan Feng, Xin Hu, Changle Chen and Zhengchun Song
Remote Sens. 2024, 16(12), 2095; https://doi.org/10.3390/rs16122095 - 10 Jun 2024
Abstract
The experimental setup is devised to simulate the presence of vertical cracks with varying widths within concrete structures. Co-polarimetric ground-penetrating radar (GPR) surveys are carried out to acquire the “VV” and “HH” polarization data. The time-varying centroid frequency attribute is employed to describe
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The experimental setup is devised to simulate the presence of vertical cracks with varying widths within concrete structures. Co-polarimetric ground-penetrating radar (GPR) surveys are carried out to acquire the “VV” and “HH” polarization data. The time-varying centroid frequency attribute is employed to describe the vertical variation in the center frequency of the radar wave, unveiling a gradual vertical decay in the centroid frequency at the locations of vertical cracks. An improved time-varying centroid frequency attribute based on the adaptive sparse S-transform (ASST) is proposed and tested by a finite-difference time-domain model and co-polarimetric GPR data, which can offer better resolution compared to that of the conventional S-transform. By analyzing the waveform and centroid frequency properties of the two polarizations, we conclude that the “VV” polarization is relatively sensitive to centimeter scale cracks, while the “HH” polarization is more sensitive to millimeter scale cracks.
Full article
(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)
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Open AccessArticle
Dynamics of the Net Precipitation in China from 2001 to 2020
by
Jing Pan, Yongyue Ji, Lingyun Yan, Yixia Luo and Jilong Chen
Remote Sens. 2024, 16(12), 2094; https://doi.org/10.3390/rs16122094 - 10 Jun 2024
Abstract
Net precipitation (NP) is the primary source of soil water essential for the functioning of vegetated ecosystems. By quantifying NP as the difference between gross precipitation and canopy interception evaporation, this study examined the dynamics of NP in China from 2001 to 2020
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Net precipitation (NP) is the primary source of soil water essential for the functioning of vegetated ecosystems. By quantifying NP as the difference between gross precipitation and canopy interception evaporation, this study examined the dynamics of NP in China from 2001 to 2020 and the contribution of environmental factors to NP variations was investigated. The findings revealed a multiyear mean NP of 674.62 mm, showcasing a 2.93 mm/yr increase. The spatiotemporal variations in NP were mainly attributed to a remarkable increase in precipitation rather than canopy interception. Notably, climate (temperature, wind speed, surface solar radiation downward and vapor pressure deficit) and vegetation factors (leaf area index and net primary productivity) played a dominant role in NP in 61.53% and 15.39% of China, respectively. The dominant factors contributing to NP changes were vapor pressure deficit (mean contribution rate: −43.68%), temperature (mean contribution rate: 11.69%), and leaf area index (mean contribution rate: 2.13%). The vapor pressure deficit negatively exerts a negative influence on the southern and eastern regions. Temperature and leaf area index have the greatest effect on the northeastern and southwestern regions, respectively. The results provide valuable insights into the pivotal role of climatic and vegetation factors in ecohydrological cycles.
Full article
(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation II)
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Open AccessArticle
Lossy Compression of Single-channel Noisy Images by Modern Coders
by
Sergii Kryvenko, Vladimir Lukin and Benoit Vozel
Remote Sens. 2024, 16(12), 2093; https://doi.org/10.3390/rs16122093 - 10 Jun 2024
Abstract
Lossy compression of remote-sensing images is a typical stage in their processing chain. In design or selection of methods for lossy compression, it is commonly assumed that images are noise-free. Meanwhile, there are many practical situations where an image or a set of
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Lossy compression of remote-sensing images is a typical stage in their processing chain. In design or selection of methods for lossy compression, it is commonly assumed that images are noise-free. Meanwhile, there are many practical situations where an image or a set of its components are noisy. This fact needs to be taken into account since noise presence leads to specific effects in lossy compressed data. The main effect is the possible existence of the optimal operation point (OOP) shown for JPEG, JPEG2000, some coders based on the discrete cosine transform (DCT), and the better portable graphics (BPG) encoder. However, the performance of such modern coders as AVIF and HEIF with application to noisy images has not been studied yet. In this paper, analysis is carried out for the case of additive white Gaussian noise. We demonstrate that OOP can exist for AVIF and HEIF and the performance characteristics in it are quite similar to those for the BPG encoder. OOP exists with a higher probability for images of simpler structure and/or high-intensity noise, and this takes place according to different metrics including visual quality ones. The problems of providing lossy compression by AVIF or HEIF are shown and an initial solution is proposed. Examples for test and real-life remote-sensing images are presented.
Full article
(This article belongs to the Special Issue Recent Progress in Hyperspectral Remote Sensing Data Processing)
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Open AccessTechnical Note
Examining the Capability of the VLF Technique for Nowcasting Solar Flares Based on Ground Measurements in Antarctica
by
Shiwei Wang, Ruoxian Zhou, Xudong Gu, Wei Xu, Zejun Hu, Binbin Ni, Wen Cheng, Jingyuan Feng, Wenchen Ma, Haotian Xu, Yudi Pan, Bin Li, Fang He, Xiangcai Chen and Hongqiao Hu
Remote Sens. 2024, 16(12), 2092; https://doi.org/10.3390/rs16122092 - 9 Jun 2024
Abstract
Measurements of Very-Low-Frequency (VLF) transmitter signals have been widely used to investigate the effects of various space weather events on the D-region ionosphere, including nowcasting solar flares. Previous studies have established a method to nowcast solar flares using VLF measurements, but only using
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Measurements of Very-Low-Frequency (VLF) transmitter signals have been widely used to investigate the effects of various space weather events on the D-region ionosphere, including nowcasting solar flares. Previous studies have established a method to nowcast solar flares using VLF measurements, but only using measurements from dayside propagation paths, and there remains limited focus on day–night mixed paths, which are important for method applicability. Between March and May of 2022, the Sun erupted a total of 56 M-class and 6 X-class solar flares, all of which were well captured by our VLF receiver in Antarctica. Using these VLF measurements, we reexamine the capability of the VLF technique to nowcast solar flares by including day–night mixed propagation paths and expanding the path coverage in longitude compared to that in previous studies. The amplitude and phase maximum changes are generally positively correlated with X-ray fluxes, whereas the time delay is negatively correlated. The curve-fitting parameters that we obtain for the X-ray fluxes and VLF signal maximum changes are consistent with those in previous studies for dayside paths, even though different instruments are used, supporting the flare-nowcasting method. Moreover, the present results show that, for day–night mixed paths, the amplitude and phase maximum changes also scale linearly with the logarithm of the flare X-ray fluxes, but the level of change is notably different from that for dayside paths. The coefficients used in the flare-nowcasting method need to be updated for mixed propagation paths.
Full article
Open AccessArticle
Detecting Melt Pond Onset on Landfast Arctic Sea Ice Using a Dual C-Band Satellite Approach
by
Syeda Shahida Maknun, Torsten Geldsetzer, Vishnu Nandan, John Yackel and Mallik Mahmud
Remote Sens. 2024, 16(12), 2091; https://doi.org/10.3390/rs16122091 - 9 Jun 2024
Abstract
The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic’s changing climate. This study investigates the utility
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The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic’s changing climate. This study investigates the utility of a novel method for detecting the onset of melt ponds on sea ice using a satellite-based, dual-sensor C-band approach, whereby Sentinel-1 provides horizontally polarized (HH) data and Advanced SCATterometer (ASCAT) provides vertically polarized (VV) data. The co-polarized ratio (VV/HH) is used to detect the presence of melt ponds on landfast sea ice in the Canadian Arctic Archipelago in 2017 and 2018. ERA-5 air temperature and wind speed re-analysis datasets are used to establish the VV/HH threshold for pond onset detection, which have been further validated by Landsat-8 reflectance. The co-polarized ratio threshold of three standard deviations from the late winter season (April) mean co-pol ratio values are used for assessing pond onset detection associated with the air temperature and wind speed data, along with visual observations from Sentinel-1 and cloud-free Sentinel-2 imagery. In 2017, the pond onset detection rates were 70.59% for FYI and 92.3% for MYI. Results suggest that this method, because of its dual-platform application, has potential for providing large-area coverage estimation of the timing of sea ice melt pond onset using different earth observation satellites.
Full article
Open AccessArticle
An Automated Approach for Mapping Mining-Induced Fissures Using CNNs and UAS Photogrammetry
by
Kun Wang, Bowei Wei, Tongbin Zhao, Gengkun Wu, Junyang Zhang, Liyi Zhu and Letian Wang
Remote Sens. 2024, 16(12), 2090; https://doi.org/10.3390/rs16122090 - 9 Jun 2024
Abstract
Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and
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Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and mapping. Initially, the ResNet-50 network was employed for the binary classification of the cropped UASP orthophoto images. A comparative analysis was conducted to determine the optimal model between DeepLabv3+ and U-Net. Subsequently, the identified fissures were mosaicked and spatially projected onto the original orthophoto image, incorporating precise projection data, thereby furnishing a spatial reference for environmental governance. The results indicate a classification accuracy of 93% for the ResNet-50 model, with the U-Net model demonstrating a superior identification performance. Fissure orientation and distribution patterns are influenced by the mining direction, ground position of the mining workface, and topographic undulations. Enhancing the CNN performance can be achieved by incorporating variables such as slope indices, vegetation density, and mining workface locations. Lastly, a remote unmanned approach is proposed for the automated mapping of mining-induced fissures, integrated with UAS automated charging station technology. This study contributes to the advancement of intelligent, labor-saving, and unmanned management approaches advocated by the mining industry, with potential for broad applications in mining environmental protection efforts.
Full article
(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)
Open AccessArticle
A Spectral and Spatial Comparison of Satellite-Based Hyperspectral Data for Geological Mapping
by
Rupsa Chakraborty, Imane Rachdi, Samuel Thiele, René Booysen, Moritz Kirsch, Sandra Lorenz, Richard Gloaguen and Imane Sebari
Remote Sens. 2024, 16(12), 2089; https://doi.org/10.3390/rs16122089 - 9 Jun 2024
Abstract
The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra
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The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra can be retrieved. These are typically applied by the satellite operators but use different approaches that can yield different results. In this study, we conduct a comparative analysis of PRISMA, EnMAP, and EMIT hyperspectral satellite data, alongside airborne data acquired by the HyMap sensor, to investigate the consistency between these datasets and their suitability for geological mapping. Two sites in Namibia were selected for this comparison, the Marinkas-Quellen and Epembe carbonatite complexes, based on their geological significance, relatively good exposure, arid climate and data availability. We conducted qualitative and three different quantitative comparisons of the hyperspectral data from these sites. These included correlative comparisons of (1) the reflectance values across the visible-near infrared (VNIR) to shortwave infrared (SWIR) spectral ranges, (2) established spectral indices sensitive to minerals we expect in each of the scenes, and (3) spectral abundances estimated using linear unmixing. The results highlighted a notable shift in inter-sensor consistency between the VNIR and SWIR spectral ranges, with the VNIR range being more similar between the compared sensors than the SWIR. Our qualitative comparisons suggest that the SWIR spectra from the EnMAP and EMIT sensors are the most interpretable (show the most distinct absorption features) but that latent features (i.e., endmember abundances) from the HyMap and PRISMA sensors are consistent with geological variations. We conclude that our results reinforce the need for accurate radiometric and topographic corrections, especially for the SWIR range most commonly used for geological mapping.
Full article
(This article belongs to the Topic Advances in Earth Observation and Geosciences)
Open AccessTechnical Note
Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China
by
Xiaoxue Li, Juan Wu, Shunfa Lu, Dengqiu Li and Dengsheng Lu
Remote Sens. 2024, 16(12), 2088; https://doi.org/10.3390/rs16122088 - 9 Jun 2024
Abstract
Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian
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Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian Province, China, where soil erosion has been a severe problem for a long time, as a case study to explore the method to estimate biomass, including total and aboveground biomass, through the integration of field measurements, handheld laser scanning (HLS), and airborne laser scanning (ALS) data. A stepwise regression model and an allometric equation form model were used to develop biomass estimation models based on Lidar-derived variables at typical areas and at a regional scale. The results indicate that at typical areas, both total and aboveground biomass were best estimated using an allometric equation form model when HLS-derived height and density variables were extracted from a window size of 6 m × 6 m, with the coefficients of determination (R2) of 0.64 and 0.58 and relative root mean square error (rRMSE) of 28.2% and 35.8%, respectively. When connecting HLS-estimated biomass with ALS-derived variables at a regional scale, total and aboveground biomass were effectively predicted with rRMSE values of 17.68% and 17.91%, respectively. The HLS data played an important role in linking field measurements and ALS data. This research provides a valuable method to map Dicranopteris biomass distribution using ALS data when other remotely sensed data cannot effectively estimate the understory vegetation biomass. The estimated biomass spatial pattern will be helpful to understand the role of Dicranopteris in reducing soil erosion and improving the degraded ecosystem.
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(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
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Open AccessArticle
MFPANet: Multi-Scale Feature Perception and Aggregation Network for High-Resolution Snow Depth Estimation
by
Liling Zhao, Junyu Chen, Muhammad Shahzad, Min Xia and Haifeng Lin
Remote Sens. 2024, 16(12), 2087; https://doi.org/10.3390/rs16122087 - 9 Jun 2024
Abstract
Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low
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Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects.
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(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)
Open AccessArticle
Preliminary Exploration of Coverage for Moon-Based/HEO Spaceborne Bistatic SAR Earth Observation in Polar Regions
by
Ke Zhang, Huadong Guo, Di Jiang, Chunming Han and Guoqiang Chen
Remote Sens. 2024, 16(12), 2086; https://doi.org/10.3390/rs16122086 - 9 Jun 2024
Abstract
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To address the challenge of achieving both temporal consistency and spatial continuity in Earth observation data of polar regions, this paper proposes an innovative concept of Moon-based/Highly Elliptical Orbit (HEO) Spaceborne Bistatic Synthetic Aperture Radar (MH-BiSAR), with transmitters on the Moon and receivers
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To address the challenge of achieving both temporal consistency and spatial continuity in Earth observation data of polar regions, this paper proposes an innovative concept of Moon-based/Highly Elliptical Orbit (HEO) Spaceborne Bistatic Synthetic Aperture Radar (MH-BiSAR), with transmitters on the Moon and receivers on HEO satellites. By utilizing ephemeris data and an orbit propagator, this study explores MH-BiSAR’s geometric coverage capabilities in polar regions and conducts a preliminary analysis of its characteristics. The findings reveal that MH-BiSAR could provide continuous multi-day revisit observations of polar regions within each sidereal month, presenting a significant advantage for monitoring high-dynamic and large-scale scientific phenomena, such as polar sea ice observations. This innovative observational method offers a new perspective for polar monitoring and is expected to deepen our understanding of polar phenomena.
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Characterizing Canopy Structure Variability in Amazonian Secondary Successions with Full-Waveform Airborne LiDAR
by
Aline D. Jacon, Lênio Soares Galvão, Rorai Pereira Martins-Neto, Pablo Crespo-Peremarch, Luiz E. O. C. Aragão, Jean P. Ometto, Liana O. Anderson, Laura Barbosa Vedovato, Celso H. L. Silva-Junior, Aline Pontes Lopes, Vinícius Peripato, Mauro Assis, Francisca R. S. Pereira, Isadora Haddad, Catherine Torres de Almeida, Henrique L. G. Cassol and Ricardo Dalagnol
Remote Sens. 2024, 16(12), 2085; https://doi.org/10.3390/rs16122085 - 9 Jun 2024
Abstract
Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in
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Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (early—SS1; intermediate—SS2; advanced—SS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon.
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(This article belongs to the Special Issue Retrieving Leaf Area Index Using Remote Sensing)
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Reconstruction of High-Resolution 3D GPR Data from 2D Profiles: A Multiple-Point Statistical Approach
by
Chongmin Zhang, Mathieu Gravey, Grégoire Mariéthoz and James Irving
Remote Sens. 2024, 16(12), 2084; https://doi.org/10.3390/rs16122084 - 8 Jun 2024
Abstract
Ground-penetrating radar (GPR) is a popular geophysical tool for mapping the underground. High-resolution 3D GPR data carry a large amount of information and can greatly help to interpret complex subsurface geometries. However, such data require a dense collection along closely spaced parallel survey
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Ground-penetrating radar (GPR) is a popular geophysical tool for mapping the underground. High-resolution 3D GPR data carry a large amount of information and can greatly help to interpret complex subsurface geometries. However, such data require a dense collection along closely spaced parallel survey lines, which is time consuming and costly. In many cases, for the sake of efficiency, a choice is made during 3D acquisitions to use a larger spacing between the profile lines, resulting in a dense measurement spacing along the lines but a much coarser one in the across-line direction. Simple interpolation methods are then commonly used to increase the sampling before interpretation, which can work well when the subsurface structures are already well sampled in the across-line direction but can distort such structures when this is not the case. In this work, we address the latter problem using a novel multiple-point geostatistical (MPS) simulation methodology. For a considered 3D GPR dataset with reduced sampling in the across-line direction, we attempt to reconstruct a more densely spaced, high-resolution dataset using a series of 2D conditional stochastic simulations in both the along-line and across-line directions. For these simulations, the existing profile data serve as training images from which complex spatial patterns are quantified and reproduced. To reduce discontinuities in the generated 3D spatial structures caused by independent 2D simulations, the target profile being simulated is chosen randomly, and simulations in the along-line and across-line directions are performed alternately. We show the successful application of our approach to 100 MHz synthetic and 200 MHz field GPR data under multiple decimation scenarios where survey lines are regularly deleted from a dense 3D reference dataset, and the corresponding reconstructions are compared with the original data.
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(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications)
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