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
Design of a Multimodal Detection System Tested on Tea Impurity Detection
Remote Sens. 2024, 16(9), 1590; https://doi.org/10.3390/rs16091590 (registering DOI) - 29 Apr 2024
Abstract
A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time
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A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.
Full article
(This article belongs to the Special Issue Machine Learning and Image Processing for Object Detection)
Open AccessArticle
FEMSFNet: Feature Enhancement and Multi-Scales Fusion Network for SAR Aircraft Detection
by
Wenbo Zhu, Liu Zhang, Chunqiang Lu, Guowei Fan, Ying Song, Jianbo Sun and Xueying Lv
Remote Sens. 2024, 16(9), 1589; https://doi.org/10.3390/rs16091589 (registering DOI) - 29 Apr 2024
Abstract
Aircraft targets, as high-value subjects, are a focal point in Synthetic Aperture Radar (SAR) image interpretation. To tackle challenges like limited SAR aircraft datasets and shortcomings in existing detection algorithms (complexity, poor performance, weak generalization), we present the Feature Enhancement and Multi-Scales Fusion
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Aircraft targets, as high-value subjects, are a focal point in Synthetic Aperture Radar (SAR) image interpretation. To tackle challenges like limited SAR aircraft datasets and shortcomings in existing detection algorithms (complexity, poor performance, weak generalization), we present the Feature Enhancement and Multi-Scales Fusion Network (FEMSFNet) for SAR aircraft detection. FEMSFNet employs diverse image augmentation and integrates optimized Squeeze-and-Excitation Networks (SE) with residual network (ResNet) in a SdE-Resblock structure for a lightweight yet accurate model. It introduces ssppf-CSP module, an improved pyramid pooling model, to prevent receptive field deviation in deep network training. Tailored for SAR aircraft detection, FEMSFNet optimizes loss functions, emphasizing both speed and accuracy. Evaluation on the SAR Aircraft Detection Dataset (SADD) demonstrates significant improvements compared to the contrasted algorithms: precision rate (92%), recall rate (96%), and F1 score (94%), with a maximum increase of 12.2% in precision, 12.9% in recall, and 13.3% in F1 score.
Full article
Open AccessArticle
A Modified Frequency Nonlinear Chirp Scaling Algorithm for High-Speed High-Squint Synthetic Aperture Radar with Curved Trajectory
by
Kun Deng, Yan Huang, Zhanye Chen, Dongning Fu, Weidong Li, Xinran Tian and Wei Hong
Remote Sens. 2024, 16(9), 1588; https://doi.org/10.3390/rs16091588 (registering DOI) - 29 Apr 2024
Abstract
The imaging of high-speed high-squint synthetic aperture radar (HSHS-SAR), which is mounted on maneuvering platforms with curved trajectory, is a challenging task due to the existence of 3-D acceleration and the azimuth spatial variability of range migration and Doppler parameters. Although existing imaging
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The imaging of high-speed high-squint synthetic aperture radar (HSHS-SAR), which is mounted on maneuvering platforms with curved trajectory, is a challenging task due to the existence of 3-D acceleration and the azimuth spatial variability of range migration and Doppler parameters. Although existing imaging algorithms based on linear range walk correction (LRWC) and nonlinear chirp scaling (NCS) can reduce the range–azimuth coupling of the frequency spectrum (FS) and the spatial variability of the Doppler parameter to some extent, they become invalid as the squint angle, speed, and resolution increase. Additionally, most of them ignore the effect of acceleration phase calibration (APC) on NCS, which should not be neglected as resolution increases. For these issues, a modified frequency nonlinear chirp scaling (MFNCS) algorithm is proposed in this paper. The proposed MFNCS algorithm mainly includes the following aspects. First, a more accurate approximation of range model (MAARM) is established to improve the accuracy of the instantaneous slant range history. Second, a preprocessing of the proposed algorithm based on the first range compression, LRWC, and a spatial-invariant APC (SIVAPC) is implemented to eliminate most of the effects of high-squint angle and 3-D acceleration on the FS. Third, a spatial-variant APC (SVAPC) is performed to remove azimuth spatial variability introduced by 3-D acceleration, and the range focusing is accomplished by the bulk range cell migration correction (BRCMC) and extended secondary range compression (ESRC). Fourth, the azimuth-dependent characteristics evaluation based on LRWC, SIVAPC, and SVAPC is completed to derive the MFNCS algorithm with fifth-order chirp scaling function for azimuth compression. Consequently, the final image is focused on the range time and azimuth frequency domain. The experimental simulation results verify the effectiveness of the proposed algorithm. With a curved trajectory, HSHS-SAR imaging is carried out at a 50° geometric squint angle and 500 m × 500 m imaging width. The integrated sidelobe ratio and peak sidelobe ratio of the point targets at the scenario edges approach the theoretical values, and the range-azimuth resolution is 1.5 m × 3.0 m.
Full article
(This article belongs to the Topic Information Sensing Technology for Intelligent/Driverless Vehicle, 2nd Volume)
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Open AccessArticle
Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation
by
Xiangjie She, Deying Li, Shuo Yang, Xiaoxu Xie, Yiqing Sun and Wenjie Zhao
Remote Sens. 2024, 16(9), 1587; https://doi.org/10.3390/rs16091587 - 29 Apr 2024
Abstract
The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in
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The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control.
Full article
(This article belongs to the Special Issue Application of Remote Sensing Approaches in Geohazard Risk)
Open AccessArticle
Assessment of the Added Value of the GOCE GPS Data on the GRACE Monthly Gravity Field Solutions
by
Xiang Guo, Yidu Lian, Yu Sun, Hao Zhou and Zhicai Luo
Remote Sens. 2024, 16(9), 1586; https://doi.org/10.3390/rs16091586 - 29 Apr 2024
Abstract
The time-varying gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission suffer from pronounced longitudinal stripe errors in the spatial domain. A potential way to mitigate such errors is to combine GRACE data with observations from other sources.
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The time-varying gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission suffer from pronounced longitudinal stripe errors in the spatial domain. A potential way to mitigate such errors is to combine GRACE data with observations from other sources. In this study, we investigate the impacts on GRACE monthly gravity field solutions of incorporating the GPS data collected by the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission. To that end, we produce GRACE/GOCE combined monthly gravity field solutions through combination on the normal equation level and compare them with the GRACE-only solutions, for which we have considered the state-of-the-art ITSG-Grace2018 solutions. Analysis in the spectral domain reveals that the combined solutions have a notably lower noise level beyond degree 30, with cumulative errors up to degree 96 being reduced by 31%. A comparison of the formal errors reveals that the addition of GOCE GPS data mainly improves (near-) sectorial coefficients and resonant orders, which cannot be well determined by GRACE alone. In the spatial domain, we also observe a significant reduction by at least 30% in the noise of recovered mass changes after incorporating the GOCE GPS data. Furthermore, the signal-to-noise ratios of mass changes over 180 large river basins were improved by 8–20% (dependent on the applied Gaussian filter radius). These results demonstrate that the GOCE GPS data can augment the GRACE monthly gravity field solutions and support a future GOCE-type mission for tracking more accurate time-varying gravity fields.
Full article
(This article belongs to the Special Issue Geophysical Applications of GOCE and GRACE Measurements)
Open AccessArticle
Spatial Distribution and Differentiation Analysis of Coastal Aquaculture in China Based on Remote Sensing Monitoring
by
Dan Meng, Xiaomei Yang, Zhihua Wang, Yueming Liu, Junyao Zhang, Xiaoliang Liu and Bin Liu
Remote Sens. 2024, 16(9), 1585; https://doi.org/10.3390/rs16091585 - 29 Apr 2024
Abstract
Multiple datasets related to pond and marine aquaculture have been published using diverse remote sensing technologies, yet a comprehensive dataset detailing spatial distribution on both land and sea sides is lacking. Firstly, a meticulous comparison of datasets which we selected related to aquaculture
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Multiple datasets related to pond and marine aquaculture have been published using diverse remote sensing technologies, yet a comprehensive dataset detailing spatial distribution on both land and sea sides is lacking. Firstly, a meticulous comparison of datasets which we selected related to aquaculture ponds and marine, ensuring consistency in trends. Subsequently, the datasets published by our team were edited and integrated to illustrate aquaculture activities on both sides of China’s coastal zone. Finally, a spatial differentiation of coastal aquaculture in major provinces was analyzed. This analysis also utilizes the types of coastline and statistical data, guiding coordinated resource management efforts. The results unveil a distinctive spatial distribution pattern, concentrating aquaculture in the northern regions—Bohai Sea, Jiangsu, Fujian, and Pearl River coasts in Guangdong. The provinces rich in aquaculture resources, such as Shandong, Guangdong, and Liaoning, exhibit extensive coastlines. However, remote sensing monitoring suggests an underestimation of Liaoning’s marine aquaculture compared to statistical yearbook data. Furthermore, southern provinces like Guangdong and Fujian exhibit significantly higher aquaculture output than Liaoning. Zhejiang leads in fishing output. The paper outlines the future development direction of coastal aquaculture, emphasizing a strategic, integrated land–sea approach for sustainable development.
Full article
(This article belongs to the Section Ocean Remote Sensing)
Open AccessArticle
Hyperfidelis: A Software Toolkit to Empower Precision Agriculture with GeoAI
by
Vasit Sagan, Roberto Coral, Sourav Bhadra, Haireti Alifu, Omar Al Akkad, Aviskar Giri and Flavio Esposito
Remote Sens. 2024, 16(9), 1584; https://doi.org/10.3390/rs16091584 - 29 Apr 2024
Abstract
The potential of artificial intelligence (AI) and machine learning (ML) in agriculture for improving crop yields and reducing the use of water, fertilizers, and pesticides remains a challenge. The goal of this work was to introduce Hyperfidelis, a geospatial software package that provides
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The potential of artificial intelligence (AI) and machine learning (ML) in agriculture for improving crop yields and reducing the use of water, fertilizers, and pesticides remains a challenge. The goal of this work was to introduce Hyperfidelis, a geospatial software package that provides a comprehensive workflow that includes imagery visualization, feature extraction, zonal statistics, and modeling of key agricultural traits including chlorophyll content, yield, and leaf area index in a ML framework that can be used to improve food security. The platform combines a user-friendly graphical user interface with cutting-edge machine learning techniques, bridging the gap between plant science, agronomy, remote sensing, and data science without requiring users to possess any coding knowledge. Hyperfidelis offers several data engineering and machine learning algorithms that can be employed without scripting, which will prove essential in the plant science community.
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(This article belongs to the Section Biogeosciences Remote Sensing)
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Open AccessArticle
Synergy between Short-Range Lidar and In Situ Instruments for Determining the Atmospheric Boundary Layer Lidar Ratio
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Andres Esteban Bedoya-Velásquez, Romain Ceolato, Gloria Titos, Juan Antonio Bravo-Aranda, Andrea Casans, Diego Patrón, Sol Fernández-Carvelo, Juan Luis Guerrero-Rascado and Lucas Alados-Arboledas
Remote Sens. 2024, 16(9), 1583; https://doi.org/10.3390/rs16091583 - 29 Apr 2024
Abstract
Short-range elastic backscatter lidar (SR-EBL) systems are remote sensing instruments for studying low atmospheric boundary layer processes. This work presents a field campaign oriented to filling the gap between the near-surface aerosol processes regarding aerosol radiative properties and connecting them with the atmospheric
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Short-range elastic backscatter lidar (SR-EBL) systems are remote sensing instruments for studying low atmospheric boundary layer processes. This work presents a field campaign oriented to filling the gap between the near-surface aerosol processes regarding aerosol radiative properties and connecting them with the atmospheric boundary layer (ABL), centering attention on the residual layer and the ABL transition periods. A Colibri Aerosol Lidar (CAL) instrument, based on the short-range lidar with high spatio-temporal resolution, was used for the first time in the ACTRIS AGORA facility (Andalusian Global Observatory of the Atmosphere) in Granada (Spain). This study showed the possibility of combining lidar and in situ measurements in the lowermost 150 m. The results address, on the one hand, the characterization of the short-range lidar for developing a method to find the calibration constant of the system and to correct the incomplete overlap to further data exploitation. On the other hand, relevant radiative properties such as the temporal series of the aerosol lidar ratio and extinction coefficient were quantified. The campaign was divided in three different periods based on the vehicular emission peak in the early mornings, namely, before, during, and after the emission peak. For before and after the emission peak data classification, aerosol properties presented closer values; however, large variability was obtained after the emission peak reaching the maximum values of extinction and a lidar ratio up to 51.5 ± 11.9 and 36.0 ± 10.5 sr, respectively. During the emission peaks, the values reached for extinction and lidar ratio were up to 136.8 ± 26.5 and 119.0 ± 22.7 sr, respectively.
Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Recent Progress in Atmospheric Remote Sensing)
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Open AccessArticle
Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments
by
Narmilan Amarasingam, Fernando Vanegas, Melissa Hele, Angus Warfield and Felipe Gonzalez
Remote Sens. 2024, 16(9), 1582; https://doi.org/10.3390/rs16091582 - 29 Apr 2024
Abstract
The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML)
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The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species.
Full article
(This article belongs to the Special Issue Remote Sensing for Management of Invasive Species)
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Open AccessArticle
Vertical Profiles of PM2.5 and O3 Measured Using an Unmanned Aerial Vehicle (UAV) and Their Relationships with Synoptic- and Local-Scale Air Movements
by
Hyemin Hwang, Ju Eun Lee, Seung A. Shin, Chae Rim You, Su Hyun Shin, Jong-Sung Park and Jae Young Lee
Remote Sens. 2024, 16(9), 1581; https://doi.org/10.3390/rs16091581 - 29 Apr 2024
Abstract
The vertical air pollutant concentrations and their relationships with synoptic- and local-scale air movement have been studied. This study measured the vertical profiles of PM2.5 and O3 using an unmanned aerial vehicle during summer in South Korea and analyzed the characteristics
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The vertical air pollutant concentrations and their relationships with synoptic- and local-scale air movement have been studied. This study measured the vertical profiles of PM2.5 and O3 using an unmanned aerial vehicle during summer in South Korea and analyzed the characteristics of the measured profiles. To understand the impact of synoptic air movements, we generated and categorized the 48 h air trajectories based on HYSPLIT, and we analyzed how the vertical profiles varied under different categories of long-range transport. We found that the vertical PM2.5 concentration has a positive gradient with altitude when more polluted air was transported from China or North Korea and has negative gradient when cleaner air was transported from the East Sea. Unlike PM2.5, the O3 concentration did not depend significantly on the long-range transport scenario because of the short photochemical lifetime of O3 during summer. For local-scale air movements, we found no significant impact of local wind on the measured profiles.
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(This article belongs to the Special Issue Drone Remote Sensing II)
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Open AccessArticle
Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems
by
Fan Yang, Chao Liu, Qianqian Chen, Jianbin Lai and Tiegang Liu
Remote Sens. 2024, 16(9), 1580; https://doi.org/10.3390/rs16091580 - 29 Apr 2024
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Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of
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Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of vegetation phenology and gross primary productivity (GPP) across the source region of the Yellow River (SRYR) by analyzing MODIS-derived vegetation phenology and GPP from 2001 to 2019, and explored how vegetation phenology and maximum carbon uptake capacity (GPPmax) affected seasonal GPP over the region. Our results showed that the SRYR experienced significantly advanced trends (p < 0.05) for both start (SOS) and peak (POS) of the growing season from 2001 to 2019. Spring GPP (GPPspr) had a significantly increasing trend (p < 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p < 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p < 0.01), respectively. It was found that an earlier spring-summer phenology and higher photosynthetic peak enhanced the photosynthetic efficiency of vegetation in spring and summer and altered the seasonal patterns of vegetation productivity in the SRYR under warming and wetting climates. This study indicated that not only spring and autumn phenology but also summer phenology and maximum carbon uptake capacity should be regarded as crucial indicators regulating the carbon uptake process in alpine ecosystems. This research provides important information about how changes in phenology affect vegetation productivity in alpine ecosystems under global climate warming.
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Open AccessArticle
Innovative Decision Fusion for Accurate Crop/Vegetation Classification with Multiple Classifiers and Multisource Remote Sensing Data
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Shuang Shuai, Zhi Zhang, Tian Zhang, Wei Luo, Li Tan, Xiang Duan and Jie Wu
Remote Sens. 2024, 16(9), 1579; https://doi.org/10.3390/rs16091579 - 29 Apr 2024
Abstract
Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize
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Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.
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(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle
Salinity Fronts in the South Atlantic
by
Igor M. Belkin and Xin-Tang Shen
Remote Sens. 2024, 16(9), 1578; https://doi.org/10.3390/rs16091578 - 29 Apr 2024
Abstract
Monthly climatology data for salinity fronts in the South Atlantic have been created from satellite SMOS sea surface salinity (SSS) measurements taken from 2011–2019, processed at the Barcelona Expert Center of Remote Sensing (BEC), and provided as high-resolution (1/20°) daily SSS data. The
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Monthly climatology data for salinity fronts in the South Atlantic have been created from satellite SMOS sea surface salinity (SSS) measurements taken from 2011–2019, processed at the Barcelona Expert Center of Remote Sensing (BEC), and provided as high-resolution (1/20°) daily SSS data. The SSS fronts have been identified with narrow zones of enhanced horizontal gradient magnitude (GM) of SSS, computed using the Belkin–O’Reilly algorithm (BOA). The SSS gradient fields generated by the BOA have been log-transformed to facilitate feature recognition. The log-transformation of SSS gradients markedly improved the visual contrast of gradient maps, which in turn allowed new features to be revealed and previously known features to be documented with a monthly temporal resolution and a mesoscale (~100 km) spatial resolution. Monthly climatologies were generated and analyzed for large-scale open-ocean SSS fronts and for low-salinity regions maintained by the Rio de la Plata discharge, Magellan Strait outflow, Congo River discharge, and Benguela Upwelling. A 2000 km-long triangular area between Africa and Brazil was found to be filled with regular quasi-meridional mesoscale striations that form a giant ripple field with a 100 km wave length. South of the Tropical Front, within the subtropical high-salinity pool, a trans-ocean quasi-zonal narrow linear belt of meridional SSS maximum (Smax) was documented. The meridional Smax belt shifts north–south seasonally while retaining its well-defined linear morphology, which is suggestive of a yet unidentified mechanism that maintains this feature. The Subtropical Frontal Zone (STFZ) consists of two tenuously connected fronts, western and eastern. The Brazil Current Front (BCF) extends SE between 40 and 45°S to join the subantarctic front (SAF). The STFZ trends NW–SE across the South Atlantic, seemingly merging with the SAF/BCF south of Africa to form a single front between 40 and 45°S. In the SW Atlantic, the Rio de la Plata plume migrates seasonally, expanding northward in winter (June–July) from 39°S into the South Brazilian Bight, up to Cabo Frio (23°S) and beyond. The inner Plata front moves in and out seasonally. Farther south, the Magellan Strait outflow expands northward in winter (June–July) from 53°S up to 39–40°S to nearly join the Plata outflow. In the SE Atlantic, the Congo River plume spreads radially from the river mouth, with the spreading direction varying seasonally. The plume is often bordered from the south by a quasi-zonal front along 6°S. The diluted Congo River water spreads southward seasonally down to the Angola–Benguela Front at 16°S. The Benguela Upwelling is delineated by a meridional front, which extends north alongshore up to 20°S, where the low-salinity Benguela Upwelling water forms a salinity front, which is separate from the thermal Angola–Benguela Front at 16°S. The high-salinity tropical water (“Angola water”) forms a wedge between the low-salinity waters of the Congo River outflow and Benguela Upwelling. This high-salinity wedge is bordered by salinity fronts that migrate north–south seasonally.
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(This article belongs to the Special Issue Advances in Remote Sensing of Ocean Salinity)
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Open AccessArticle
Intermittency of Gravity Wave Potential Energy Generated by Mountains Revealed from COSMIC-2 Observations
by
Jiarui Wei, Jiyao Xu and Xiao Liu
Remote Sens. 2024, 16(9), 1577; https://doi.org/10.3390/rs16091577 - 29 Apr 2024
Abstract
The intermittency of gravity wave potential energy (GWPE) in the upper troposphere and stratosphere was investigated using the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) temperature data over three typical mountains (Tibetan Plateau, Rocky Mountains, and Andes). These typical mountains have
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The intermittency of gravity wave potential energy (GWPE) in the upper troposphere and stratosphere was investigated using the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) temperature data over three typical mountains (Tibetan Plateau, Rocky Mountains, and Andes). These typical mountains have high sea level elevations but different land–sea contrast. The probability density function (PDF) of GWPE has the independent variable of GWPE and dependent variable of occurrence probability of GWPE over a region. Our analysis showed that the PDFs of GWPE over these three mountains roughly followed lognormal distributions in all heights and months. But, the key parameters (mean value and standard deviation) of lognormal distribution varied with heights and months. Above each mountain, the two key parameters exhibited similar temporal and spatial distributions. They had the largest values around the tropopause region, smaller values in the lower stratosphere (~20–30 km), and larger values in the upper stratosphere (~35–45 km). The intermittency of GWs is represented as the ratio of the GWPE at 50th percentile to the GWPE at 90th percentile. The weakest intermittency was at ~20–30 km (above the zonal mean winds of zero) over the Tibetan Plateau and Rocky Mountains in all months and over the Andes from November to March, respectively. Generally, the weakest intermittency (~0.4) occurred in the region where the key parameters were the smallest around summer. The key parameters of lognormal distribution were dominated by annual variation over the Andes throughout the height range, 8–50 km. However, the semiannual variations are also significant in the lower stratosphere over the Tibetan Plateau and Rocky Mountains. The seasonal variations in the intermittency were not as obvious as those of the key parameters. The lognormal distributions and the intermittencies derived here provide an observational constraint on the tunable parameters in GW parameterization schemes.
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(This article belongs to the Section Atmospheric Remote Sensing)
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Open AccessArticle
Improvement of Aerosol Coarse-Mode Detection through Additional Use of Infrared Wavelengths in the Inversion of Arctic Lidar Data
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Christine Böckmann, Christoph Ritter and Sandra Graßl
Remote Sens. 2024, 16(9), 1576; https://doi.org/10.3390/rs16091576 - 29 Apr 2024
Abstract
An Nd:YAG-based Raman lidar provides a mature technology to derive profiles of the optical properties of aerosols over a wide altitude range. However, the derivation of micro-physical parameters is an ill-posed problem. Hence, increasing the information content of lidar data is desirable. Recently,
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An Nd:YAG-based Raman lidar provides a mature technology to derive profiles of the optical properties of aerosols over a wide altitude range. However, the derivation of micro-physical parameters is an ill-posed problem. Hence, increasing the information content of lidar data is desirable. Recently, ceilometers and wind lidar systems, both operating in the near-infrared region, have been successfully employed in aerosol research. In this study, we demonstrate that the inclusion of additional backscatter coefficients from these two latter instruments clearly improves the inversion of micro-physical parameters such as volume distribution function, effective radius, or single-scattering albedo. We focus on the Arctic aerosol and start with the typical volume distribution functions of Arctic haze and boreal biomass burning. We forward calculate the optical coefficients that the lidar systems should have seen and include or exclude the backscatter coefficients of the ceilometer (910 nm) and wind lidar data (1500 nm) to analyze the value of these wavelengths in their ability to reproduce the volume distribution function, which may be mono- or bimodal. We found that not only the coarse mode but also the properties of the accumulation mode improved when the additional wavelengths were considered. Generally, the 1500 nm wavelength has greater value in correctly reproducing the aerosol properties.
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(This article belongs to the Special Issue Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements)
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Open AccessTechnical Note
Occurrence Characteristics of Nighttime Merged EIA Based on NASA GOLD Observations from 2018 to 2023
by
Kun Wu and Liying Qian
Remote Sens. 2024, 16(9), 1575; https://doi.org/10.3390/rs16091575 - 29 Apr 2024
Abstract
The ionosphere equatorial ionization anomaly (EIA) is usually characterized by two plasma density maxima in the Earth’s equatorial region. Merged EIA (MEIA) is a unique phenomenon in the evolution of the EIA. Currently, the occurrence characteristics of MEIA are still not well understood.
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The ionosphere equatorial ionization anomaly (EIA) is usually characterized by two plasma density maxima in the Earth’s equatorial region. Merged EIA (MEIA) is a unique phenomenon in the evolution of the EIA. Currently, the occurrence characteristics of MEIA are still not well understood. In this study, we investigate the occurrence characteristics of nighttime MEIA using NASA Global-scale Observations of the Limb and Disk (GOLD) observations between October 2018 and the end of 2023. We found that the occurrence of nighttime MEIA exhibits solar cycle, seasonal, and local time variations. The occurrence rate of the MEIA is inversely dependent on solar activity. Occurrence of the MEIA maximizes near the equinoxes, with a primary (secondary) low occurrence rate near the June (December) solstice. In addition, occurrences of the MEIA are suppressed during the pre-reversal enhancement (PRE), resulting in relatively fewer events. Furthermore, it was found that the occurrence of the MEIA is not significantly dependent on the strength of geomagnetic activity. As far as we know, this study represents the first instance of utilizing observations from GOLD observations to investigate the characteristics of MEIA occurrences and their correlations with solar activity, season, and local time.
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(This article belongs to the Special Issue Applications of Remote Sensing in Monitoring Ionospheric and Atmospheric Physics)
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Open AccessArticle
AFMUNet: Attention Feature Fusion Network Based on a U-Shaped Structure for Cloud and Cloud Shadow Detection
by
Wenjie Du, Zhiyong Fan, Ying Yan, Rui Yu and Jiazheng Liu
Remote Sens. 2024, 16(9), 1574; https://doi.org/10.3390/rs16091574 - 28 Apr 2024
Abstract
Cloud detection technology is crucial in remote sensing image processing. While cloud detection is a mature research field, challenges persist in detecting clouds on reflective surfaces like ice, snow, and sand. Particularly, the detection of cloud shadows remains a significant area of concern
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Cloud detection technology is crucial in remote sensing image processing. While cloud detection is a mature research field, challenges persist in detecting clouds on reflective surfaces like ice, snow, and sand. Particularly, the detection of cloud shadows remains a significant area of concern within cloud detection technology. To address the above problems, a convolutional self-attention mechanism feature fusion network model based on a U-shaped structure is proposed. The model employs an encoder–decoder structure based on UNet. The encoder performs down-sampling to extract deep features, while the decoder uses up-sampling to reconstruct the feature map. To capture the key features of the image, Channel Spatial Attention Module (CSAM) is introduced in this work. This module incorporates an attention mechanism for adaptive field-of-view adjustments. In the up-sampling process, different channels are selected to obtain rich information. Contextual information is integrated to improve the extraction of edge details. Feature fusion at the same layer between up-sampling and down-sampling is carried out. The Feature Fusion Module (FFM) facilitates the positional distribution of the image on a pixel-by-pixel basis. A clear boundary is distinguished using an innovative loss function. Finally, the experimental results on the dataset GF1_WHU show that the segmentation results of this method are better than the existing methods. Hence, our model is of great significance for practical cloud shadow segmentation.
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(This article belongs to the Special Issue Remote Sensing Image Classification and Semantic Segmentation)
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Open AccessArticle
Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellite Time Series Data
by
Johann Desloires, Dino Ienco and Antoine Botrel
Remote Sens. 2024, 16(9), 1573; https://doi.org/10.3390/rs16091573 - 28 Apr 2024
Abstract
Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed.
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Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 reanalysis dataset. Resampling the time series over thermal time significantly enhances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2. Finally, we propose a new two-stage machine learning strategy to mitigate early season partially available data. It consists in extending the current time series on the basis of complete historical data and adapting the model inference according to the crop progress.
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(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)
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Open AccessArticle
SCRP-Radar: Space-Aware Coordinate Representation for Human Pose Estimation Based on SISO UWB Radar
by
Xiaolong Zhou, Tian Jin, Yongpeng Dai, Yongping Song and Kemeng Li
Remote Sens. 2024, 16(9), 1572; https://doi.org/10.3390/rs16091572 - 28 Apr 2024
Abstract
Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology
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Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology offers a non-invasive, lighting-insensitive solution that preserves user privacy. This paper presents a novel radar-based framework for HPE, SCRP-Radar (space-aware coordinate representation for human pose estimation using single-input single-output (SISO) ultra-wideband (UWB) radar). The methodology begins with clutter suppression and denoising techniques to enhance the quality of radar echo signals, followed by the construction of a micro-Doppler (MD) matrix from these refined signals. This matrix is segmented into bins to extract distinctive features that are critical for pose estimation. The SCRP-Radar leverages the Hrnet and LiteHrnet networks, incorporating space-aware coordinate representation to reconstruct 2D human poses with high precision. Our method redefines HPE as dual classification tasks for vertical and horizontal coordinates, which is a significant departure from existing methods such as RF-Pose, RF-Pose 3D, UWB-Pose, and RadarFormer. Extensive experimental evaluations demonstrate that SCRP-Radar significantly surpasses these methods in accuracy and robustness, consistently exhibiting lower average error rates, achieving less than 40 mm across 17 skeletal key-points. This innovative approach not only enhances the precision of radar-based HPE but also sets a new benchmark for future research and application, particularly in sectors that benefit from accurate and privacy-preserving monitoring technologies.
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(This article belongs to the Special Issue State-of-the-Art and Future Developments: Short-Range Radar)
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Annual and Seasonal Variations in Aerosol Optical Characteristics in the Huai River Basin, China from 2007 to 2021
by
Xu Deng, Chenbo Xie, Dong Liu and Yingjian Wang
Remote Sens. 2024, 16(9), 1571; https://doi.org/10.3390/rs16091571 - 28 Apr 2024
Abstract
Over the past three decades, China has seen aerosol levels substantially surpass the global average, significantly impacting regional climate. This study investigates the long-term and seasonal variations of aerosols in the Huai River Basin (HRB) using MODIS, CALIOP observations from 2007 to 2021,
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Over the past three decades, China has seen aerosol levels substantially surpass the global average, significantly impacting regional climate. This study investigates the long-term and seasonal variations of aerosols in the Huai River Basin (HRB) using MODIS, CALIOP observations from 2007 to 2021, and ground-based measurements. A notable finding is a significant decline in the annual mean Aerosol Optical Depth (AOD) across the HRB, with MODIS showing a decrease of approximately 0.023 to 0.027 per year, while CALIOP, which misses thin aerosol layers, recorded a decrease of about 0.016 per year. This downward trend is corroborated by improvements in air quality, as evidenced by PM2.5 measurements and visibility-based aerosol extinction coefficients. Aerosol decreases occurred at all heights, but for aerosols below 800 m, with an annual AOD decrease of 0.011. The study also quantifies the long-term trends of five major aerosol types, identifying Polluted Dust (PD) as the predominant frequency type (46%), which has significantly decreased, contributing to about 68% of the total AOD reduction observed by CALIOP (0.011 per year). Despite this, Dust and Polluted Continental (PC) aerosols persist, with PC showing no clear trend of decrease. Seasonal analysis reveals aerosol peaks in summer, contrary to surface measurements, attributed to variations in the Boundary Layer (BL) depth, affecting aerosol distribution and extinction. Furthermore, the study explores the influence of seasonal wind patterns on aerosol type variation, noting that shifts in wind direction contribute to the observed changes in aerosol types, particularly affecting Dust and PD occurrences. The integration of satellite and ground measurements provides a comprehensive view of regional aerosol properties, highlighting the effectiveness of China’s environmental policies in aerosol reduction. Nonetheless, the persistence of high PD and PC levels underscores the need for continued efforts to reduce both primary and secondary aerosol production to further enhance regional air quality.
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(This article belongs to the Section Atmospheric Remote Sensing)
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