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Volume 16
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Remote Sens., Volume 16, Issue 14 (July-2 2024) – 177 articles

Cover Story (view full-size image): Web-GIS applications represent useful tools for processing and analyzing geographic data from various sources. Here, we present ARCHIMEDE, a Web-GIS platform that collects various datasets about Mediterranean tropical-like cyclones and hurricanes. These datasets comprise climatic and oceanographic data obtained from remote sensing techniques as well as seismic and geomorphological data obtained from field observations. ARCHIMEDE provides a valuable support for the development of robust coastal management strategies aimed at mitigating the challenges posed by Mediterranean hurricanes. View this paper
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19 pages, 5597 KiB  
Article
Spatiotemporal Feature Fusion Transformer for Precipitation Nowcasting via Feature Crossing
by Taisong Xiong, Weiping Wang, Jianxin He, Rui Su, Hao Wang and Jinrong Hu
Remote Sens. 2024, 16(14), 2685; https://doi.org/10.3390/rs16142685 - 22 Jul 2024
Cited by 1 | Viewed by 1138
Abstract
Precipitation nowcasting plays an important role in mitigating the damage caused by severe weather. The objective of precipitation nowcasting is to forecast the weather conditions 0–2 h ahead. Traditional models based on numerical weather prediction and radar echo extrapolation obtain relatively better results. [...] Read more.
Precipitation nowcasting plays an important role in mitigating the damage caused by severe weather. The objective of precipitation nowcasting is to forecast the weather conditions 0–2 h ahead. Traditional models based on numerical weather prediction and radar echo extrapolation obtain relatively better results. In recent years, models based on deep learning have also been applied to precipitation nowcasting and have shown improvement. However, the forecast accuracy is decreased with longer forecast times and higher intensities. To mitigate the shortcomings of existing models for precipitation nowcasting, we propose a novel model that fuses spatiotemporal features for precipitation nowcasting. The proposed model uses an encoder–forecaster framework that is similar to U-Net. First, in the encoder, we propose a spatial and temporal multi-head squared attention module based on MaxPool and AveragePool to capture every independent sequence feature, as well as a global spatial and temporal feedforward network, to learn the global and long-distance relationships between whole spatiotemporal sequences. Second, we propose a cross-feature fusion strategy to enhance the interactions between features. This strategy is applied to the components of the forecaster. Based on the cross-feature fusion strategy, we constructed a novel multi-head squared cross-feature fusion attention module and cross-feature fusion feedforward network in the forecaster. Comprehensive experimental results demonstrated that the proposed model more effectively forecasted high-intensity levels than other models. These results prove the effectiveness of the proposed model in terms of predicting convective weather. This indicates that our proposed model provides a feasible solution for precipitation nowcasting. Extensive experiments also proved the effectiveness of the components of the proposed model. Full article
(This article belongs to the Special Issue Deep Learning Techniques Applied in Remote Sensing)
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18 pages, 16686 KiB  
Article
Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images
by Konstantin Nahrstedt, Tobias Reuter, Dieter Trautz, Björn Waske and Thomas Jarmer
Remote Sens. 2024, 16(14), 2684; https://doi.org/10.3390/rs16142684 - 22 Jul 2024
Cited by 1 | Viewed by 924
Abstract
In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of [...] Read more.
In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures. Full article
(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing II)
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18 pages, 11045 KiB  
Article
Weak-Texture Seafloor and Land Image Matching Using Homography-Based Motion Statistics with Epipolar Geometry
by Yifu Chen, Yuan Le, Lin Wu, Dongfang Zhang, Qian Zhao, Xueman Zhang and Lu Liu
Remote Sens. 2024, 16(14), 2683; https://doi.org/10.3390/rs16142683 - 22 Jul 2024
Cited by 1 | Viewed by 1015
Abstract
The matching of remote sensing images is a critical and necessary procedure that directly impacts the correctness and accuracy of underwater topography, change detection, digital elevation model (DEM) generation, and object detection. The texture of images becomes weaker with increasing water depth, and [...] Read more.
The matching of remote sensing images is a critical and necessary procedure that directly impacts the correctness and accuracy of underwater topography, change detection, digital elevation model (DEM) generation, and object detection. The texture of images becomes weaker with increasing water depth, and this results in matching-extraction failure. To address this issue, a novel method, homography-based motion statistics with an epipolar constraint (HMSEC), is proposed to improve the number, reliability, and robustness of matching points for weak-textured seafloor images. In the matching process of HMSEC, a large number of reliable matching points can be identified from the preliminary matching points based on the motion smoothness assumption and motion statistics. Homography and epipolar geometry are also used to estimate the scale and rotation influences of each matching point in image pairs. The results show that the matching-point numbers for the seafloor and land regions can be significantly improved. In this study, we evaluated this method for the areas of Zhaoshu Island, Ganquan Island, and Lingyang Reef and compared the results to those of the grid-based motion statistics (GMS) method. The increment of matching points reached 2672, 2767, and 1346, respectively. In addition, the seafloor matching points had a wider distribution and reached greater water depths of −11.66, −14.06, and −9.61 m. These results indicate that the proposed method could significantly improve the number and reliability of matching points for seafloor images. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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24 pages, 23818 KiB  
Article
Effects of Assimilating Ground-Based Microwave Radiometer and FY-3D MWTS-2/MWHS-2 Data in Precipitation Forecasting
by Bingli Wang, Wei Cheng, Yansong Bao, Shudong Wang, George P. Petropoulos, Shuiyong Fan, Jiajia Mao, Ziqi Jin and Zihui Yang
Remote Sens. 2024, 16(14), 2682; https://doi.org/10.3390/rs16142682 - 22 Jul 2024
Viewed by 956
Abstract
This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted [...] Read more.
This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted in this study, concerning a heavy precipitation event in Beijing on 2 July 2021, and 10-day batch experiments were also conducted. The key study findings include the following: (1) Both ground-based microwave radiometer and MWTS-2/MWHS-2 data contribute to improvements in the initial fields of the model, leading to appropriate adjustments in the thermal structure of the model. (2) The forecast fields of the experiments assimilating ground-based microwave radiometer and MWTS-2/MWHS-2 data show temperature and humidity performances closer to the true fields compared with the control experiment. (3) Separate assimilation of two types of microwave radiometer data can improve precipitation forecasts, while joint assimilation provides the most accurate forecasts among all the experiments. In the single-case, compared with the control experiment, the individual and combined assimilation of MWR and MWTS-2/MWHS-2 improves the six-hour cumulative precipitation threat score (TS) at the 25 mm level by 57.1%, 28.9%, and 38.2%, respectively. The combined assimilation also improves the scores at the 50 mm level by 54.4%, whereas individual assimilations show a decrease in performance. In the batch experiments, the MWR_FY experiment’s TS of 24 h precipitation forecast improves 28.5% at 10 mm and 330% at 25 mm based on the CTRL. Full article
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18 pages, 5839 KiB  
Article
Digital Mapping and Scenario Prediction of Soil Salinity in Coastal Lands Based on Multi-Source Data Combined with Machine Learning Algorithms
by Mengge Zhou and Yonghua Li
Remote Sens. 2024, 16(14), 2681; https://doi.org/10.3390/rs16142681 - 22 Jul 2024
Cited by 1 | Viewed by 1453
Abstract
Salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted [...] Read more.
Salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, and Sentinel-2) was used to compare the predictive performance of empirical bayesian kriging regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training and testing datasets, with an average MAE of 1.86, an average RMSE of 3.11, and an average R2 of 0.59 in the testing datasets. Among explanatory factors, soil Na was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC1:5 predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline–alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline–alkali land quality and plan regional agricultural development. Full article
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22 pages, 937 KiB  
Article
Radar Emitter Recognition Based on Spiking Neural Networks
by Zhenghao Luo, Xingdong Wang, Shuo Yuan and Zhangmeng Liu
Remote Sens. 2024, 16(14), 2680; https://doi.org/10.3390/rs16142680 - 22 Jul 2024
Cited by 2 | Viewed by 1300
Abstract
Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) [...] Read more.
Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods. Full article
(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)
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18 pages, 11915 KiB  
Article
Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery
by Freda Dorbu and Leila Hashemi-Beni
Remote Sens. 2024, 16(14), 2679; https://doi.org/10.3390/rs16142679 - 22 Jul 2024
Cited by 1 | Viewed by 1122
Abstract
Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an [...] Read more.
Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level. Full article
(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)
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20 pages, 11907 KiB  
Article
Precise Motion Compensation of Multi-Rotor UAV-Borne SAR Based on Improved PTA
by Yao Cheng, Xiaolan Qiu and Dadi Meng
Remote Sens. 2024, 16(14), 2678; https://doi.org/10.3390/rs16142678 - 22 Jul 2024
Viewed by 923
Abstract
In recent years, with the miniaturization of high-precision position and orientation systems (POS), precise motion errors during SAR data collection can be calculated based on high-precision POS. However, compensating for these errors remains a significant challenge for multi-rotor UAV-borne SAR systems. Compared with [...] Read more.
In recent years, with the miniaturization of high-precision position and orientation systems (POS), precise motion errors during SAR data collection can be calculated based on high-precision POS. However, compensating for these errors remains a significant challenge for multi-rotor UAV-borne SAR systems. Compared with large aircrafts, multi-rotor UAVs are lighter, slower, have more complex flight trajectories, and have larger squint angles, which result in significant differences in motion errors between building targets and ground targets. If the motion compensation is based on ground elevation, the motion error of the ground target will be fully compensated, but the building target will still have a large residual error; as a result, although the ground targets can be well-focused, the building targets may be severely defocused. Therefore, it is necessary to further compensate for the residual motion error of building targets based on the actual elevation on the SAR image. However, uncompensated errors will affect the time–frequency relationship; furthermore, the ω-k algorithm will further change these errors, resulting in errors in SAR images becoming even more complex and difficult to compensate for. To solve this problem, this paper proposes a novel improved precise topography and aperture-dependent (PTA) method that can precisely compensate for motion errors in the UAV-borne SAR system. After motion compensation and imaging processing based on ground elevation, a secondary focus is applied to defocused buildings. The improved PTA fully considers the coupling of the residual error with the time–frequency relationship and ω-k algorithm, and the precise errors in the two-dimensional frequency domain are determined through numerical calculations without any approximations. Simulation and actual data processing verify the effectiveness of the method, and the experimental results show that the proposed method in this paper is better than the traditional method. Full article
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53 pages, 21900 KiB  
Article
Multi-Tier Land Use and Land Cover Mapping Framework and Its Application in Urbanization Analysis in Three African Countries
by Shahriar Shah Heydari, Jody C. Vogeler, Orion S. E. Cardenas-Ritzert, Steven K. Filippelli, Melissa McHale and Melinda Laituri
Remote Sens. 2024, 16(14), 2677; https://doi.org/10.3390/rs16142677 - 22 Jul 2024
Cited by 2 | Viewed by 1315
Abstract
The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa’s anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform [...] Read more.
The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa’s anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous “developed” or “built-up” area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. Full article
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24 pages, 16124 KiB  
Article
Estimation of Actual Evapotranspiration and Water Stress in Typical Irrigation Areas in Xinjiang, Northwest China
by Siyu Zhao, Yue Huang, Zhibin Liu, Tie Liu and Xiaoyu Tang
Remote Sens. 2024, 16(14), 2676; https://doi.org/10.3390/rs16142676 - 22 Jul 2024
Cited by 1 | Viewed by 1242
Abstract
The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, [...] Read more.
The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas. Full article
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18 pages, 15194 KiB  
Article
Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework
by Tianhao Liu, Mingdong Zang, Jianbing Peng and Chong Xu
Remote Sens. 2024, 16(14), 2675; https://doi.org/10.3390/rs16142675 - 22 Jul 2024
Viewed by 1083
Abstract
Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. [...] Read more.
Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the receiver operating characteristic (ROC) curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high-susceptibility areas for coseismic landslides. This study explores factors influencing model fit, revealing that the inclusion of the epicentral distance and the distance to the seismogenic fault in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to the seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to the seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations. Full article
(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)
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23 pages, 16859 KiB  
Article
Modelling and Mitigating Wind Turbine Clutter in Space–Air Bistatic Radar
by Shuo Zhang, Shuangxi Zhang, Ning Qiao, Yongliang Wang and Qinglei Du
Remote Sens. 2024, 16(14), 2674; https://doi.org/10.3390/rs16142674 - 22 Jul 2024
Cited by 1 | Viewed by 1125
Abstract
The extensive deployment of wind farms has significantly impacted the detection capabilities of space–air bistatic radar (SABR) systems. Although space–time adaptive processing techniques are available, their performance is significantly degraded, and even unable to suppress clutter. This paper explores the geometric configuration of [...] Read more.
The extensive deployment of wind farms has significantly impacted the detection capabilities of space–air bistatic radar (SABR) systems. Although space–time adaptive processing techniques are available, their performance is significantly degraded, and even unable to suppress clutter. This paper explores the geometric configuration of the SABR system and the selection of detection areas, establishing a space–time clutter model that addresses the effects of wind turbine clutter (WTC). Expressions for spatial and Doppler frequencies have been derived to deeply analyze the characteristics of clutter spreading. Building on this, the paper extends two-dimensional space–time data to three-dimensional azimuth–elevation–Doppler data. It proposes a three-dimensional space–time multi-beam (STMB) strategy incorporating the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to suppress WTC effectively. This algorithm selects WTC samples and applies OPTICS clustering to the clutter-suppressed data to achieve this effect. Simulation experiments further verify the effectiveness of the algorithm. Full article
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21 pages, 15805 KiB  
Article
Wet-ConViT: A Hybrid Convolutional–Transformer Model for Efficient Wetland Classification Using Satellite Data
by Ali Radman, Fariba Mohammadimanesh and Masoud Mahdianpari
Remote Sens. 2024, 16(14), 2673; https://doi.org/10.3390/rs16142673 - 22 Jul 2024
Cited by 1 | Viewed by 1543
Abstract
Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, [...] Read more.
Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constrained in practical issues regarding efficiency while gaining high accuracy with limited training ground truth samples. To address these limitations, in this study, a novel deep learning model, namely Wet-ConViT, is designed for the precise mapping of wetlands using multi-source satellite data, combining the strengths of multispectral Sentinel-2 and SAR Sentinel-1 datasets. Both capturing local information of convolution and the long-range feature extraction capabilities of transformers are considered within the proposed architecture. Specifically, the key to Wet-ConViT’s foundation is the multi-head convolutional attention (MHCA) module that integrates convolutional operations into a transformer attention mechanism. By leveraging convolutions, MHCA optimizes the efficiency of the original transformer self-attention mechanism. This resulted in high-precision land cover classification accuracy with a minimal computational complexity compared with other state-of-the-art models, including two convolutional neural networks (CNNs), two transformers, and two hybrid CNN–transformer models. In particular, Wet-ConViT demonstrated superior performance for classifying land cover with approximately 95% overall accuracy metrics, excelling the next best model, hybrid CoAtNet, by about 2%. The results highlighted the proposed architecture’s high precision and efficiency in terms of parameters, memory usage, and processing time. Wet-ConViT could be useful for practical wetland mapping tasks, where precision and computational efficiency are paramount. Full article
(This article belongs to the Special Issue Satellite-Based Climate Change and Sustainability Studies)
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18 pages, 10628 KiB  
Article
A CNN- and Self-Attention-Based Maize Growth Stage Recognition Method and Platform from UAV Orthophoto Images
by Xindong Ni, Faming Wang, Hao Huang, Ling Wang, Changkai Wen and Du Chen
Remote Sens. 2024, 16(14), 2672; https://doi.org/10.3390/rs16142672 - 22 Jul 2024
Cited by 4 | Viewed by 1098
Abstract
The accurate recognition of maize growth stages is crucial for effective farmland management strategies. In order to overcome the difficulty of quickly obtaining precise information about maize growth stage in complex farmland scenarios, this study proposes a Maize Hybrid Vision Transformer (MaizeHT) that [...] Read more.
The accurate recognition of maize growth stages is crucial for effective farmland management strategies. In order to overcome the difficulty of quickly obtaining precise information about maize growth stage in complex farmland scenarios, this study proposes a Maize Hybrid Vision Transformer (MaizeHT) that combines a convolutional algorithmic structure with self-attention for maize growth stage recognition. The MaizeHT model utilizes a ResNet34 convolutional neural network to extract image features to self-attention, which are then transformed into sequence vectors (tokens) using Patch Embedding. It simultaneously inserts category information and location information as a token. A Transformer architecture with multi-head self-attention is employed to extract token features and predict maize growth stage categories using a linear layer. In addition, the MaizeHT model is standardized and encapsulated, and a prototype platform for intelligent maize growth stage recognition is developed for deployment on a website. Finally, the performance validation test of MaizeHT was carried out. To be specific, MaizeHT has an accuracy of 97.71% when the input image resolution is 224 × 224 and 98.71% when the input image resolution is 512 × 512 on the self-built dataset, the number of parameters is 15.446 M, and the floating-point operations are 4.148 G. The proposed maize growth stage recognition method could provide computational support for maize farm intelligence. Full article
(This article belongs to the Special Issue Object Detection and Information Extraction Based on Remote Sensing Imagery)
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21 pages, 6948 KiB  
Article
Has IMERG_V07 Improved the Precision of Precipitation Retrieval in Mainland China Compared to IMERG_V06?
by Hao Guo, Yunfei Tian, Junli Li, Chunrui Guo, Xiangchen Meng, Wei Wang and Philippe De Maeyer
Remote Sens. 2024, 16(14), 2671; https://doi.org/10.3390/rs16142671 - 22 Jul 2024
Cited by 2 | Viewed by 969
Abstract
Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from [...] Read more.
Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG. Full article
(This article belongs to the Topic Applications of Geodesy in Meteorological, Hydrological and Climatic Environments)
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22 pages, 3924 KiB  
Article
Diurnal Variation in Surface Incident Solar Radiation Retrieved by CERES and Himawari-8
by Lu Lu, Ying Li, Lingjun Liang and Qian Ma
Remote Sens. 2024, 16(14), 2670; https://doi.org/10.3390/rs16142670 - 22 Jul 2024
Viewed by 865
Abstract
The diurnal variation of surface incident solar radiation (Rs) has a significant impact on the Earth’s climate. Satellite-retrieved Rs datasets display good spatial and temporal continuity compared with ground-based observations and, more importantly, have higher accuracy than reanalysis datasets. Facilitated by these advantages, [...] Read more.
The diurnal variation of surface incident solar radiation (Rs) has a significant impact on the Earth’s climate. Satellite-retrieved Rs datasets display good spatial and temporal continuity compared with ground-based observations and, more importantly, have higher accuracy than reanalysis datasets. Facilitated by these advantages, many scholars have evaluated satellite-retrieved Rs, especially based on monthly and annual data. However, there is a lack of evaluation on an hourly scale, which has a profound impact on sea–air interactions, climate change, agriculture, and prognostic models. This study evaluates Himawari-8 and Clouds and the Earth’s Radiant Energy System Synoptic (CERES)-retrieved hourly Rs data covering 60°S–60°N and 80°E–160°W based on ground-based observations from the Baseline Surface Radiation Network (BSRN). Hourly Rs were first standardized to remove the diurnal and seasonal cycles. Furthermore, the sensitivities of satellite-retrieved Rs products to clouds, aerosols, and land cover types were explored. It was found that Himawari-8-retrieved Rs was better than CERES-retrieved Rs at 8:00–16:00 and worse at 7:00 and 17:00. Both satellites performed better at continental sites than at island/coastal sites. The diurnal variations of statistical parameters of Himawari-8 satellite-retrieved Rs were stronger than those of CERES. Relatively larger MABs in the case of stratus and stratocumulus were exhibited for both hourly products. Smaller MAB values were found for CERES covered by deep convection and cumulus clouds and for Himawari-8 covered by deep convection and nimbostratus clouds. Larger MAB values at evergreen broadleaf forest sites and smaller MAB values at open shrubland sites were found for both products. In addition, Rs retrieved by Himawari-8 was more sensitive to AOD at 10:00–16:00, while that retrieved by CERES was more sensitive to COD at 9:00–15:00. The CERES product showed larger sensitivity to COD (at 9:00–15:00) and AOD (at 7:00–10:00) than Himawari-8. This work helps data producers know how to improve their future products and helps data users be aware of the uncertainties that exist in hourly satellite-retrieved Rs data. Full article
(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing (Second Edition))
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16 pages, 4007 KiB  
Technical Note
The Nighttime Horizontal Neutral Winds at Mohe Station in Response to the Temporal Oscillations of Interplanetary Magnetic Field Bz
by Kedeng Zhang, Hui Wang, Chunxin Zheng, Tiantian Yin and Zhenzhu Liu
Remote Sens. 2024, 16(14), 2669; https://doi.org/10.3390/rs16142669 - 22 Jul 2024
Viewed by 739
Abstract
Temporal oscillations in the IMF Bz associated with Alfvén waves occur frequently in solar wind, with a duration ranging from minutes to hours. Using Swarm observations, Fabry–Pérot interferometer measurements at Mohe station, and Thermosphere–Ionosphere–Electrodynamic General Circulation Model simulations, the perturbations of zonal (ΔUN) [...] Read more.
Temporal oscillations in the IMF Bz associated with Alfvén waves occur frequently in solar wind, with a duration ranging from minutes to hours. Using Swarm observations, Fabry–Pérot interferometer measurements at Mohe station, and Thermosphere–Ionosphere–Electrodynamic General Circulation Model simulations, the perturbations of zonal (ΔUN) and meridional (ΔVN) winds due to temporal oscillations in the IMF Bz on 23–24 April 2023 are explored in the following work. ΔUN is strong westward with a speed of greater than 100 m/s at pre-midnight on 23–24 April. This phenomenon is primarily driven by the pressure gradient, offsetting by the ion drag and Coriolis force. On 23 April, ΔVN is weak northward at the pre-midnight and strong southward at a speed of ~200 m/s at pre-dawn. On 24 April, ΔVN is strong (weak) northward at pre-midnight (pre-dawn). It is mainly controlled by a balance between the pressure gradient, ion drag, and Coriolis force. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Monitoring Ionospheric and Atmospheric Physics)
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16 pages, 18068 KiB  
Article
Multi-Wave Structures of Traveling Ionospheric Disturbances Associated with the 2022 Tonga Volcanic Eruptions in the New Zealand and Australia Regions
by Xiaolin Li, Feng Ding, Bo Xiong, Ge Chen, Tian Mao, Qian Song and Changhao Yu
Remote Sens. 2024, 16(14), 2668; https://doi.org/10.3390/rs16142668 - 21 Jul 2024
Viewed by 1033
Abstract
Using dense global navigation satellite system data and brightness temperature data across the New Zealand and Australia regions, we tracked the propagation of traveling ionospheric disturbances (TIDs) associated with the 15 January 2022 Tonga volcanic eruptions. We identified two shock wave-related TIDs and [...] Read more.
Using dense global navigation satellite system data and brightness temperature data across the New Zealand and Australia regions, we tracked the propagation of traveling ionospheric disturbances (TIDs) associated with the 15 January 2022 Tonga volcanic eruptions. We identified two shock wave-related TIDs and two Lamb wave-related TIDs following the eruptions. The two shock wave-related TIDs, propagating with velocities of 724–750 and 445–471 m/s, respectively, were observed around New Zealand and Australia within a distance of 3500–6500 km from the eruptive center. These shock wave-related TIDs suffered severe attenuation during the propagation and disappeared more than 6500 km from the eruptive center. Based on the TEC data from the nearest ground-based receivers, we estimated the onset times of two main volcanic explosions at 04:20:54 UT ± 116 s and 04:24:37 UT ± 141 s, respectively. The two shock wave-related TIDs were most likely generated by these two main volcanic eruptions. The two Lamb wave-related TIDs propagated with velocities of 300–370 and 250 m/s in the near-field region. The Lamb wave-related TIDs experienced minimal attenuation during their long-distance propagation, with only a 0.17% decrease observed in the relative amplitudes of the Lamb wave-related TIDs from the near-field to far-field regions. Full article
(This article belongs to the Special Issue Application of GNSS Remote Sensing in Ionosphere Monitoring)
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22 pages, 10559 KiB  
Article
Development of an Algorithm for Assessing the Scope of Large Forest Fire Using VIIRS-Based Data and Machine Learning
by Min-Woo Son, Chang-Gyun Kim and Byung-Sik Kim
Remote Sens. 2024, 16(14), 2667; https://doi.org/10.3390/rs16142667 - 21 Jul 2024
Cited by 1 | Viewed by 1811
Abstract
Forest fires pose a multifaceted threat, encompassing human lives and property loss, forest resource destruction, and toxic gas release. This crucial disaster’s global occurrence and impact have risen in recent years, primarily driven by climate change. Hence, the scope and frequency of forest [...] Read more.
Forest fires pose a multifaceted threat, encompassing human lives and property loss, forest resource destruction, and toxic gas release. This crucial disaster’s global occurrence and impact have risen in recent years, primarily driven by climate change. Hence, the scope and frequency of forest fires must be collected to establish disaster prevention policies and conduct relevant research projects. However, some countries do not share details, including the location of forest fires, which can make research problematic when it is necessary to know the exact location or shape of a forest fire. This non-disclosure warrants remote surveys of forest fire sites using satellites, which sidestep national information disclosure policies. Meanwhile, original data from satellites have a great advantage in terms of data acquisition in that they are independent of national information disclosure policies, making them the most effective method that can be used for environmental monitoring and disaster monitoring. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-Orbiting Partnership (NPP) satellite has worldwide coverage at a daily temporal resolution and spatial resolution of 375 m. It is widely used for detecting hotspots worldwide, enabling the recognition of forest fires and affected areas. However, information collection on affected regions and durations based on raw data necessitates identifying and filtering hotspots caused by industrial activities. Therefore, this study used VIIRS hotspot data collected over long periods and the Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to develop ST-MASK, which masks said hotspots. By targeting the concentrated and fixed nature of these hotspots, ST-MASK is developed and used to distinguish forest fires from other hotspots, even in mountainous areas, and through an outlier detection algorithm, it generates identified forest fire areas, which will ultimately allow for the creation of a global forest fire watch system. Full article
(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)
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23 pages, 6165 KiB  
Article
RIRNet: A Direction-Guided Post-Processing Network for Road Information Reasoning
by Guoyuan Zhou, Changxian He, Hao Wang, Qiuchang Xie, Qiong Chen, Liang Hong and Jie Chen
Remote Sens. 2024, 16(14), 2666; https://doi.org/10.3390/rs16142666 - 21 Jul 2024
Viewed by 897
Abstract
Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, [...] Read more.
Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The information inference module can infer spatial information relationships between different rows or columns of feature images from different directions, effectively inferring and repairing road fractures. The road direction inference task branch performs the road direction prediction task, which can constrain and promote the road extraction task, thereby indirectly enhancing the inference ability of the post-processing model and realizing the optimization of the initial road extraction results. Experimental results demonstrate that the RIRNet model can achieve an excellent post-processing effect, which is manifested in the effective repair of broken road segments, as well as the handling of errors such as omission, misclassification, and noise, proving the effectiveness and generalization of the model in post-processing optimization. Full article
(This article belongs to the Special Issue AI-Driven Mapping Using Remote Sensing Data)
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23 pages, 15641 KiB  
Article
Impacts of the Middle Route of the South-to-North Water Diversion Project on Land Surface Temperature and Fractional Vegetation Coverage in the Danjiang River Basin
by Shidong Wang, Yuanyuan Liu, Jianhua Guo, Jinping Liu and Huabin Chai
Remote Sens. 2024, 16(14), 2665; https://doi.org/10.3390/rs16142665 - 21 Jul 2024
Cited by 1 | Viewed by 1271
Abstract
The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional [...] Read more.
The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin. Full article
(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)
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14 pages, 5602 KiB  
Article
Surface Soil Moisture Estimation from Time Series of RADARSAT Constellation Mission Compact Polarimetric Data for the Identification of Water-Saturated Areas
by Igor Zakharov, Sarah Kohlsmith, Jon Hornung, François Charbonneau, Pradeep Bobby and Mark Howell
Remote Sens. 2024, 16(14), 2664; https://doi.org/10.3390/rs16142664 - 21 Jul 2024
Viewed by 984
Abstract
Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium [...] Read more.
Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands). Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling)
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25 pages, 8689 KiB  
Article
Assessment of Atmospheric Correction Algorithms for Sentinel-3 OLCI in the Amazon River Continuum
by Aline M. Valerio, Milton Kampel, Vincent Vantrepotte, Victoria Ballester and Jeffrey Richey
Remote Sens. 2024, 16(14), 2663; https://doi.org/10.3390/rs16142663 - 20 Jul 2024
Cited by 1 | Viewed by 1472
Abstract
Water colour remote sensing is a valuable tool for assessing bio-optical and biogeochemical parameters across the vast extent of the Amazon River Continuum (ARC). However, accurate retrieval depends on selecting the best atmospheric correction (AC). Four AC processors (Acolite, Polymer, C2RCC, OC-SMART) were [...] Read more.
Water colour remote sensing is a valuable tool for assessing bio-optical and biogeochemical parameters across the vast extent of the Amazon River Continuum (ARC). However, accurate retrieval depends on selecting the best atmospheric correction (AC). Four AC processors (Acolite, Polymer, C2RCC, OC-SMART) were evaluated against in situ remote sensing reflectance (Rrs) measurements. K-means classification identified four optical water types (OWTs) that are affected by the ARC. Two OWTs showed seasonal differences in the Lower Amazon River, influenced by the increase in suspended sediment concentration with river discharge. The other OWTs in the Amazon River Plume are dominated by phytoplankton or by a mixture of optically significant constituents. The Quality Water Index Polynomial method used to assess the quality of in situ and orbital Rrs had a high failure rate when the Apparent Visible Wavelength was >580 nm for in situ Rrs. OC-SMART Rrs products showed better spectral quality compared to Rrs derived from other AC processors evaluated in this study. These results improve our understanding of remotely sensing very turbid waters, such as those in the Amazon River Continuum. Full article
(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)
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20 pages, 2810 KiB  
Article
An Effective Scheme for Modeling and Compensating Differential Age Errors in Real-Time Kinematic Positioning
by Wei Huang, Zhiqin Zhao and Xiaozhang Zhu
Remote Sens. 2024, 16(14), 2662; https://doi.org/10.3390/rs16142662 - 20 Jul 2024
Viewed by 1008
Abstract
In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. [...] Read more.
In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. To bridge the significant age difference, in this work, we propose a simple and effective scheme for modeling and compensating for such errors. Firstly, the overall differential age error was modeled using truncated Taylor expansion. Then, a time-differenced carrier phase (TDCP)-based observation model was established to estimate the errors with the Kalman framework. Since estimating the receiver’s clock error is unnecessary, equivalent transformation and sequential filtering technology were adopted to significantly reduce the computational complexity. Furthermore, a predictor performance monitor was introduced to mitigate the integrity risks that may occur due to model mismatches. The effectiveness of this scheme was validated by static and dynamic field experiments. The static experiment results showed that when the differential age was 60 s, the GPS and BDS satellites’ overall root mean square error (RMSE) with the asynchronous RTK (ARTK) prediction method was 2.8 and 5.5 times that of the proposed method, respectively. Moreover, when the differential age was 120 s, these values were 3.3 and 5.4 times that of the proposed method, respectively. The field experiment results showed that when the differential age was 60 s, the integer ambiguity fixed rate and false fixed rate of the ARTK method were 0.90 and 1.63 times that of the proposed method, respectively. Finally, at a 120 s differential age, these values were 0.78 and 4.78 times that of the proposed, respectively. Full article
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24 pages, 8960 KiB  
Article
Segment Anything Model-Based Building Footprint Extraction for Residential Complex Spatial Assessment Using LiDAR Data and Very High-Resolution Imagery
by Yingjie Ji, Weiguo Wu, Guangtong Wan, Yindi Zhao, Weilin Wang, Hui Yin, Zhuang Tian and Song Liu
Remote Sens. 2024, 16(14), 2661; https://doi.org/10.3390/rs16142661 - 20 Jul 2024
Viewed by 1552
Abstract
With rapid urbanization, retrieving information about residential complexes in a timely manner is essential for urban planning. To develop efficiency and accuracy of building extraction in residential complexes, a Segment Anything Model-based residential building instance segmentation method with an automated prompt generator was [...] Read more.
With rapid urbanization, retrieving information about residential complexes in a timely manner is essential for urban planning. To develop efficiency and accuracy of building extraction in residential complexes, a Segment Anything Model-based residential building instance segmentation method with an automated prompt generator was proposed combining LiDAR data and VHR remote sensing images in this study. Three key steps are included in this method: approximate footprint detection using LiDAR data, automatic prompt generation for the SAM, and residential building footprint extraction. By applying this method, residential building footprints were extracted in Pukou District, Nanjing, Jiangsu Province. Based on this, a comprehensive assessment model was constructed to systematically evaluate the spatial layout of urban complexes using six dimensions of assessment indicators. The results showed the following: (1) The proposed method was used to effectively extract residential building footprints. (2) The residential complexes in the study area were classified into four levels. The numbers of complexes classified as Excellent, Good, Average, and Poor were 10, 29, 16, and 1, respectively. Residential complexes of different levels exhibited varying spatial layouts and building distributions. The results provide a visual representation of the spatial distribution of residential complexes that belong to different levels within the study area, aiding in urban planning. Full article
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25 pages, 15972 KiB  
Article
CACM-Net: Daytime Cloud Mask for AGRI Onboard the FY-4A Satellite
by Jingyuan Yang, Zhongfeng Qiu, Dongzhi Zhao, Biao Song, Jiayu Liu, Yu Wang, Kuo Liao and Kailin Li
Remote Sens. 2024, 16(14), 2660; https://doi.org/10.3390/rs16142660 - 20 Jul 2024
Viewed by 797
Abstract
Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The [...] Read more.
Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named “Convolutional and Attention-based Cloud Mask Net (CACM-Net)”, was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) < 70° and >70°, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA < 70° subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA > 70° section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January–June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA < 70° and 7.8%, 11.3%, and 4.8% for SZA > 70°, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results. Full article
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17 pages, 41292 KiB  
Article
A Deep Learning Approach to Segment Coastal Marsh Tidal Creek Networks from High-Resolution Aerial Imagery
by Richa Dutt, Collin Ortals, Wenchong He, Zachary Charles Curran, Christine Angelini, Alberto Canestrelli and Zhe Jiang
Remote Sens. 2024, 16(14), 2659; https://doi.org/10.3390/rs16142659 - 20 Jul 2024
Cited by 1 | Viewed by 1226
Abstract
Tidal creeks play a vital role in influencing geospatial evolution and marsh ecological communities in coastal landscapes. However, evaluating the geospatial characteristics of numerous creeks across a site and understanding their ecological relationships pose significant challenges due to the labor-intensive nature of manual [...] Read more.
Tidal creeks play a vital role in influencing geospatial evolution and marsh ecological communities in coastal landscapes. However, evaluating the geospatial characteristics of numerous creeks across a site and understanding their ecological relationships pose significant challenges due to the labor-intensive nature of manual delineation from imagery. Traditional methods rely on manual annotation in GIS interfaces, which is slow and tedious. This study explores the application of Attention-based Dense U-Net (ADU-Net), a deep learning image segmentation model, for automatically classifying creek pixels in high-resolution (0.5 m) orthorectified aerial imagery in coastal Georgia, USA. We observed that ADU-Net achieved an outstanding F1 score of 0.98 in identifying creek pixels, demonstrating its ability in tidal creek mapping. The study highlights the potential of deep learning models for automated tidal creek mapping, opening avenues for future investigations into the role of creeks in marshes’ response to environmental changes. Full article
(This article belongs to the Special Issue Remote Sensing Application in Coastal Geomorphology and Processes II)
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24 pages, 8333 KiB  
Article
Technical Possibilities and Limitations of the DPS-4D Type of Digisonde in Individual Meteor Detections
by Csilla Szárnya, Zbyšek Mošna, Antal Igaz, Daniel Kouba, Tobias G. W. Verhulst, Petra Koucká Knížová, Kateřina Podolská and Veronika Barta
Remote Sens. 2024, 16(14), 2658; https://doi.org/10.3390/rs16142658 - 20 Jul 2024
Cited by 1 | Viewed by 982
Abstract
During the peak days of the 2019 Leonids and Geminids (16–19 November and 10–16 December), two ionograms/minute and one Skymap/minute campaign measurements were carried out at the Sopron (47.63°N, 16.72°E) and Průhonice (50.00°N, 14.60°E) Digisonde stations. The stations used frequencies between 1 and [...] Read more.
During the peak days of the 2019 Leonids and Geminids (16–19 November and 10–16 December), two ionograms/minute and one Skymap/minute campaign measurements were carried out at the Sopron (47.63°N, 16.72°E) and Průhonice (50.00°N, 14.60°E) Digisonde stations. The stations used frequencies between 1 and 17 MHz for the ionograms, and the Skymaps were made at 2.5 MHz. A temporary optical camera was also installed at Sopron with a lower brightness limit of +1 visual magnitude. The manual scaling of ionograms for November and December 2019 to study the behavior of the regular sporadic E layer was also completed. Although the distributions of the stations were similar, there were interesting differences despite the relative proximity of the stations. The optical measurements detected 88 meteors. A total of 376 meteor-induced traces were found on the Digisonde ionograms at a most probable amplitude (MPA) threshold of 4 dB and of these, 40 cases could be linked to reflections on the Skymaps, too. Of the 88 optical detections, 31 could be identified on the ionograms. The success of detections depends on the sensitivity of the instruments and the noise-filtering. Geometrically, meteors above 80 km and with an altitude angle of 40° or higher can be detected using the Digisondes. Full article
(This article belongs to the Special Issue Advances in Near-Earth Space and Atmospheric Physics from Ground-Based and Satellite Observations)
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23 pages, 22355 KiB  
Article
Development of an Adaptive Fuzzy Integral-Derivative Line-of-Sight Method for Bathymetric LiDAR Onboard Unmanned Surface Vessel
by Guoqing Zhou, Jinhuang Wu, Ke Gao, Naihui Song, Guoshuai Jia, Xiang Zhou, Jiasheng Xu and Xia Wang
Remote Sens. 2024, 16(14), 2657; https://doi.org/10.3390/rs16142657 - 20 Jul 2024
Viewed by 1010
Abstract
Previous control methods developed by our research team cannot satisfy the high accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight [...] Read more.
Previous control methods developed by our research team cannot satisfy the high accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight (AFIDLOS) method for USV path-tracking control. Integral and derivative terms were added to counteract the effect of the sideslip angle with which the USV could be quickly guided to converge to the planned path for bathymetric mapping. To obtain high accuracy of the look-ahead distance, a fuzzy control method was proposed. The proposed method was verified using simulations and outdoor experiments. The results demonstrate that the AFIDLOS method can reduce the overshoot by 79.85%, shorten the settling time by 55.32% in simulation experiments, reduce the average cross-track error by 10.91% and can ensure a 30% overlap of neighboring strips of bathymetric LiDAR outdoor mapping when compared with the traditional guidance law. Full article
(This article belongs to the Special Issue Optical Remote Sensing Payloads, from Design to Flight Test)
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37 pages, 4497 KiB  
Review
Satellite Oceanography in NOAA: Research, Development, Applications, and Services Enabling Societal Benefits from Operational and Experimental Missions
by Eric Bayler, Paul S. Chang, Jacqueline L. De La Cour, Sean R. Helfrich, Alexander Ignatov, Jeff Key, Veronica Lance, Eric W. Leuliette, Deirdre A. Byrne, Yinghui Liu, Xiaoming Liu, Menghua Wang, Jianwei Wei and Paul M. DiGiacomo
Remote Sens. 2024, 16(14), 2656; https://doi.org/10.3390/rs16142656 - 20 Jul 2024
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Abstract
The National Oceanic and Atmospheric Administration’s (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, [...] Read more.
The National Oceanic and Atmospheric Administration’s (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, applications, tools, and services, and curating, translating, and integrating diverse data products into information that supports informed decision making. STAR research, development, and application efforts span from passive visible, infrared, and microwave observations to active altimetry, scatterometry, and synthetic aperture radar (SAR) observations. These efforts directly support NOAA’s operational geostationary (GEO) and low Earth orbit (LEO) missions with calibration/validation and retrieval algorithm development, implementation, maintenance, and anomaly resolution, as well as leverage the broader international constellation of environmental satellites for NOAA’s benefit. STAR’s satellite data products and services enable research, assessments, applications, and, ultimately, decision making for understanding, predicting, managing, and protecting ocean and coastal resources, as well as assessing impacts of change on the environment, ecosystems, and climate. STAR leads the NOAA Coral Reef Watch and CoastWatch/OceanWatch/PolarWatch Programs, helping people access and utilize global and regional satellite data for ocean, coastal, and ecosystem applications. Full article
(This article belongs to the Special Issue Oceans from Space V)
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