Remote Sensing

21 pages, 2399 KiB

Open AccessArticle

Gridless DOA Estimation Method for Arbitrary Array Geometries Based on Complex-Valued Deep Neural Networks

by Yuan Cao, Tianjun Zhou and Qunfei Zhang

Remote Sens. 2024, 16(19), 3752; https://doi.org/10.3390/rs16193752 - 9 Oct 2024

Viewed by 943

Gridless direction of arrival (DOA) estimation methods have garnered significant attention due to their ability to avoid grid mismatch errors, which can adversely affect the performance of high-resolution DOA estimation algorithms. However, most existing gridless methods are primarily restricted to applications involving uniform [...] Read more.

Gridless direction of arrival (DOA) estimation methods have garnered significant attention due to their ability to avoid grid mismatch errors, which can adversely affect the performance of high-resolution DOA estimation algorithms. However, most existing gridless methods are primarily restricted to applications involving uniform linear arrays or sparse linear arrays. In this paper, we derive the relationship between the element-domain covariance matrix and the angular-domain covariance matrix for arbitrary array geometries by expanding the steering vector using a Fourier series. Then, a deep neural network is designed to reconstruct the angular-domain covariance matrix from the sample covariance matrix and the gridless DOA estimation can be obtained by Root-MUSIC. Simulation results on arbitrary array geometries demonstrate that the proposed method outperforms existing methods like MUSIC, SPICE, and SBL in terms of resolution probability and DOA estimation accuracy, especially when the angular separation between targets is small. Additionally, the proposed method does not require any hyperparameter tuning, is robust to varying snapshot numbers, and has a lower computational complexity. Finally, real hydrophone data from the SWellEx-96 ocean experiment validates the effectiveness of the proposed method in practical underwater acoustic environments. Full article

(This article belongs to the Special Issue Ocean Remote Sensing Based on Radar, Sonar and Optical Techniques)

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17 pages, 3427 KiB

Open AccessArticle

Discriminating between Biotic and Abiotic Stress in Poplar Forests Using Hyperspectral and LiDAR Data

by Quan Zhou, Jinjia Kuang, Linfeng Yu, Xudong Zhang, Lili Ren and Youqing Luo

Remote Sens. 2024, 16(19), 3751; https://doi.org/10.3390/rs16193751 - 9 Oct 2024

Viewed by 532

Abstract

Sustainable forest management faces challenges from various biotic and abiotic stresses. The Asian longhorned beetle (ALB) and drought stress both induce water shortages in poplar trees, but require different management strategies. In northwestern China, ALB and drought stress caused massive mortality in poplar [...] Read more.

Sustainable forest management faces challenges from various biotic and abiotic stresses. The Asian longhorned beetle (ALB) and drought stress both induce water shortages in poplar trees, but require different management strategies. In northwestern China, ALB and drought stress caused massive mortality in poplar shelterbelts, which seriously affected the ecological functions of poplars. Developing a large-scale detection method for discriminating them is crucial for applying targeted management. This study integrated UAV-hyperspectral and LiDAR data to distinguish between ALB and drought stress in poplars of China’s Three-North Shelterbelt. These data were analyzed using a Partial Least Squares-Support Vector Machine (PLS-SVM). The results showed that the LiDAR metric (elev_sqrt_mean_sq) was key in detecting drought, while the hyperspectral band (R970) was key in ALB detection, underscoring the necessity of integrating both sensors. Detection of ALB in poplars improved when the poplars were well watered. The classification accuracy was 94.85% for distinguishing well-watered from water-deficient trees, and 80.81% for detecting ALB damage. Overall classification accuracy was 78.79% when classifying four stress types: healthy, only ALB affected, only drought affected, and combined stress of ALB and drought. The results demonstrate the effectiveness of UAV-hyperspectral and LiDAR data in distinguishing ALB and drought stress in poplar forests, which contribute to apply targeted treatments based on the specific stress in poplars in northwest China. Full article

(This article belongs to the Special Issue Dynamic Monitoring of Forest Resources Based on Multi-source Remote Sensing Data)

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16 pages, 12421 KiB

Open AccessArticle

High-Visibility Edge-Highlighting Visualization of 3D Scanned Point Clouds Based on Dual 3D Edge Extraction

by Yuri Yamada, Satoshi Takatori, Motoaki Adachi, Brahmantara, Kyoko Hasegawa, Liang Li, Jiao Pan, Fadjar I. Thufail, Hiroshi Yamaguchi and Satoshi Tanaka

Remote Sens. 2024, 16(19), 3750; https://doi.org/10.3390/rs16193750 - 9 Oct 2024

Viewed by 1465

Abstract

Recent advances in 3D scanning have enabled the digital recording of complex objects as large-scale point clouds, which require clear visualization to convey their 3D shapes effectively. Edge-highlighting visualization is used to improve the comprehensibility of complex 3D structures by enhancing the 3D [...] Read more.

Recent advances in 3D scanning have enabled the digital recording of complex objects as large-scale point clouds, which require clear visualization to convey their 3D shapes effectively. Edge-highlighting visualization is used to improve the comprehensibility of complex 3D structures by enhancing the 3D edges and high-curvature regions of the scanned objects. However, traditional methods often struggle with real-world objects due to inadequate representation of soft edges (i.e., rounded edges) and excessive line clutter, impairing resolution and depth perception. To address these challenges, we propose a novel visualization method for 3D scanned point clouds based on dual 3D edge extraction and opacity–color gradation. Dual 3D edge extraction separately identifies sharp and soft edges, integrating both into the visualization. Opacity–color gradation enhances the clarity of fine structures within soft edges through variations in color and opacity, while also creating a halo effect that improves both resolution and depth perception of the visualized edges. Computation times required for dual 3D edge extraction are comparable to conventional binary statistical edge-extraction methods. Visualizations with opacity–color gradation are executable at interactive rendering speeds. The effectiveness of the proposed method is demonstrated using 3D scanned point cloud data from high-value cultural heritage objects. Full article

(This article belongs to the Special Issue New Insight into Point Cloud Data Processing)

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23 pages, 36489 KiB

Open AccessArticle

Comparison of the Morrison and WDM6 Microphysics Schemes in the WRF Model for a Convective Precipitation Event in Guangdong, China, Through the Analysis of Polarimetric Radar Data

by Xiaolong Chen and Xiaoli Liu

Remote Sens. 2024, 16(19), 3749; https://doi.org/10.3390/rs16193749 - 9 Oct 2024

Viewed by 650

Abstract

Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. [...] Read more.

Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. The advancement of dual-polarization radar has enriched the comparative validation of these simulations. This study simulated a convective event in Guangdong, China, from May 7 to 8, 2017, employing two bulk microphysical schemes (Morrison and WDM6) in the WRF v4.2 model. Each scheme was divided into two versions: one representing rimed ice particles as graupel (Mor_G, WDM6_G) and the other as hail (Mor_H, WDM6_H). The simulation results indicated negligible differences between the rimed ice set as graupel or hail particles, for both schemes. However, the Morrison schemes (Mor_G, Mor_H) depicted a more accurate raindrop size distribution below the 0 °C height level. A further analysis suggested that disparities between the Morrison and WDM6 schemes could be attributed to the intercept parameter (N₀) setting for snow and graupel/hail in WDM6 scheme. The prescribed snow and graupel/hail N₀ of WDM6 scheme might influence the melting processes, leading to a higher number concentration but a reduced mass-weighted diameter of raindrops. Reducing the intercept parameter for snow and graupel/hail in the WDM6 scheme could potentially enhance the simulation of convective precipitation. Conversely, the increase in N₀ might deteriorate the precipitation simulation performance of the WDM6_G scheme, whereas the WDM6_H scheme exhibits minimal sensitivity to such changes. Full article

(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)

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17 pages, 2987 KiB

Open AccessArticle

Melt Pond Evolution along the MOSAiC Drift: Insights from Remote Sensing and Modeling

by Mingfeng Wang, Felix Linhardt, Victor Lion and Natascha Oppelt

Remote Sens. 2024, 16(19), 3748; https://doi.org/10.3390/rs16193748 - 9 Oct 2024

Viewed by 579

Abstract

Melt ponds play a crucial role in the melting of Arctic sea ice. Studying the evolution of melt ponds is essential for understanding changes in Arctic sea ice. In this study, we used a revised sea ice model to simulate the evolution of [...] Read more.

Melt ponds play a crucial role in the melting of Arctic sea ice. Studying the evolution of melt ponds is essential for understanding changes in Arctic sea ice. In this study, we used a revised sea ice model to simulate the evolution of melt ponds along the MOSAiC drift at a resolution of 10 m. A novel melt pond parameterization scheme simulates the movement of meltwater under the influence of gravity over a realistic sea ice topography. We evaluated different melt pond parameterization schemes based on remote sensing observations. The absolute deviation of the maximum pond coverage simulated by the new scheme is within 3%, while differences among parameterization schemes exceed 50%. Errors were found to be primarily due to the calculation of macroscopic meltwater loss, which is related to sea ice surface topography. Previous studies have indicated that sea ice with a lower surface roughness has a larger catchment area, resulting in larger pond coverage during the melt season. This study has identified an opposing mechanism: sea ice with lower surface roughness has a larger catchment area connected to the macroscopic flaws of the sea ice surface, which leads to more macroscopic drainage into the ocean and thereby a decrease in melt pond coverage. Experimental simulations showed that sea ice with 46% higher surface roughness, resulting in 12% less macroscopic drainage, exhibited a 38% higher maximum pond fraction. The presence of macroscopic flaws is related to the fragmentation of sea ice cover. As Arctic sea ice cover becomes increasingly fragmented and mobile, this mechanism will become more significant. Full article

(This article belongs to the Special Issue Surface Radiative Transfer: Modeling, Inversion, and Applications)

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17 pages, 7168 KiB

Open AccessArticle

Evaluating the Prediction Performance of the WRF-CUACE Model in Xinjiang, China

by Yisilamu Wulayin, Huoqing Li, Lei Zhang, Ali Mamtimin, Junjian Liu, Wen Huo and Hongli Liu

Remote Sens. 2024, 16(19), 3747; https://doi.org/10.3390/rs16193747 - 9 Oct 2024

Viewed by 700

Abstract

Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air [...] Read more.

Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air pollution forecasting system based on the Weather Research and Forecasting model coupled with the China Meteorological Administration Chemistry Environment (WRF-CUACE) model using ground and satellite observations. The results showed that the forecasting system accurately predicted the formation, development, and termination of dust events. It demonstrated good capability for predicting the evolution and spatial distribution of dust storms, although it overestimated dust intensity. Specifically, the correlation coefficient (R) between simulated and observed PM10 was up to 0.85 with a mean absolute error (MAE) of 721.36 µg·m⁻³ during dust storm periods. During air pollution events, the forecasting system displayed notable variations in predictive accuracy across various urban areas. The simulated trends of PM2.5 and the Air Quality Index (AQI) closely aligned with the actual observations in Ürümqi. The R for simulated and observed PM2.5 concentrations at 24 and 48 h intervals were 0.60 and 0.54, respectively, with MAEs of 28.92 µg·m⁻³ and 29.10 µg·m⁻³, respectively. The correlation coefficients for simulated and observed AQIs at 24 and 48 h intervals were 0.79 and 0.70, respectively, with MAEs of 24.21 and 27.56, respectively. The evolution of the simulated PM10 was consistent with observations despite relatively high concentrations. The simulated PM2.5 concentrations in Changji and Shihezi were notably lower than those observed, resulting in a lower AQI. For PM10, the simulation–observation error was relatively small; however, the trends were inconsistent. Future research should focus on optimizing model parameterization schemes and emission source data. Full article

(This article belongs to the Special Issue Comprehensive Analysis Based on Observation, Remote Sensing, and Numerical Models to Understand the Meteorological Environment in Arid Areas and Their Surrounding Areas)

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18 pages, 4210 KiB

Open AccessArticle

Quantifying Creep on the Laohushan Fault Using Dense Continuous GNSS

by Wenquan Zhuang, Yuhang Li, Ming Hao, Shangwu Song, Baiyun Liu and Lihong Fan

Remote Sens. 2024, 16(19), 3746; https://doi.org/10.3390/rs16193746 - 9 Oct 2024

Viewed by 549

Abstract

The interseismic behavior of faults (whether they are locked or creeping) and their quantitative kinematic constraints are critical for assessing the seismic hazards of faults and their surrounding areas. Currently, the creep of the eastern segment of the Laohushan Fault in the Haiyuan [...] Read more.

The interseismic behavior of faults (whether they are locked or creeping) and their quantitative kinematic constraints are critical for assessing the seismic hazards of faults and their surrounding areas. Currently, the creep of the eastern segment of the Laohushan Fault in the Haiyuan Fault Zone at the northeastern margin of the Tibetan Plateau, as revealed by InSAR observations, lacks confirmation from other observational methods, particularly high-precision GNSS studies. In this study, we utilized nearly seven years of observation data from a dense GNSS continuous monitoring profile (with a minimum station spacing of 2 km) that crosses the eastern segment of the Laohushan Fault. This dataset was integrated with GNSS data from regional continuous stations, such as those from the Crustal Movement Observation Network of China, and multiple campaign measurements to calculate GNSS baseline change time series across the Laohushan Fault and to obtain a high spatial resolution horizontal crustal velocity field for the region. A comprehensive analysis of this primary dataset indicates that the Laohushan Fault is currently experiencing left-lateral creep, characterized by a partially locked shallow segment and a deeper locked segment. The fault creep is predominantly concentrated in the shallow crustal region, within a depth range of 0–5.7 ± 3.4 km, exhibiting a creep rate of 1.5 ± 0.7 mm/yr. Conversely, at depths of 5.7 ± 3.4 km to 16.8 ± 4.2 km, the fault remains locked, with a loading rate of 3.9 ± 1.1 mm/yr. The shallow creep is primarily confined within 3 km on either side of the fault. Over the nearly seven-year observation period, the creep movement within approximately 5 km of the fault’s near field has shown no significant time-dependent variation, instead demonstrating a steady-state behavior. This steady-state creep appears unaffected by postseismic effects from historical large earthquakes in the adjacent region, although the deeper (far-field) tectonic deformation of the Laohushan Fault may have been influenced by the postseismic effects of the 1920 Haiyuan M8.5 earthquake. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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3 pages, 160 KiB

Open AccessEditorial

An Editorial for the Special Issue “Aerosol and Atmospheric Correction”

by Shuaiyi Shi, Xingfa Gu and Jing Wei

Remote Sens. 2024, 16(19), 3745; https://doi.org/10.3390/rs16193745 - 9 Oct 2024

Viewed by 488

Abstract

Aerosol is an important atmospheric component that severely influences the global climate and air quality of our planet [...] Full article

(This article belongs to the Special Issue Aerosol and Atmospheric Correction)

24 pages, 42565 KiB

Open AccessArticle

Reconstructing a Fine Resolution Landscape of Annual Gross Primary Product (1895–2013) with Tree-Ring Indices

by Hang Li, James H. Speer, Collins C. Malubeni and Emma Wilson

Remote Sens. 2024, 16(19), 3744; https://doi.org/10.3390/rs16193744 - 9 Oct 2024

Cited by 1 | Viewed by 605

Abstract

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP [...] Read more.

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP’s increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century. Full article

(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)

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33 pages, 2752 KiB

Open AccessArticle

Hyperspectral Image Transects during Transient Events in Rivers (HITTER): Framework Development and Application to a Tracer Experiment on the Missouri River, USA

by Carl J. Legleiter, Victoria M. Scholl, Brandon J. Sansom and Matthew A. Burgess

Remote Sens. 2024, 16(19), 3743; https://doi.org/10.3390/rs16193743 - 9 Oct 2024

Viewed by 1010

Abstract

Rivers convey a broad range of materials, such as sediment, nutrients, and contaminants. Much of this transport can occur during or immediately after an episodic, pulsed event like a flood or an oil spill. Understanding the flow processes that influence the motion of [...] Read more.

Rivers convey a broad range of materials, such as sediment, nutrients, and contaminants. Much of this transport can occur during or immediately after an episodic, pulsed event like a flood or an oil spill. Understanding the flow processes that influence the motion of these substances is important for managing water resources and conserving aquatic ecosystems. This study introduces a new remote sensing framework for characterizing dynamic phenomena at the scale of a channel cross-section: Hyperspectral Image Transects during Transient Events in Rivers (HITTER). We present a workflow that uses repeated hyperspectral scan lines acquired from a hovering uncrewed aircraft system (UAS) to quantify how a water attribute of interest varies laterally across the river and evolves over time. Data from a tracer experiment on the Missouri River are used to illustrate the components of the end-to-end processing chain we used to quantify the passage of a visible dye. The framework is intended to be flexible and could be applied in a number of different contexts. The results of this initial proof-of-concept investigation suggest that HITTER could potentially provide insight regarding the dispersion of a range of materials in rivers, which would facilitate ecological and geomorphic studies and help inform management. Full article

(This article belongs to the Section Environmental Remote Sensing)

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14 pages, 5427 KiB

Open AccessTechnical Note

A Study of the Mixed Layer Warming Induced by the Barrier Layer in the Northern Bay of Bengal in 2013

by Xutao Ni, Yun Qiu, Wenshu Lin, Tongtong Liu and Xinyu Lin

Remote Sens. 2024, 16(19), 3742; https://doi.org/10.3390/rs16193742 - 9 Oct 2024

Viewed by 485

Abstract

Strong salinity stratification induced by large freshwater fluxes in the northern Bay of Bengal (BOB) results in the formation of a quasi-permanent barrier layer (BL) that covers almost the entire BOB and leads to a unique temperature inversion within the thick BL in [...] Read more.

Strong salinity stratification induced by large freshwater fluxes in the northern Bay of Bengal (BOB) results in the formation of a quasi-permanent barrier layer (BL) that covers almost the entire BOB and leads to a unique temperature inversion within the thick BL in winter. In the presence of temperature inversions, the entrainment process at the bottom of the mixed layer (ML) induces warming effects in the ML, but little is known about this. In this paper, we quantify the contribution of the entrainment process to the ML temperature (MLT) in the northern BOB during the winter of 2013 using monthly and daily data from the Ocean General Circulation Model for the Earth Simulator version 2 (OFES2). It is found that the warming effect of the daily entrainment heat flux (EHF), which resolved the high-frequency variations, is 4 orders of magnitude larger than the monthly EHF for most of the wintertime. This significantly enhanced warming effect in daily data offsets up to 87% of the surface cooling induced by net heat flux during wintertime. A further analysis reveals that the larger daily EHF warming effect compared to its monthly counterpart is closely related to the deepened ML, the larger temperature difference within the ML and vertical velocity at the bottom of the ML. Full article

(This article belongs to the Special Issue Air-Sea Interaction and Climate Variability in the Ocean: Observations and Modeling Based on Remote Sensing Techniques)

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22 pages, 4305 KiB

Open AccessArticle

LiOSR-SAR: Lightweight Open-Set Recognizer for SAR Imageries

by Jie Yang, Jihong Gu, Jingyu Xin, Zhou Cong and Dazhi Ding

Remote Sens. 2024, 16(19), 3741; https://doi.org/10.3390/rs16193741 - 9 Oct 2024

Viewed by 740

Abstract

Open-set recognition (OSR) from synthetic aperture radar (SAR) imageries plays a crucial role in maritime and terrestrial monitoring. Nevertheless, numerous deep learning-based SAR classifiers struggle with unknown targets outside of the training dataset, leading to a dilemma, namely that a large model is [...] Read more.

Open-set recognition (OSR) from synthetic aperture radar (SAR) imageries plays a crucial role in maritime and terrestrial monitoring. Nevertheless, numerous deep learning-based SAR classifiers struggle with unknown targets outside of the training dataset, leading to a dilemma, namely that a large model is difficult to deploy, while a smaller one sacrifices accuracy. To address this challenge, the novel “LiOSR-SAR” lightweight recognizer is proposed for OSR in SAR imageries. It incorporates the compact attribute focusing and open-prediction modules, which collectively optimize its lightweight structure and high accuracy. To validate LiOSR-SAR, “fast image simulation using bidirectional shooting and bouncing ray (FIS-BSBR)” is exploited to construct the corresponding dataset. It enhances the details of targets for more accurate recognition significantly. Extensive experiments show that LiOSR-SAR achieves remarkable recognition accuracies of 97.9% and 94.1% while maintaining a compact model size of 7.5 MB, demonstrating its practicality and efficiency. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Video Data Processing: Theories, Technologies and Applications)

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Graphical abstract

19 pages, 15362 KiB

Open AccessArticle

Deep Tectonic Environment Analysis of the Lingshan Conjugate Earthquake within the Qinzhou Fold Belt, South China: Insights Derived from 3D Resistivity Structure Model

by Chunheng Yan, Bin Zhou, Yan Zhan, Xiangyu Sun, Sha Li, Lei Li and Peilan Guo

Remote Sens. 2024, 16(19), 3740; https://doi.org/10.3390/rs16193740 - 9 Oct 2024

Viewed by 818

Abstract

The Qinzhou fold belt, situated at the contact zone between the Yangtze and Cathaysia blocks in South China, was affected by the 1936 Lingshan M6¾ earthquake and the 1958 Lingshan M5¾ earthquake, both of which occurred within the conjugate structure. Understanding the deep [...] Read more.

The Qinzhou fold belt, situated at the contact zone between the Yangtze and Cathaysia blocks in South China, was affected by the 1936 Lingshan M6¾ earthquake and the 1958 Lingshan M5¾ earthquake, both of which occurred within the conjugate structure. Understanding the deep seismogenic setting and causal mechanism of the Lingshan conjugate earthquake is of great significance for assessing the seismic disaster risk in the region. In this study, we utilized 237 magnetotelluric datasets and employed three-dimensional electromagnetic inversion to characterize the deep-seated three-dimensional resistivity structure of the Qinzhou fold belt and the Lingshan seismic zone. The results reveal that: (1) The NE-trending faults within the Qinzhou fold belt and adjacent areas are classified as trans-crustal faults. The faults exhibit crust-mantle ductile shear zones in their deeper sections, which are essential in governing regional tectonic deformation and seismic activity; (2) The electrical structure of the Qinzhou fold belt is in line with the tectonic characteristics of a composite orogenic belt, having experienced several phases of tectonic modification. The southeastern region is being influenced by mantle-derived magmatic activities originating from the Leiqiong area over a significant distance; (3) In the Lingshan seismic zone, the NE-trending Fangcheng-Lingshan fault is a trans-crustal fault and the NW-trending Zhaixu fault is an intra-crustal fault. The electrical structure pattern “two low, one high” in the zone has a significant impact on the deep tectonic framework of the area and influences the deformation behavior of shallow faults; and (4) The seismogenic structure of the 1936 Lingshan M6¾ earthquake was the Fangcheng-Lingshan fault. The earthquake’s genesis was influenced by the coupling effect of tectonic stress and deep thermal dynamics. The seismogenic structure of the 1958 Lingshan M5¾ earthquake was the Zhaixu fault. The earthquake’s genesis was influenced by tectonic stress and static stress triggering from the 1936 Lingshan M6¾ earthquake. The conjugate rupture mode in the Lingshan seismic zone is influenced by various factors, including differences in physical properties, rheology of deep materials, and the scale and depth of fault development. Full article

(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)

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23 pages, 17457 KiB

Open AccessArticle

Research on Digital Twin Method for Spaceborne Along-Track Interferometric Synthetic Aperture Radar Velocity Inversion of Ocean Surface Currents

by Zhou Min, He Yan, Xinrui Jiang, Xin Chen, Junyi Zhou and Daiyin Zhu

Remote Sens. 2024, 16(19), 3739; https://doi.org/10.3390/rs16193739 - 8 Oct 2024

Viewed by 712

Abstract

In this paper, an end-to-end system framework is proposed for the Digital Twin study of spaceborne ATI-SAR ocean current velocity inversion. Within this framework, a fitting inversion approach is proposed to enhance the conventional spaceborne ATI-SAR ocean current velocity inversion algorithm. Consequently, the [...] Read more.

In this paper, an end-to-end system framework is proposed for the Digital Twin study of spaceborne ATI-SAR ocean current velocity inversion. Within this framework, a fitting inversion approach is proposed to enhance the conventional spaceborne ATI-SAR ocean current velocity inversion algorithm. Consequently, the issue of possible local inversion errors stemming from the mismatch between the traditional spaceborne ATI-SAR inversion algorithm and various dual-antenna configurations is resolved to a certain extent. A simulated spaceborne ATI-SAR system, featuring a dual-antenna configuration comprising a baseline direction perpendicular to the track and a squint angle, is presented to validate the efficacy of the Digital Twin methodology. Under the specified simulation parameters, the average inversion error for the final ocean current velocity is recorded at 0.0084 m/s, showcasing a reduction of 0.0401 m/s compared with the average inversion error prior to optimization. Full article

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22 pages, 11903 KiB

Open AccessArticle

Remote Sensing Mapping and Analysis of Spatiotemporal Patterns of Land Use and Cover Change in the Helong Region of the Loess Plateau Region (1986–2020)

by Jingyu Li, Yangbo Chen, Yu Gu, Meiying Wang and Yanjun Zhao

Remote Sens. 2024, 16(19), 3738; https://doi.org/10.3390/rs16193738 - 8 Oct 2024

Viewed by 798

Abstract

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in [...] Read more.

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management. Full article

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32 pages, 15160 KiB

Open AccessArticle

Analyzing Temporal Characteristics of Winter Catch Crops Using Sentinel-1 Time Series

by Shanmugapriya Selvaraj, Damian Bargiel, Abdelaziz Htitiou and Heike Gerighausen

Remote Sens. 2024, 16(19), 3737; https://doi.org/10.3390/rs16193737 - 8 Oct 2024

Viewed by 710

Abstract

Catch crops are intermediate crops sown between two main crop cycles. Their adoption into the cropping system has increased considerably in the last years due to its numerous benefits, in particular its potential in carbon fixation and preventing nitrogen leaching during winter. The [...] Read more.

Catch crops are intermediate crops sown between two main crop cycles. Their adoption into the cropping system has increased considerably in the last years due to its numerous benefits, in particular its potential in carbon fixation and preventing nitrogen leaching during winter. The growth period of catch crops in Germany is often marked by dense cloud cover, which limits land surface monitoring through optical remote sensing. In such conditions, synthetic aperture radar (SAR) emerges as a viable option. Despite the known advantages of SAR, the understanding of temporal behavior of radar parameters in relation to catch crops remains largely unexplored. Hence, in this study, we exploited the dense time series of Sentinel-1 data within the Copernicus Space Component to study the temporal characteristics of catch crops over a test site in the center of Germany. Radar parameters such as VV, VH, VH/VV backscatter, dpRVI (dual-pol Radar Vegetation Index) and VV coherence were extracted, and temporal profiles were interpreted for catch crops and preceding main crops along with in situ, temperature, and precipitation data. Additionally, we examined the temporal profiles of winter main crops (winter oilseed rape and winter cereals), that are grown parallel to the catch crop growing cycle. Based on the analyzed temporal patterns, we defined 22 descriptive features from VV, VH, VH/VV and dpRVI, which are specific to catch crop identification. Then, we conducted a Kruskal–Wallis test on the extracted parameters, both crop-wise and group-wise, to assess the significance of statistical differences among different catch crop groups. Our results reveal that there exists a unique temporal pattern for catch crops compared to main crops, and each of these extracted parameters possess a different sensitivity to catch crops. Parameters VV and VH are sensitive to phenological stages and crop structure. On the other hand, VH/VV and dpRVI were found to be highly sensitive to crop biomass. Coherence can be used to detect the sowing and harvest events. The preceding main crop analysis reveals that winter wheat and winter barley are the two dominant main crops grown before catch crops. Moreover, winter main crops (winter oilseed rape, winter cereals) cultivated during the catch crop cycle can be distinguished by exploiting the observed sowing window differences. The extracted descriptive features provide information about sowing, harvest, vigor, biomass, and early/late die-off nature specific to catch crop types. In the Kruskal–Wallis test, the observed high H-statistic and low p-value in several predictors indicates significant variability at 0.001 level. Furthermore, Dunn’s post hoc test among catch crop group pairs highlights the substantial differences between cold-sensitive and legume groups (p < 0.001). Full article

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21 pages, 9198 KiB

Open AccessArticle

Estimating Vertical Distribution of Total Suspended Matter in Coastal Waters Using Remote-Sensing Approaches

by Hailong Zhang, Xin Ren, Shengqiang Wang, Xiaofan Li, Deyong Sun and Lulu Wang

Remote Sens. 2024, 16(19), 3736; https://doi.org/10.3390/rs16193736 - 8 Oct 2024

Viewed by 803

Abstract

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations [...] Read more.

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges (3rd Edition))

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18 pages, 8934 KiB

Open AccessArticle

A New NDSA (Normalized Differential Spectral Attenuation) Measurement Campaign for Estimating Water Vapor along a Radio Link

by Luca Facheris, Fabrizio Cuccoli, Ugo Cortesi, Samuele del Bianco, Marco Gai, Giovanni Macelloni and Francesco Montomoli

Remote Sens. 2024, 16(19), 3735; https://doi.org/10.3390/rs16193735 - 8 Oct 2024

Viewed by 570

Abstract

The Normalized Differential Spectral Attenuation (NDSA) technique was proposed years ago as an active method for measuring integrated water vapor (IWV) along a Ku/K-band radio link immersed (totally or partially) in the troposphere. The approach is of the active kind, as it relies [...] Read more.

The Normalized Differential Spectral Attenuation (NDSA) technique was proposed years ago as an active method for measuring integrated water vapor (IWV) along a Ku/K-band radio link immersed (totally or partially) in the troposphere. The approach is of the active kind, as it relies on the transmission of a couple of sinusoidal signals, whose power is measured at the receiver, thus providing the differential attenuation measurements from which IWV estimates can be in turn derived. In 2018, a prototype instrument providing such differential attenuation measurements was completed and set up for a first measurement campaign aimed at demonstrating the NDSA method. By the end of June 2022, the instrument was profoundly modified and upgraded so that a second measurement campaign could be carried out from 1 August to 30 November 2022. The transmitter was placed on the top of Monte Gomito (44.1277°lat, 10.6434°lon, 1892 m a.s.l.) and the receiver on the roof of the Department of Information Engineering of the University of Florence (43.7985°lat, 11.2528°lon, 50 m a.s.l.). The resulting radio link length was 61.15 km. Four ground weather stations of the regional weather service were selected among those available. In this paper, we describe the upgraded instrument and present the outcomes of the new measurement campaign, whose purpose was mainly to compare the IWV estimates provided by the instrument with the ground sensor measurements of air temperature, air humidity, barometric pressure, and rainfall. In particular, we show that the temporal trends of the two IWV estimates are qualitatively consistent, and that the instrument is able to provide IWV estimates also in the presence of fog and rainfall. Conversely, a quantitative evaluation through comparison with IWV data from point weather station measurements appears challenging due to the significant spatial variability in temperature and relative humidity, even between couples of stations that are quite close to each other. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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18 pages, 3396 KiB

Open AccessArticle

Satellite-Based Detection of Algal Blooms in Large Alpine Lake Sevan: Can Satellite Data Overcome the Unavoidable Limitations in Field Observations?

by Shushanik Asmaryan, Anahit Khlghatyan, Azatuhi Hovsepyan, Vahagn Muradyan, Rima Avetisyan, Gor Gevorgyan, Armine Hayrapetyan, Mayada Mohamed Alshahat Arafat Eissa, Hendrik Bernert, Martin Schultze and Karsten Rinke

Remote Sens. 2024, 16(19), 3734; https://doi.org/10.3390/rs16193734 - 8 Oct 2024

Viewed by 1596

Abstract

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect [...] Read more.

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake. Full article

(This article belongs to the Section Environmental Remote Sensing)

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22 pages, 21320 KiB

Open AccessArticle

Evaluating Ecological Drought Vulnerability from Ecosystem Service Value Perspectives in North China

by Tianliang Jiang, Yanping Qu, Xuejun Zhang, Lanshu Jing, Kai Feng, Gengxi Zhang and Yu Han

Remote Sens. 2024, 16(19), 3733; https://doi.org/10.3390/rs16193733 - 8 Oct 2024

Viewed by 847

Abstract

Existing studies on the vulnerability assessment of ecological drought often focus on analyzing vegetation phenotypic characteristics, overlooking the impact of drought on ecosystem services. This study proposes an ecosystem vulnerability assessment method under ecological drought stress from the perspective of ecosystem service value [...] Read more.

Existing studies on the vulnerability assessment of ecological drought often focus on analyzing vegetation phenotypic characteristics, overlooking the impact of drought on ecosystem services. This study proposes an ecosystem vulnerability assessment method under ecological drought stress from the perspective of ecosystem service value (ESV), considering the characteristics and interactions of hazard-causing factors and hazard-bearing bodies. The spatiotemporal evolution of ecological drought, the spatial characteristics of ecosystem vulnerability, and the vulnerability characteristics of different ecosystem types in the North China region from 1991 to 2021 were evaluated. The results showed that: (1) ecological drought exhibited a trend of intensification followed by alleviation, with the most severe droughts occurring between 2002 and 2011, affecting up to 64.3% of the region; (2) ESV was mainly influenced by vegetation cover and precipitation gradients, displaying a spatial pattern of high values in the southeast and low values in the northwest, with total ESV averaging CNY 18.23 trillion; (3) grasslands exhibited higher sensitivity to drought compared to forests, and the sensitivity was higher in summer and autumn than in winter and spring. This method assessed the vulnerability of ecological drought from the perspective of ecosystem services, providing a new approach for a more comprehensive understanding of the impact of drought on ecosystem service functions. Full article

(This article belongs to the Special Issue Analyzing the Influence of Environmental Change on Water and Terrestrial Vegetation Using Satellite Data)

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30 pages, 6012 KiB

Open AccessArticle

A Remote-Sensing-Based Method Using Rockfall Inventories for Hazard Mapping at the Community Scale in the Arequipa Region of Peru

by Cassidy L. Grady, Paul M. Santi, Gabriel Walton, Carlos Luza, Guido Salas, Pablo Meza and Segundo Percy Colque Riega

Remote Sens. 2024, 16(19), 3732; https://doi.org/10.3390/rs16193732 - 8 Oct 2024

Viewed by 790

Abstract

Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the [...] Read more.

Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R² value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R² of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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28 pages, 13701 KiB

Open AccessArticle

Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset

by Shujian Wang, Xunhe Zhang, Lili Hou, Jiejie Sun and Ming Xu

Remote Sens. 2024, 16(19), 3731; https://doi.org/10.3390/rs16193731 - 8 Oct 2024

Viewed by 1063

Abstract

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect [...] Read more.

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on

V_{\max}

and

J_{\max}

, leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric

{CO}_{2}

concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4

PgC {year}^{- 1}

to 133.94

PgC {year}^{- 1}

from 2000 to 2022, with an average of 125.93

PgC {year}^{- 1}

. We also found that the global GPP showed an increasing trend of 0.548

PgC {year}^{- 1}

during the study period. Further analyses using the structure equation model showed that atmospheric

{CO}_{2}

concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP. Full article

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19 pages, 44218 KiB

Open AccessArticle

Testing the Impact of Pansharpening Using PRISMA Hyperspectral Data: A Case Study Classifying Urban Trees in Naples, Italy

by Miriam Perretta, Gabriele Delogu, Cassandra Funsten, Alessio Patriarca, Eros Caputi and Lorenzo Boccia

Remote Sens. 2024, 16(19), 3730; https://doi.org/10.3390/rs16193730 - 8 Oct 2024

Viewed by 786

Abstract

Urban trees support vital ecological functions and help with the mitigation of and adaption to climate change. Yet, their monitoring and management require significant public resources. remote sensing could facilitate these tasks. Recent hyperspectral satellite programs such as PRISMA have enabled more advanced [...] Read more.

Urban trees support vital ecological functions and help with the mitigation of and adaption to climate change. Yet, their monitoring and management require significant public resources. remote sensing could facilitate these tasks. Recent hyperspectral satellite programs such as PRISMA have enabled more advanced remote sensing applications, such as species classification. However, PRISMA data’s spatial resolution (30 m) could limit its utility in urban areas. Improving hyperspectral data resolution with pansharpening using the PRISMA coregistered panchromatic band (spatial resolution of 5 m) could solve this problem. This study addresses the need to improve hyperspectral data resolution and tests the pansharpening method by classifying exemplative urban tree species in Naples (Italy) using a convolutional neural network and a ground truths dataset, with the aim of comparing results from the original 30 m data to data refined to a 5 m resolution. An evaluation of accuracy metrics shows that pansharpening improves classification quality in dense urban areas with complex topography. In fact, pansharpened data led to significantly higher accuracy for all the examined species. Specifically, the Pinus pinea and Tilia x europaea classes showed an increase of 10% to 20% in their F1 scores. Pansharpening is seen as a practical solution to enhance PRISMA data usability in urban environments. Full article

(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging II)

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21 pages, 9019 KiB

Open AccessArticle

Aberration Modulation Correlation Method for Dim and Small Space Target Detection

by Changchun Jiang, Junwei Li, Shengjie Liu and Hao Xian

Remote Sens. 2024, 16(19), 3729; https://doi.org/10.3390/rs16193729 - 8 Oct 2024

Viewed by 565

Abstract

The significance of detecting faint and diminutive space targets cannot be overstated, as it underpins the preservation of Earth’s orbital environment’s safety and long-term sustainability. Founded by the different response characteristics between targets and backgrounds to aberrations, this paper proposes a novel aberration [...] Read more.

The significance of detecting faint and diminutive space targets cannot be overstated, as it underpins the preservation of Earth’s orbital environment’s safety and long-term sustainability. Founded by the different response characteristics between targets and backgrounds to aberrations, this paper proposes a novel aberration modulation correlation method (AMCM) for dim and small space target detection. By meticulously manipulating the light path using a wavefront corrector via a modulation signal, the target brightness will fluctuate periodically, while the background brightness remains essentially constant. Benefited by the strong correlation between targets’ characteristic changes and the modulation signal, dim and small targets can be effectively detected. Rigorous simulations and practical experiments have validated the remarkable efficacy of AMCM. Compared to conventional algorithms, AMCM boasts a substantial enhancement in the signal-to-noise ratio (SNR) detection limit from 5 to approximately 2, with an area under the precision–recall curve of 0.9396, underscoring its ability to accurately identify targets while minimizing false positives. In essence, AMCM offers an effective method for detecting dim and small space targets and is also conveniently integrated into other passive target detection systems. Full article

(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)

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19 pages, 9016 KiB

Open AccessArticle

Semi-Supervised Subcategory Centroid Alignment-Based Scene Classification for High-Resolution Remote Sensing Images

by Nan Mo and Ruixi Zhu

Remote Sens. 2024, 16(19), 3728; https://doi.org/10.3390/rs16193728 - 7 Oct 2024

Viewed by 648

Abstract

It is usually hard to obtain adequate annotated data for delivering satisfactory scene classification results. Semi-supervised scene classification approaches can transfer the knowledge learned from previously annotated data to remote sensing images with scarce samples for satisfactory classification results. However, due to the [...] Read more.

It is usually hard to obtain adequate annotated data for delivering satisfactory scene classification results. Semi-supervised scene classification approaches can transfer the knowledge learned from previously annotated data to remote sensing images with scarce samples for satisfactory classification results. However, due to the differences between sensors, environments, seasons, and geographical locations, cross-domain remote sensing images exhibit feature distribution deviations. Therefore, semi-supervised scene classification methods may not achieve satisfactory classification accuracy. To address this problem, a novel semi-supervised subcategory centroid alignment (SSCA)-based scene classification approach is proposed. The SSCA framework is made up of two components, namely the rotation-robust convolutional feature extractor (RCFE) and the neighbor-based subcategory centroid alignment (NSCA). The RCFE aims to suppress the impact of rotation changes on remote sensing image representation, while the NSCA aims to decrease the impact of intra-category variety across domains on cross-domain scene classification. The SSCA algorithm and several competitive approaches are validated on two datasets to demonstrate its effectiveness. The results prove that the proposed SSCA approach performs better than most competitive approaches by no less than 2% overall accuracy. Full article

(This article belongs to the Special Issue Deep Transfer Learning for Remote Sensing II)

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22 pages, 5856 KiB

Open AccessArticle

Automated Recognition of Snow-Covered and Icy Road Surfaces Based on T-Net of Mount Tianshan

by Jingqi Liu, Yaonan Zhang, Jie Liu, Zhaobin Wang and Zhixing Zhang

Remote Sens. 2024, 16(19), 3727; https://doi.org/10.3390/rs16193727 - 7 Oct 2024

Cited by 1 | Viewed by 1075

Abstract

The Tianshan Expressway plays a crucial role in China’s “Belt and Road” strategy, yet the extreme climate of the Tianshan Mountains poses significant traffic safety risks, hindering local economic development. Efficient detection of hazardous road surface conditions (RSCs) is vital to address these [...] Read more.

The Tianshan Expressway plays a crucial role in China’s “Belt and Road” strategy, yet the extreme climate of the Tianshan Mountains poses significant traffic safety risks, hindering local economic development. Efficient detection of hazardous road surface conditions (RSCs) is vital to address these challenges. The complexity and variability of RSCs in the region, exacerbated by harsh weather, make traditional surveillance methods inadequate for real-time monitoring. To overcome these limitations, a vision-based artificial intelligence approach is urgently needed to ensure effective, real-time detection of dangerous RSCs in the Tianshan road network. This paper analyzes the primary structures and architectures of mainstream neural networks and explores their performance for RSC recognition through a comprehensive set of experiments, filling a research gap. Additionally, T-Net, specifically designed for the Tianshan Expressway engineering project, is built upon the optimal architecture identified in this study. Leveraging the split-transform-merge structure paradigm and asymmetric convolution, the model excels in capturing detailed information by learning features across multiple dimensions and perspectives. Furthermore, the integration of channel, spatial, and multi-head attention modules enhances the weighting of key features, making the T-Net particularly effective in recognizing the characteristics of snow-covered and icy road surfaces. All models presented in this paper were trained on a custom RSC dataset, compiled from various sources. Experimental results indicate that the T-Net outperforms fourteen once state-of-the-art (SOTA) models and three models specifically designed for RSC recognition, with 97.44% accuracy and 9.79% loss on the validation set. Full article

(This article belongs to the Special Issue Deep Learning and Computer Vision in Remote Sensing-III)

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15 pages, 4494 KiB

Open AccessCommunication

Analysis of the Grid Quantization for the Microwave Radar Coincidence Imaging Based on Basic Correlation Algorithm

by Yiheng Nian, Mengran Zhao, Die Li, Ming Zhang, Anxue Zhang, Tong Li and Shitao Zhu

Remote Sens. 2024, 16(19), 3726; https://doi.org/10.3390/rs16193726 - 7 Oct 2024

Viewed by 534

Abstract

In Microwave Radar Coincidence Imaging (MRCI), the imaging region is typically discretized into a fine grid. In other words, it assumes that the equivalent scatterers of the target are precisely located at the centers of these pre-discretized grids. However, this approach usually encounters [...] Read more.

In Microwave Radar Coincidence Imaging (MRCI), the imaging region is typically discretized into a fine grid. In other words, it assumes that the equivalent scatterers of the target are precisely located at the centers of these pre-discretized grids. However, this approach usually encounters the off-grid problem, which can significantly degrade the imaging performance. In this paper, to establish a criterion for grid quantization, the performance of the MRCI system related to the grid size and the distribution of imaging points is investigated. First, the discretization of the imaging scene is regarded as a random sampling problem, and the off-grid imaging model for MRCI is established. Then, the probability distribution function (PDF) of the imaging amplitude for a single point target is analyzed, and the mean first-order imaging error (MFE) for multiple point targets is derived based on the Basic Correlation Algorithm (BCA). Finally, the relationship between the grid quantization of the imaging area and the performance of the MRCI system is analyzed, providing a theoretical guidance for enhancing the performance of MRCI. The validity of the analyses is verified through simulation experiments. Full article

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27 pages, 11457 KiB

Open AccessArticle

From Polar Day to Polar Night: A Comprehensive Sun and Star Photometer Study of Trends in Arctic Aerosol Properties in Ny-Ålesund, Svalbard

by Sandra Graßl, Christoph Ritter, Jonas Wilsch, Richard Herrmann, Lionel Doppler and Roberto Román

Remote Sens. 2024, 16(19), 3725; https://doi.org/10.3390/rs16193725 - 7 Oct 2024

Viewed by 1114

Abstract

The climate impact of Arctic aerosols, like the Arctic Haze, and their origin are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study, we present a homogenised data set from a sun and star photometer operated in [...] Read more.

The climate impact of Arctic aerosols, like the Arctic Haze, and their origin are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study, we present a homogenised data set from a sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, of the 20 years from 2004–2023. Due to polar day and polar night, it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that autumn and winter are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn. A clear signal of large particles during the Arctic Haze can not be distinguished from large aerosols in winter. With autocorrelation analysis, we found that AOD events usually occur with a duration of several hours. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions, or wildfires in the Northern Hemisphere but did not find one single cause that clearly determines the Arctic AOD. Therefore the observed optical depth is a superposition of different aerosol sources. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Vertical Profile—Air Quality, Pollution and Aerosol Optical Properties)

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17 pages, 4863 KiB

Open AccessArticle

Effects of Extreme Climatic Events on the Autumn Phenology in Northern China Are Related to Vegetation Types and Background Climates

by Xinyue Gao, Zexing Tao and Junhu Dai

Remote Sens. 2024, 16(19), 3724; https://doi.org/10.3390/rs16193724 - 7 Oct 2024

Viewed by 862

Abstract

The increased intensity and frequency of extreme climate events (ECEs) have significantly impacted vegetation phenology, further profoundly affecting the structure and functioning of terrestrial ecosystems. However, the mechanisms by which ECEs affect the end of the growing season (EOS), a crucial phenological phase, [...] Read more.

The increased intensity and frequency of extreme climate events (ECEs) have significantly impacted vegetation phenology, further profoundly affecting the structure and functioning of terrestrial ecosystems. However, the mechanisms by which ECEs affect the end of the growing season (EOS), a crucial phenological phase, remain unclear. In this study, we first evaluated the temporal variations in the EOS anomalies in Northern China (NC) based on the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from 2001 to 2018. We then used event coincidence analysis (ECA) to assess the susceptibility of EOS to four ECEs (i.e., extreme heat, extreme cold, extreme wet and extreme dry events). Finally, we examined the dependence of the response of EOS to ECEs on background climate conditions. Our results indicated a slight decrease in the proportion of areas experiencing extreme heat and dry events (1.10% and 0.66% per year, respectively) and a slight increase in the proportion of areas experiencing extreme wet events (0.77% per year) during the preseason period. Additionally, EOS exhibited a delaying trend at a rate of 0.25 days/a during the study period. The susceptibility of EOS to ECEs was closely related to local hydrothermal conditions, with higher susceptibility to extreme dry and extreme hot events in drier and warmer areas and higher susceptibility to extreme cold and extreme wet events in wetter regions. Grasslands, in contrast to forests, were more sensitive to extreme dry, hot and cold events due to their weaker resistance to water deficits and cold stress. This study sheds light on how phenology responds to ECEs across various ecosystems and hydrothermal conditions. Our results could also provide a valuable guide for ecosystem management in arid regions. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems II)

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21 pages, 8820 KiB

Open AccessArticle

Predicting Gross Primary Productivity under Future Climate Change for the Tibetan Plateau Based on Convolutional Neural Networks

by Meimei Li, Zhongzheng Zhu, Weiwei Ren and Yingzheng Wang

Remote Sens. 2024, 16(19), 3723; https://doi.org/10.3390/rs16193723 - 7 Oct 2024

Viewed by 943

Abstract

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive [...] Read more.

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO₂ concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3–7.0 and SSP5–8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management. Full article

(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)

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Remote Sens., Volume 16, Issue 19 (October-1 2024) – 209 articles

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