Remote Sensing

21 pages, 5107 KiB

Open AccessArticle

Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods

by Yuanhong You, Yuhao Zhang, Yanyu Lu, Ying Hao, Zhiguang Tang and Haiyan Hou

Remote Sens. 2025, 17(2), 241; https://doi.org/10.3390/rs17020241 (registering DOI) - 11 Jan 2025

Abstract

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation [...] Read more.

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts. Full article

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

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23 pages, 5851 KiB

Open AccessArticle

Application of Nighttime Light Data Simulation Based on Multi-Indicator System and Machine Learning Model in Predicting Potentially Suitable Economic Development Areas: A Case Study of the Turpan–Hami Region

by Guangpeng Zhang, Li Zhang, Yiyang Chen, Meng Chen, Jingjing Tian and Yin Wu

Remote Sens. 2025, 17(2), 240; https://doi.org/10.3390/rs17020240 (registering DOI) - 11 Jan 2025

Abstract

In recent years, the accelerated urbanization process in China has led to increased land resource constraints and unregulated expansion, imposing significant pressure on ecosystems and the environment. As a critical node along the Silk Road Economic Belt, the Turpan–Hami region has experienced rapid [...] Read more.

In recent years, the accelerated urbanization process in China has led to increased land resource constraints and unregulated expansion, imposing significant pressure on ecosystems and the environment. As a critical node along the Silk Road Economic Belt, the Turpan–Hami region has experienced rapid urban development under policy support but faces challenges in resource utilization efficiency and sustainable development. To address these challenges, this study innovatively combines nighttime light remote sensing data to quantify urban economic development intensity and integrates socioeconomic and natural environment indicators based on previous research. Four tree-based ensemble learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)—were employed to predict potential urban economic development suitability zones and their suitability intensity. The results show that the CatBoost model performed the best in suitability prediction, revealing significant spatial disparities: high-suitability areas are concentrated in regions with superior resource conditions and well-developed infrastructure, whereas areas with terrain constraints and inadequate infrastructure exhibit lower suitability. An analysis of changes over historical periods (2010, 2015, and 2020) demonstrates a gradual expansion of high-suitability regions over time. Full article

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25 pages, 9783 KiB

Open AccessArticle

The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products

by Xuesong Bai, Zhengkun Qin, Juan Li, Shupeng Zhang and Lili Wang

Remote Sens. 2025, 17(2), 239; https://doi.org/10.3390/rs17020239 - 10 Jan 2025

Abstract

Soil moisture is a key factor affecting the exchange of heat and water between the land and the atmosphere. Land data assimilation (LDA) methods that leverage the strengths of both models and observations can generate more accurate initial conditions. However, soil moisture exhibits [...] Read more.

Soil moisture is a key factor affecting the exchange of heat and water between the land and the atmosphere. Land data assimilation (LDA) methods that leverage the strengths of both models and observations can generate more accurate initial conditions. However, soil moisture exhibits significant spatial heterogeneity, implying strong local characteristics for both observational and background errors. To elucidate the impact of error localization on LDA, we constructed a land data assimilation system (LDAS) suitable for the Common Land Model (CoLM), based on the simplified extended Kalman filter (SEKF) method. Through practical assimilation experiments using soil moisture retrieval products from the Soil Moisture Active Passive (SMAP) and Fenyun-3D (FY3D) satellites, we investigated the influence of spatial static and dynamic observational and background errors on LDA. The results indicate that by incorporating dynamic errors that account for the spatial heterogeneity of soil, LDAS can adaptively absorb observational information, thereby significantly enhancing assimilation impact and subsequent model forecast accuracy. Compared to experiments applying static errors, dynamic errors increased the spatial correlation coefficients by 17.4% and reduced the root mean square error (RMSE) by 11.2%. The results clearly demonstrate that for soil variable assimilation studies with strong spatial heterogeneity, progressively refined dynamic error estimation is a crucial direction for improving land surface assimilation performance. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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22 pages, 26419 KiB

Open AccessArticle

Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data

by Hao Wang, Rong Yang, Jianxin He, Qiangyu Zeng, Taisong Xiong, Zhihao Liu and Hongfei Jin

Remote Sens. 2025, 17(2), 238; https://doi.org/10.3390/rs17020238 - 10 Jan 2025

Abstract

Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this limitation, we introduce the [...] Read more.

Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this limitation, we introduce the Dual-Attention Recurrent Neural Network (DA-RNN), a model that combines satellite infrared (IR) data with radar-derived vertically integrated liquid (VIL) content. This model leverages the fundamental physical relationship between temperature and precipitation in a predictive framework that captures thermal and water vapor dynamics, thereby enhancing prediction accuracy. The results of experimental evaluations on the SEVIR dataset demonstrate that the DA-RNN model surpasses traditional methods on the test set. Notably, the DA-TrajGRU model achieves reductions in mean squared error (MSE) and mean absolute error (MAE) of 30 (

9.3 %

) and 89 (

6.4 %

), respectively, compared with those of the conventional TrajGRU model. Furthermore, our DA-RNN exhibits robust false alarm rates (FAR) for various thresholds, with only slight decreases in the critical success index (CSI) and Heidke skill score (HSS) when increasing the threshold. Additionally, we present a visualization of precipitation nowcasting, illustrating that the integration of multiple data sources effectively avoids overestimation of VIL values, further increasing the precision of precipitation forecasts. Full article

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16 pages, 5417 KiB

Open AccessTechnical Note

Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors?

by Liping Wang, Huanjun Liu, Xiang Wang, Xiaofeng Xu, Liyuan He, Chong Luo, Yong Li, Xinle Zhang, Deqiang Zang, Shufeng Zheng and Xiaodan Mei

Remote Sens. 2025, 17(2), 237; https://doi.org/10.3390/rs17020237 - 10 Jan 2025

Abstract

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we [...] Read more.

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R² > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions. Full article

(This article belongs to the Section Remote Sensing for Geospatial Science)

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17 pages, 1523 KiB

Open AccessTechnical Note

Bandlimited Frequency-Constrained Iterative Methods

by Harrison Garrett and David G. Long

Remote Sens. 2025, 17(2), 236; https://doi.org/10.3390/rs17020236 - 10 Jan 2025

Abstract

Variable aperture sampling reconstruction matrices have a history of being computationally intensive due to the need to compute a full matrix inverse. In the field of remote sensing, several spaceborne radiometers and scatterometers, which have irregular sampling and variable apertures, use iterative techniques [...] Read more.

Variable aperture sampling reconstruction matrices have a history of being computationally intensive due to the need to compute a full matrix inverse. In the field of remote sensing, several spaceborne radiometers and scatterometers, which have irregular sampling and variable apertures, use iterative techniques to reconstruct measurements of the Earth’s surface. However, many of these iterative techniques tend to over-amplify noise features outside the reconstructable bandwidth. Because the reconstruction of discrete samples is inherently bandlimited, solving a bandlimited inverse can focus on recovering signal features and prevent the over-amplification of noise outside the signal bandwidth. To approximate a bandlimited inverse, we apply bandlimited constraints to several well-known iterative reconstruction techniques: Landweber iteration, additive reconstruction technique (ART), Richardson–Lucy iteration, and conjugate gradient descent. In the context of these iterative techniques, we derive an iterative method for inverting variable aperture samples, taking advantage of the regular and irregular content of variable apertures. We find that this iterative method for variable aperture reconstruction is equivalent to solving a bandlimited conjugate gradient descent algorithm. Full article

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15 pages, 2064 KiB

Open AccessArticle

Assessing the Air Quality Impact of Train Operation at Tokyo Metro Shibuya Station from Portable Sensor Data

by Deepanshu Agarwal, Xuan Truong Trinh and Wataru Takeuchi

Remote Sens. 2025, 17(2), 235; https://doi.org/10.3390/rs17020235 - 10 Jan 2025

Abstract

Air pollution remains a critical global health concern, with 91% of the world’s population exposed to air quality exceeding World Health Organization (WHO) standards and indoor pollution causing approximately 3.8 million deaths annually due to incomplete fuel combustion. Subways, as major public transportation [...] Read more.

Air pollution remains a critical global health concern, with 91% of the world’s population exposed to air quality exceeding World Health Organization (WHO) standards and indoor pollution causing approximately 3.8 million deaths annually due to incomplete fuel combustion. Subways, as major public transportation modes in densely populated cities, can exhibit fine particulate matter (PM) levels that surpass safety limits, even in developed countries. Contributing factors include station location, ambient air quality, train frequency, ventilation efficiency, braking systems, tunnel structure, and electrical components. While elevated PM levels in underground platforms are recognized, the vertical and horizontal variations within stations are not well understood. This study examines the vertical and horizontal distribution of PM2.5 and PM10 levels at Shibuya Station, a structurally complex hub in the Tokyo Subway System. Portable sensors were employed to measure PM concentrations across different platform levels—both above and underground—and at various locations along the platforms. The results indicate that above-ground platforms have significantly lower PM2.5 and PM10 levels compared to underground platforms (17.09 μg/m³ vs. 22.73 μg/m³ for PM2.5; 39.54 μg/m³ vs. 56.98 μg/m³ for PM10). Notably, the highest pollution levels were found not at the deepest platform but at the one with the least effective ventilation. On the same platform, PM levels varied by up to 63.72% for PM2.5 and 120.23% for PM10, with elevated concentrations near the platform extremities compared to central areas. These findings suggest that ventilation efficiency plays a more significant role than elevation in vertical PM variation, while horizontal differences are likely influenced by piston effects from moving trains. This study underscores the risk of exposure to unsafe PM2.5 levels in underground platforms, particularly at platform extremities, highlighting the need for improved ventilation strategies to enhance air quality in subway environments. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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25 pages, 28841 KiB

Open AccessArticle

Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia

by Carl Bethuel, Damien Arvor, Thomas Corpetti, Julia Hélie, Adrià Descals, David Gaveau, Cécile Chéron-Bessou, Jérémie Gignoux and Samuel Corgne

Remote Sens. 2025, 17(2), 234; https://doi.org/10.3390/rs17020234 - 10 Jan 2025

Viewed by 71

Abstract

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of [...] Read more.

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis. Full article

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27 pages, 5909 KiB

Open AccessArticle

A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite

by Ge Gao, Ziti Jiao, Zhilong Li, Chenxia Wang, Jing Guo, Xiaoning Zhang, Anxin Ding, Zheyou Tan, Sizhe Chen, Fangwen Yang and Xin Dong

Remote Sens. 2025, 17(2), 233; https://doi.org/10.3390/rs17020233 - 10 Jan 2025

Viewed by 90

Abstract

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water [...] Read more.

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI_LOS is smaller than the CI_LFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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19 pages, 5023 KiB

Open AccessArticle

Effective First-Break Picking of Seismic Data Using Geometric Learning Methods

by Zhongyang Wen and Jinwen Ma

Remote Sens. 2025, 17(2), 232; https://doi.org/10.3390/rs17020232 - 10 Jan 2025

Viewed by 93

Abstract

Automatic first-break(FB) picking is a key task in seismic data processing, with numerous applications in the field. Over the past few years, both unsupervised and supervised learning algorithms have been applied to 2D seismic arrival time picking and obtained good picking results. In [...] Read more.

Automatic first-break(FB) picking is a key task in seismic data processing, with numerous applications in the field. Over the past few years, both unsupervised and supervised learning algorithms have been applied to 2D seismic arrival time picking and obtained good picking results. In this paper, we introduce a strategy of optimizing certain geometric properties of the _target curve for first-break picking which can be implemented in both unsupervised and supervised learning modes. Specifically, in the case of unsupervised learning, we design an effective curve evolving algorithm according to the active contour(AC) image segmentation model, in which the length of the _target curve and the fitting region energy are minimized together. It is interpretable, and its effectiveness and robustness are demonstrated by the experiments on real world seismic data. We further investigate three schemes of combining it with human interaction, which is shown to be highly useful in assisting data annotation or correcting picking errors. In the case of supervised learning especially for deep learning(DL) models, we add a curve loss term based on the _target curve geometry of first-break picking to the typical loss function. It is demonstrated by various experiments that this curve regularized loss function can greatly enhance the picking quality. Full article

(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)

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27 pages, 27746 KiB

Open AccessArticle

Multi-Criteria Assessment of Urban Thermal Hotspots: A GIS-Based Remote Sensing Approach in a Mediterranean Climate City

by Javier Sola-Caraballo, Antonio Serrano-Jiménez, Carlos Rivera-Gomez and Carmen Galan-Marin

Remote Sens. 2025, 17(2), 231; https://doi.org/10.3390/rs17020231 - 10 Jan 2025

Viewed by 104

Abstract

One of the most significant urban challenges focuses on addressing the effects of urban overheating as a consequence of climate change. Several methods have been developed to characterize urban heat islands (UHIs); however, the most widely used involve complex planning, huge time consumption, [...] Read more.

One of the most significant urban challenges focuses on addressing the effects of urban overheating as a consequence of climate change. Several methods have been developed to characterize urban heat islands (UHIs); however, the most widely used involve complex planning, huge time consumption, and substantial human and technical resources on field monitoring campaigns. Therefore, this study aims to provide an easily accessible and affordable remote sensing method for locating urban hotspots and addresses a multi-criteria assessment of urban heat-related parameters, allowing for a comprehensive city-wide evaluation. The novelty is based on leveraging the potential of the last Landsat 9 satellite, the application of kernel spatial interpolation, and GIS open access data, providing very high-resolution land surface temperature images over urban spaces. Within GIS workflow, the city is divided into LCZs, thermal hotspots are detected, and finally, it is analyzed to understand how urban factors, such as urban boundaries, building density, and vegetation, affect urban scale LST, all using graphical and analytical cross-assessment. The methodology has been tested in Seville, a representative warm Mediterranean city, where variations of up to 10 °C have been found between homogeneous residential areas. Thermal hotspots have been located, representing 11% of the total residential fabric, while results indicate a clear connection between the urban factors studied and overheating. The conclusions support the possibility of generating a powerful affordable tool for future research and the design of public policy renewal actions in vulnerable areas. Full article

(This article belongs to the Special Issue Multi-Platform Remote Sensing for the Modeling and Analysis of Smart Cities)

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7 pages, 1868 KiB

Open AccessCommunication

A Sentinel-2-Based System to Detect and Monitor Oil Spills: Demonstration on 2024 Tobago Accident

by Emilio D’Ugo, Ashish Kallikkattilkuruvila, Roberto Giuseppetti, Alejandro Carvajal, Abdou Mbacke Diouf, Matteo Tucci, Federico Aulenta, Alessandro Ursi, Patrizia Sacco, Deodato Tapete, Giovanni Laneve and Fabio Magurano

Remote Sens. 2025, 17(2), 230; https://doi.org/10.3390/rs17020230 - 10 Jan 2025

Viewed by 149

Abstract

In this paper, we analyze the serious environmental accident caused by a massive oil spill on 7 February 2024, off the island of Tobago, using two separate algorithms, namely, the established visible near-red index (VNRI) algorithm and the novel IVI visible reflectance ratio [...] Read more.

In this paper, we analyze the serious environmental accident caused by a massive oil spill on 7 February 2024, off the island of Tobago, using two separate algorithms, namely, the established visible near-red index (VNRI) algorithm and the novel IVI visible reflectance ratio index (IVI), both applied to Sentinel-2 satellite images. These algorithms were specifically designed to monitor oil spills in inner waters. In this paper, where the IVI is presented for the first time, its effectiveness in the open sea is also showcased allowing the identification and subsequent monitoring over time of the oily masses that threaten the coral reef of the island. The analysis suggests that with sufficient cloud-free conditions, high temporal revisit multispectral optical satellites could support the timely detection and tracking of oil masses during environmental incidents near natural sanctuaries. Full article

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26 pages, 5460 KiB

Open AccessArticle

Assessing Methods to Measure Stem Diameter at Breast Height with High Pulse Density Helicopter Laser Scanning

by Matthew J. Sumnall, Ivan Raigosa-Garcia, David R. Carter, Timothy J. Albaugh, Otávio C. Campoe, Rafael A. Rubilar, Bart Alexander, Christopher W. Cohrs and Rachel L. Cook

Remote Sens. 2025, 17(2), 229; https://doi.org/10.3390/rs17020229 - 10 Jan 2025

Viewed by 115

Abstract

Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal [...] Read more.

Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal size was estimated every 25 cm below the living crown, and a cubic spline was used to estimate where there were gaps. Individual stem diameter at breast height (DBH) was estimated for 77% of field-measured trees. The root mean square error (RMSE) of DBH estimates was 7–12 cm using stem circle fitting. Adapting the approach to use an existing stem taper model reduced the RMSE of estimates (<1 cm). In contrast, estimates that were produced from a previously existing DBH estimation method (PREV) could be achieved for 100% of stems (DBH RMSE 6 cm), but only after location-specific error was corrected. The stem classification method required comparatively little development of statistical models to provide estimates, which ultimately had a similar level of accuracy (RMSE < 1 cm) to PREV. HALS datasets can measure broad-scale forest plantations and reduce field efforts and should be considered an important tool for aiding in inventory creation and decision-making within forest management. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Structure Analysis and Forest Ecosystems Monitoring)

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18 pages, 3959 KiB

Open AccessArticle

A High Efficiency Discontinuous Galerkin Method for 3D Ground-Penetrating Radar Simulation

by Shuyang Xue, Changchun Yin, Jing Li, Jiao Zhu and Wuyang Liu

Remote Sens. 2025, 17(2), 228; https://doi.org/10.3390/rs17020228 - 9 Jan 2025

Viewed by 355

Abstract

As an effective geophysical tool, ground penetrating radar (GPR) is widely used for environmental and engineering detections. Numerous numerical simulation algorithms have been developed to improve the computational efficiency of GPR simulations, enabling the modeling of complex structures. The discontinuous Galerkin method is [...] Read more.

As an effective geophysical tool, ground penetrating radar (GPR) is widely used for environmental and engineering detections. Numerous numerical simulation algorithms have been developed to improve the computational efficiency of GPR simulations, enabling the modeling of complex structures. The discontinuous Galerkin method is a high efficiency numerical simulation algorithm which can deal with complex geometry. This method uses numerical fluxes to ensure the continuity between elements, allowing Maxwell’s equations to be solved within each element without the need to assemble a global matrix or solve large systems of linear equations. As a result, memory consumption can be significantly reduced, and parallel solvers can be applied at the element level, facilitating the construction of high-order schemes to enhance computational accuracy. In this paper, we apply the discontinuous Galerkin (DG) method based on unstructured meshes to 3D GPR simulation. To verify the accuracy of our algorithm, we simulate a full-space vacuum and a cuboid in a homogeneous medium and compare results, respectively, with the analytical solutions and those from the finite-difference method. The results demonstrate that, for the same error level, the proposed DG method has significant advantages over the FDTD method, with less than 20% of the memory consumption and calculation time. Additionally, we evaluate the effectiveness of our method by simulating _targets in an undulating subsurface, and further demonstrate its capability for simulating complex models. Full article

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17 pages, 1733 KiB

Open AccessArticle

Lightweight Multi-Scale Network for Segmentation of Riverbank Sand Mining Area in Satellite Images

by Hongyang Zhang, Shuo Liu and Huamei Liu

Remote Sens. 2025, 17(2), 227; https://doi.org/10.3390/rs17020227 - 9 Jan 2025

Viewed by 166

Abstract

Riverbank sand overexploitation is threatening the ecology and shipping safety of rivers. The rapid identification of riverbank sand mining areas from satellite images is extremely important for ecological protection and shipping management. Image segmentation methods based on AI technology are gradually becoming popular [...] Read more.

Riverbank sand overexploitation is threatening the ecology and shipping safety of rivers. The rapid identification of riverbank sand mining areas from satellite images is extremely important for ecological protection and shipping management. Image segmentation methods based on AI technology are gradually becoming popular in academia and industry. However, traditional neural networks have complex structures and numerous parameters, making them unsuitable for meeting the needs of rapid extraction in large areas. To improve efficiency, we proposed a lightweight multi-scale network (LMS Net), which uses a lightweight multi-scale (LMS) block in both the encoder and decoder. The lightweight multi-scale block combines parallel computing and depthwise convolution to reduce the parameters of the network and enhance its multi-scale extraction ability. We created a benchmark dataset to validate the accuracy and efficiency improvements of our network. Comparative experiments and ablation studies proved that our LMS Net is more efficient than traditional methods like Unet and more accurate than typical lightweight methods like Ghostnet and other more recent methods. The performance of our proposed network meets the requirements of river management. Full article

22 pages, 8532 KiB

Open AccessArticle

Dynamic Analysis of Spartina alterniflora in Yellow River Delta Based on U-Net Model and Zhuhai-1 Satellite

by Huiying Li, Guoli Cui, Haojie Liu, Qi Wang, Sheng Zhao, Xiao Huang, Rong Zhang, Mingming Jia, Dehua Mao, Hao Yu, Zongming Wang and Zhiyong Lv

Remote Sens. 2025, 17(2), 226; https://doi.org/10.3390/rs17020226 - 9 Jan 2025

Viewed by 232

Abstract

Coastal wetlands are critical for global biodiversity and ecological stability, yet the invasive Spartina alterniflora (S. alterniflora) poses severe threats to these ecosystems. This study evaluates the effectiveness of management efforts _targeting S. alterniflora in the Yellow River Delta (YRD) using [...] Read more.

Coastal wetlands are critical for global biodiversity and ecological stability, yet the invasive Spartina alterniflora (S. alterniflora) poses severe threats to these ecosystems. This study evaluates the effectiveness of management efforts _targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The U-Net model, coupled with the Relief-F algorithm, achieved a superior extraction accuracy (Kappa > 0.9 and overall accuracy of 93%) compared to traditional machine learning methods. From 2019 to 2021, S. alterniflora expanded rapidly, increasing from 4055.06 hm² to 6105.50 hm², primarily in tidal flats and water bodies. A clearing project reduced its extent to 5063.62 hm² by 2022, and by 2023, only 0.55 hm² remained. These results underscore the effectiveness of Shandong’s management policies but highlight the risk of regrowth due to the species’ resilience. Continuous monitoring and maintenance are essential to prevent its resurgence and ensure wetland restoration. This study offers critical insights into dynamic vegetation monitoring and informs conservation strategies for wetland health. Full article

(This article belongs to the Special Issue Land Cover Change Detection and Mapping Based on Remote Sensing and Artificial Intelligence)

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33 pages, 41733 KiB

Open AccessReview

A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms

by Jingfeng Shan, Lapo Miccinesi, Alessandra Beni, Lorenzo Pagnini, Andrea Cioncolini and Massimiliano Pieraccini

Remote Sens. 2025, 17(2), 225; https://doi.org/10.3390/rs17020225 - 9 Jan 2025

Viewed by 243

Abstract

The detection of Foreign Object Debris (FOD) is crucial for maintaining safety in critical areas like airport runways. This paper presents a comprehensive review of FOD detection technologies, covering traditional, radar-based, and artificial intelligence (AI)-driven methods. Manual visual inspection and optical sensors, while [...] Read more.

The detection of Foreign Object Debris (FOD) is crucial for maintaining safety in critical areas like airport runways. This paper presents a comprehensive review of FOD detection technologies, covering traditional, radar-based, and artificial intelligence (AI)-driven methods. Manual visual inspection and optical sensors, while widely used, are limited in scalability and reliability under adverse conditions. Radar technologies, such as millimeter-wave radar and synthetic aperture radar, offer robust performance, with advancements in algorithms and sensor fusion significantly enhancing their effectiveness. AI approaches, employing supervised and unsupervised learning, demonstrate potential for automating detection and improving precision, although challenges such as limited datasets and high computational demands persist. This review consolidates the recent progress across these domains, highlighting the need for integrated systems that combine radar and AI to improve adaptability, scalability, and small-FOD detection. By addressing these limitations, the study provides insights into future research directions and the development of innovative FOD detection solutions, contributing to safer and more efficient operational environments. Full article

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22 pages, 10787 KiB

Open AccessArticle

GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars

by Song Zhang, Yuntao Yang, Yilin Xie, Haoran Tang, Haiyang Li, Lianbi Yao and Yin Yang

Remote Sens. 2025, 17(2), 224; https://doi.org/10.3390/rs17020224 - 9 Jan 2025

Viewed by 216

Abstract

Traditional surveying methods have various drawbacks in monitoring cable-stayed bridge deformations. Global Navigation Satellite System (GNSS) technology is increasingly recognized for its critical role in structural deformation monitoring, providing precise measurements for various structural applications. Accurate signal extraction is essential for reliable deformation [...] Read more.

Traditional surveying methods have various drawbacks in monitoring cable-stayed bridge deformations. Global Navigation Satellite System (GNSS) technology is increasingly recognized for its critical role in structural deformation monitoring, providing precise measurements for various structural applications. Accurate signal extraction is essential for reliable deformation monitoring, as it directly influences the quality of the detected structural changes. However, effective signal extraction from GNSS data remains a challenging task due to the presence of noise and complex signal components. This study integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and wavelet packet decomposition (WPD) to extract GNSS deformation monitoring signals for the ropeway pillar. The proposed approach effectively mitigates high-frequency noise interference and modal mixing in GNSS signals, thereby enhancing the accuracy and reliability of deformation measurements. Simulation experiments and real-world scenario applications with operational field data processing demonstrate the effectiveness of the proposed method. This research contributes to advancing GNSS-based deformation monitoring techniques, offering a robust solution for detecting and analyzing subtle structural changes in various engineering contexts. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Infrastructure and Building Monitoring)

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28 pages, 9564 KiB

Open AccessArticle

Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects

by Aaron N. Schad, Molly K. Reif, Joseph H. Harwood, Christopher L. Macon, Lynde L. Dodd, Katie L. Vasquez, Kevin D. Philley, Glenn E. Dobson and Katie M. Steinmetz

Remote Sens. 2025, 17(2), 223; https://doi.org/10.3390/rs17020223 - 9 Jan 2025

Viewed by 313

Abstract

Traditional field vegetation plot surveys are critical for monitoring ecosystem restoration performance and include visual observations to quantitatively measure plants (e.g., species composition and abundance). However, surveys can be costly, time-consuming, and only provide data at discrete locations, leaving potential data gaps across [...] Read more.

Traditional field vegetation plot surveys are critical for monitoring ecosystem restoration performance and include visual observations to quantitatively measure plants (e.g., species composition and abundance). However, surveys can be costly, time-consuming, and only provide data at discrete locations, leaving potential data gaps across a site. Uncrewed aircraft system (UAS) technology can help fill data gaps between high-to-moderate spatial resolution (e.g., 1–30 m) satellite imagery, manned airborne data, and traditional field surveys, yet it has not been thoroughly evaluated in a virtual capacity as an alternative to traditional field vegetation plot surveys. This study assessed the utility of UAS red-green-blue (RGB) and low-altitude imagery for virtually surveying vegetation plots in a web application and compared to traditional field surveys at two coastal marsh restoration sites in southeast Louisiana, USA. Separate expert botanists independently observed vegetation plots in the field vs. using UAS imagery in a web application to identify growth form, species, and coverages. Taxa richness and assemblages were compared between field and virtual vegetation plot survey results using taxa resolution (growth-form and species-level) and data collection type (RGB imagery, Anafi [low-altitude] imagery, or field data) to assess accuracy. Virtual survey results obtained using Anafi low-altitude imagery compared better to field data than those from RGB imagery, but they were dependent on growth-form or species-level resolution. There were no significant differences in taxa richness between all survey types for a growth-form level analysis. However, there were significant differences between each survey type for species-level identification. The number of species identified increased by approximately two-fold going from RGB to Anafi low-altitude imagery and another two-fold from Anafi low-altitude imagery to field data. Vegetation community assemblages were distinct between the two marsh sites, and similarity percentages were higher between Anafi low-altitude imagery and field data compared to RGB imagery. Graminoid identification mismatches explained a high amount of variance between virtual and field similarity percentages due to the challenge of discriminating between them in a virtual setting. The higher level of detail in Anafi low-altitude imagery proved advantageous for properly identifying lower abundance species. These identifications included important taxa, such as invasive species, that were overlooked when using RGB imagery. This study demonstrates the potential utility of high-resolution UAS imagery for increasing marsh vegetation monitoring efficiencies to improve ecosystem management actions and outcomes. Restoration practitioners can use these results to better understand the level of accuracy for identifying vegetation growth form, species, and coverages from UAS imagery compared to field data to effectively monitor restored marsh ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)

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17 pages, 6492 KiB

Open AccessArticle

Correction of CAMS PM₁₀ Reanalysis Improves AI-Based Dust Event Forecast

by Ron Sarafian, Sagi Nathan, Dori Nissenbaum, Salman Khan and Yinon Rudich

Remote Sens. 2025, 17(2), 222; https://doi.org/10.3390/rs17020222 - 9 Jan 2025

Viewed by 313

Abstract

High dust loading significantly impacts air quality, climate, and public health. Early warning is crucial for mitigating short-term effects, and accurate dust field estimates are needed for forecasting. The Copernicus Atmosphere Monitoring Service (CAMS) offers global reanalysis datasets and forecasts of particulate matter [...] Read more.

High dust loading significantly impacts air quality, climate, and public health. Early warning is crucial for mitigating short-term effects, and accurate dust field estimates are needed for forecasting. The Copernicus Atmosphere Monitoring Service (CAMS) offers global reanalysis datasets and forecasts of particulate matter with a diameter of under 10 μm (PM₁₀), which approximate dust, but recent studies highlight discrepancies between CAMS data and ground in-situ measurements. Since CAMS is often used for forecasting, errors in PM₁₀ fields can hinder accurate dust event forecasts, which is particularly challenging for models that use artificial intelligence (AI) due to the scarcity of dust events and limited training data. This study proposes a machine-learning approach to correct CAMS PM₁₀ fields using in-situ data to enhance AI-based dust event forecasting. A correction model that links pixel-wise errors with atmospheric and meteorological variables was taught using gradient-boosting algorithms. This model is then utilized to predict CAMS error in previously unobserved pixels across the Eastern Mediterranean, generating CAMS error fields. Our bias-corrected PM₁₀ fields are, on average, 12 μg m⁻³ more accurate, often reducing CAMS errors by significant percentages. To evaluate the contribution, we train a deep neural network to predict city-scale dust events (0–72 h) over the Balkans using PM₁₀ fields. Comparing the network’s performance when trained on both original and bias-corrected CAMS PM₁₀ fields, we show that the correction improves AI-based forecasting performance across all metrics. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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25 pages, 7245 KiB

Open AccessArticle

Long-Term Evaluation of GCOM-C/SGLI Reflectance and Water Quality Products: Variability Among JAXA G-Portal and JASMES

by Salem Ibrahim Salem, Mitsuhiro Toratani, Hiroto Higa, SeungHyun Son, Eko Siswanto and Joji Ishizaka

Remote Sens. 2025, 17(2), 221; https://doi.org/10.3390/rs17020221 - 9 Jan 2025

Viewed by 243

Abstract

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Global Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including the unique 380 nm band. After six years in orbit, [...] Read more.

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Global Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including the unique 380 nm band. After six years in orbit, a comprehensive evaluation of SGLI products and their temporal consistency is needed. Remote sensing reflectance (

R_{r s}

) is the primary product for monitoring water quality, forming the basis for deriving key oceanic constituents such as chlorophyll-a (Chla) and total suspended matter (TSM). The Japan Aerospace Exploration Agency (JAXA) provides

R_{r s}

products through two platforms, G-Portal and JASMES, each employing different atmospheric correction methodologies and assumptions. This study aims to evaluate the SGLI full-resolution

R_{r s}

products from G-Portal and JASMES at regional scales (Japan and East Asia) and assess G-Portal

R_{r s}

products globally between January 2018 and December 2023. The evaluation employs in situ matchups from NASA’s Aerosol Robotic Network-Ocean Color (AERONET-OC) and cruise measurements. We also assess the retrieval accuracy of two water quality indices, Chla and TSM. The AERONET-OC data analysis reveals that JASMES systematically underestimates

R_{r s}

values at shorter wavelengths, particularly at 412 nm. While the

R_{r s}

accuracy at 412 nm is relatively low, G-Portal’s

R_{r s}

products perform better than JASMES at shorter wavelengths, showing lower errors and stronger correlations with AERONET-OC data. Both G-Portal and JASMES show lower agreement with AERONET-OC and cruise datasets at shorter wavelengths but demonstrate improved agreement at longer wavelengths (530 nm, 565 nm, and 670 nm). JASMES generates approximately 12% more matchup data points than G-Portal, likely due to G-Portal’s stricter atmospheric correction thresholds that exclude pixels with high reflectance. In situ measurements indicate that G-Portal provides better overall agreement, particularly at lower

R_{r s}

magnitudes and Chla concentrations below 5 mg/m³. This evaluation underscores the complexities and challenges of atmospheric correction, particularly in optically complex coastal waters (Case 2 waters), which may require tailored atmospheric correction methods different from the standard approach. The assessment of temporal consistency and seasonal variations in

R_{r s}

data shows that both platforms effectively capture interannual trends and maintain temporal stability, particularly from the 490 nm band onward, underscoring the potential of SGLI data for long-term monitoring of coastal and oceanic environments. Full article

(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products (Second Edition))

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15 pages, 70657 KiB

Open AccessArticle

High-Resolution Satellite Reveals the Methane Emissions from China’s Coal Mines

by Xingyu Li, Tianhai Cheng, Hao Zhu, Xiaotong Ye, Donghao Fan, Tao Tang, Haoran Tong, Shizhe Yin and Jingyu Xiong

Remote Sens. 2025, 17(2), 220; https://doi.org/10.3390/rs17020220 - 9 Jan 2025

Viewed by 338

Abstract

Coal mines are a major global source of methane emissions, accounting for 10% of global methane emissions. As the world’s largest coal producer and consumer, China has various coal mine types, yet significant uncertainty exists in its methane emissions due to a lack [...] Read more.

Coal mines are a major global source of methane emissions, accounting for 10% of global methane emissions. As the world’s largest coal producer and consumer, China has various coal mine types, yet significant uncertainty exists in its methane emissions due to a lack of systematic ground-based data. Therefore, accurately quantifying methane emissions from coal mining activities is crucial. Existing inventories struggle to capture complex and anomalous emissions, while medium-resolution satellites lack facility-level precision. High-spatial-resolution satellite observations offer detailed insights. With a spatial resolution of 60 m and spectral channels from 381 to 2493 nm, the EMIT satellite can finely characterize facility-level methane plumes. This study uses data from 88 methane emission plumes captured by the EMIT satellite to quantify the methane emission characteristics of 32 coal mines located in Inner Mongolia, Ningxia Hui Autonomous Region, and Shanxi Province, China. Principal Component Analysis reveals that mine size, coal type, and processing stage are key factors influencing methane emissions, with emission rates varying significantly under different conditions. Data indicate varying methane emission rates across production stages. The median methane emission rate in gas treatment/utilization is double that of ventilation shafts and chemical plants. Larger coal mines show a decreasing trend in the unit methane emission rate with scale increase, with super-large mines emitting only one-tenth that of medium-sized mines. For large coal mines, bituminous coal mines emit nearly double that of anthracite coal mines. Bottom-up emission inventory evaluation results for the 32 coal mines studied show that EDGAR v8.0 and GFEI v2 underestimated annual methane total emissions, capturing only about half of the emissions quantified through satellite observations. The average emission intensity of the 32 coal mines estimated by satellite data is 0.48 kg/GJ, which is higher than the emission intensities reported by EDGAR v8.0 (0.24 kg/GJ) and GFEI v2 (0.18 kg/GJ). Overall, high-resolution satellite data offer new insights into facility-level emissions, revealing the complexity of methane emissions from coal mines and underscoring the need for tailored mitigation strategies that consider different mine types and operational stages. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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23 pages, 7230 KiB

Open AccessArticle

Assessment of Ecosystem Vulnerability in the Tropic of Cancer (Yunnan Section)

by Hui Ye, Die Bai, Jinliang Wang, Shucheng Tan and Shiyin Liu

Remote Sens. 2025, 17(2), 219; https://doi.org/10.3390/rs17020219 - 9 Jan 2025

Viewed by 188

Abstract

The stability and diversity of the natural landscape is critical to maintaining the ecological functions of a region. However, ecosystems in the Yunnan section of the Tropic of Cancer face increasing pressure from climate change, human activities, and natural disasters, which significantly influence [...] Read more.

The stability and diversity of the natural landscape is critical to maintaining the ecological functions of a region. However, ecosystems in the Yunnan section of the Tropic of Cancer face increasing pressure from climate change, human activities, and natural disasters, which significantly influence their vulnerability. Ecosystem vulnerability is determined by structural and functional sensitivity, coupled with insufficient adaptability to external stressors. While previous research has emphasized the effects of climate change, the multidimensional impacts of land use and human activities have often been overlooked. This study aims to comprehensively assess the ecological vulnerability of the Yunnan section of the Tropic of Cancer, addressing this research gap by utilizing geographic information system (GIS) technology and the Vulnerability Scoping Diagram (VSD) model. The study constructs a multidimensional evaluation index system based on exposure, sensitivity, and adaptive capacity, with a specific focus on the effects of land use, human activities, and natural disasters. Key indicators include road and population density, soil erosion, and geological hazards, along with innovative considerations of economic adaptive capacity to address gaps in previous assessments. The findings highlight that ecological vulnerability is predominantly concentrated in areas with low vegetation cover and severe soil erosion. Human activities, particularly road and population density, are identified as significant drivers of ecological vulnerability. Sensitivity is heavily influenced by soil erosion and geological disasters, while economic adaptability emerges as a critical factor in mitigating ecological risks. By proposing _targeted policy recommendations—such as enhancing ecological protection and restoration, optimizing land use planning, and increasing public environmental awareness—this study provides actionable strategies to reduce ecological vulnerability. The findings offer crucial scientific support for improving the ecological environment in the Tropic of Cancer region and contribute to achieving sustainable development goals. Full article

(This article belongs to the Section Ecological Remote Sensing)

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17 pages, 605 KiB

Open AccessCommunication

Coherent Signal DOA Estimation Method Based on Space–Time–Coding Metasurface

by Guanchao Chen, Xiaolong Su, Lida He, Dongfang Guan and Zhen Liu

Remote Sens. 2025, 17(2), 218; https://doi.org/10.3390/rs17020218 - 9 Jan 2025

Viewed by 213

Abstract

A novel method for the direction of arrival (DOA) estimation of coherent signals under a space–time–coding metasurface (STCM) is proposed in this paper. Noticeably, the STCM can replace multi-channel arrays with a single channel, which can be utilized to modulate incident electromagnetic waves [...] Read more.

A novel method for the direction of arrival (DOA) estimation of coherent signals under a space–time–coding metasurface (STCM) is proposed in this paper. Noticeably, the STCM can replace multi-channel arrays with a single channel, which can be utilized to modulate incident electromagnetic waves and generate harmonics. However, coherent signals are overlapping in the frequency spectrum and cannot achieve DOA estimation through subspace methods. Therefore, the proposed method transforms the angle information in the time domain into amplitude and phase information at harmonics in the frequency domain by modulating incident coherent signals using the STCM and performing a fast Fourier transform (FFT) on these signals. Based on the harmonics in the frequency spectrum of the coherent signals, appropriate harmonics are selected. Finally, the ℓ₁ norm singular value decomposition (ℓ₁-SVD) algorithm is utilized for achieving high-precision DOA estimation. Simulation experiments are conducted to show the performance of the proposed method under the condition of different incident angles, harmonic numbers, signal-to-noise ratios (SNRs), etc. Compared to the traditional algorithms, the performance of the proposed algorithm can achieve more accurate DOA estimation under a low SNR. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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19 pages, 13029 KiB

Open AccessArticle

Object-Level Contrastive-Learning-Based Multi-Branch Network for Building Change Detection from Bi-Temporal Remote Sensing Images

by Shiming Li, Fengtao Yan, Cheng Liao, Qingfeng Hu, Kaifeng Ma, Wei Wang and Hui Zhang

Remote Sens. 2025, 17(2), 217; https://doi.org/10.3390/rs17020217 - 9 Jan 2025

Viewed by 227

Abstract

Buildings are fundamental elements of human environments, and detecting changes in them is crucial for land cover studies, urban expansion monitoring, and the detection of illegal construction activities. Existing methods primarily focus on pixel-level differences in bi-temporal remote sensing imagery. However, pseudo-changes, such [...] Read more.

Buildings are fundamental elements of human environments, and detecting changes in them is crucial for land cover studies, urban expansion monitoring, and the detection of illegal construction activities. Existing methods primarily focus on pixel-level differences in bi-temporal remote sensing imagery. However, pseudo-changes, such as variations in non-building areas caused by differences in illumination, seasonal changes, and other factors, pose significant challenges for reliable building change detection. To address these issues, we propose a novel object-level contrastive-learning-based multi-branch network (OCL-Net) for detecting building changes by integrating bi-temporal remote sensing images. First, we design a multi-head decoder to separately extract more distinguishable building change features and auxiliary semantic features from bi-temporal images, effectively leveraging building-specific priors. Second, an object-level contrastive learning loss is designed and jointly optimized with a pixel-level similarity loss to ensure the global consistency of buildings. Finally, an attention-based discriminative feature generation and fusion block is designed to enhance the representation of multi-scale change features. We validate the effectiveness of the proposed method through comparative experiments on the publicly available WHU-CD and S2Looking datasets. Our approach achieves IoU values of 88.54% and 51.94%, respectively, surpassing state-of-the-art methods for building change detection. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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34 pages, 11564 KiB

Open AccessArticle

Derivation of Hyperspectral Profiles for Global Extended Pseudo Invariant Calibration Sites (EPICS) and Their Application in Satellite Sensor Cross-Calibration

by Juliana Fajardo Rueda, Larry Leigh, Morakot Kaewmanee, Harshitha Byregowda and Cibele Teixeira Pinto

Remote Sens. 2025, 17(2), 216; https://doi.org/10.3390/rs17020216 - 9 Jan 2025

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Abstract

This study presents the selection of 20 Extended Pseudo Invariant Calibration Sites (EPICS) for radiometric calibration and the derivation of their hyperspectral profiles using the DLR Earth Sensing Imaging Spectrometer (DESIS) and Hyperion data. The hyperspectral profile of one of these clusters, the [...] Read more.

This study presents the selection of 20 Extended Pseudo Invariant Calibration Sites (EPICS) for radiometric calibration and the derivation of their hyperspectral profiles using the DLR Earth Sensing Imaging Spectrometer (DESIS) and Hyperion data. The hyperspectral profile of one of these clusters, the GONA-EPICS cluster, was validated against ground truth measurements from the RadCalNet Gobabeb Namibia (GONA) site, demonstrating statistical agreement within their respective uncertainties through Welch’s test. The applicability of these hyperspectral profiles was further evaluated by generating Spectral Band Adjustment Factor (SBAF) between Landsat 8 and Sentinel-2A using the GONA-EPICS hyperspectral profile and comparing them to SBAF values derived from RadCalNet GONA site measurements. SBAF results were statistically the same, while SBAF derived from the combined DESIS and Hyperion data exhibited reduced uncertainty compared to those derived using Hyperion data alone, which is attributed to DESIS’s finer spectral resolution (2.5 nm vs. 10 nm). To assess EPICS applicability in cross-calibration, Cluster 13-GTS, which includes pixels from the Libya 4 CNES ROI, was used as a _target. Cross-calibration gains obtained using EPICS and the T2T cross-calibration methodology were compared to those from the traditional cross-calibration approach using Libya 4 CNES ROI. Results demonstrated statistically similar gains, with EPICS achieving an uncertainty better than 6% across all bands compared to 4.4% for the traditional method, while enabling global coverage for daily cross-calibration opportunities. This study introduces globally distributed EPICS with validated hyperspectral profiles, offering enhanced spectral resolution and reliability for radiometric calibration and stability monitoring. The methodology supports efficient global scale sensor calibration and prepares for future hyperspectral missions. Full article

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24 pages, 10895 KiB

Open AccessArticle

Orthogonal Capsule Network with Meta-Reinforcement Learning for Small Sample Hyperspectral Image Classification

by Prince Yaw Owusu Amoako, Guo Cao, Boshan Shi, Di Yang and Benedict Boakye Acka

Remote Sens. 2025, 17(2), 215; https://doi.org/10.3390/rs17020215 - 9 Jan 2025

Viewed by 177

Abstract

Most current hyperspectral image classification (HSIC) models require a large number of training samples, and when the sample size is small, the classification performance decreases. To address this issue, we propose an innovative model that combines an orthogonal capsule network with meta-reinforcement learning [...] Read more.

Most current hyperspectral image classification (HSIC) models require a large number of training samples, and when the sample size is small, the classification performance decreases. To address this issue, we propose an innovative model that combines an orthogonal capsule network with meta-reinforcement learning (OCN-MRL) for small sample HSIC. The OCN-MRL framework employs Meta-RL for feature selection and CapsNet for classification with a small data sample. The Meta-RL module through clustering, augmentation, and multiview techniques enables the model to adapt to new HSIC tasks with limited samples. Learning a meta-policy with a Q-learner generalizes across different tasks to effectively select discriminative features from the hyperspectral data. Integrating orthogonality into CapsNet reduces the network complexity while maintaining the ability to preserve spatial hierarchies and relationships in the data with a 3D convolution layer, suitably capturing complex patterns. Experimental results on four rich Chinese hyperspectral datasets demonstrate the OCN-MRL model’s competitiveness in both higher classification accuracy and less computational cost compared to existing CapsNet-based methods. Full article

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23 pages, 10942 KiB

Open AccessArticle

MambaShadowDet: A High-Speed and High-Accuracy Moving _target Shadow Detection Network for Video SAR

by Xiaowo Xu, Tianwen Zhang, Xiaoling Zhang, Wensi Zhang, Xiao Ke and Tianjiao Zeng

Remote Sens. 2025, 17(2), 214; https://doi.org/10.3390/rs17020214 - 9 Jan 2025

Viewed by 239

Abstract

Existing convolution neural network (CNN)-based video synthetic aperture radar (SAR) moving _target shadow detectors are difficult to model long-range dependencies, while transformer-based ones often suffer from greater complexity. To handle these issues, this paper proposes MambaShadowDet, a novel lightweight deep learning (DL) detector [...] Read more.

Existing convolution neural network (CNN)-based video synthetic aperture radar (SAR) moving _target shadow detectors are difficult to model long-range dependencies, while transformer-based ones often suffer from greater complexity. To handle these issues, this paper proposes MambaShadowDet, a novel lightweight deep learning (DL) detector based on a state space model (SSM), dedicated to high-speed and high-accuracy moving _target shadow detection in video SAR images. By introducing SSM with the linear complexity into YOLOv8, MambaShadowDet effectively captures the global feature dependencies while relieving computational load. Specifically, it designs Mamba-Backbone, combining SSM and CNN to effectively extract both global contextual and local spatial information, as well as a slim path aggregation feature pyramid network (Slim-PAFPN) to enhance multi-level feature extraction and further reduce complexity. Abundant experiments on the Sandia National Laboratories (SNL) video SAR data show that MambaShadowDet achieves superior moving _target shadow detection performance with a detection accuracy of 80.32% F1 score and an inference speed of 44.44 frames per second (FPS), outperforming existing models in both accuracy and speed. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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23 pages, 8216 KiB

Open AccessFeature PaperArticle

Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction

by Chandan Kumar, Gabriel Walton, Paul Santi and Carlos Luza

Remote Sens. 2025, 17(2), 213; https://doi.org/10.3390/rs17020213 - 9 Jan 2025

Viewed by 244

Abstract

Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. The performance statistics of these models are vital for assessing their reliability, which is typically obtained using the random cross-validation (R-CV) method. However, R-CV has a major drawback, [...] Read more.

Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. The performance statistics of these models are vital for assessing their reliability, which is typically obtained using the random cross-validation (R-CV) method. However, R-CV has a major drawback, i.e., it ignores the spatial autocorrelation (SAC) inherent in spatial datasets when partitioning the training and testing sets. We assessed the impact of SAC at three crucial phases of ML modeling: hyperparameter tuning, performance evaluation, and learning curve analysis. As an alternative to R-CV, we used spatial cross-validation (S-CV). This method considers SAC when partitioning the training and testing subsets. This experiment was conducted on regional landslide susceptibility prediction using different ML models: logistic regression (LR), k-nearest neighbor (KNN), linear discriminant analysis (LDA), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and C5.0. The experimental results showed that R-CV often produces optimistic performance estimates, e.g., 6–18% higher than those obtained using the S-CV. R-CV also occasionally fails to reveal the true importance of the hyperparameters of models such as SVM and ANN. Additionally, R-CV falsely portrays a considerable improvement in model performance as the number of variables increases. However, this was not the case when the models were evaluated using S-CV. The impact of SAC was more noticeable in complex models such as SVM, RF, and C5.0 (except for ANN) than in simple models such as LDA and LR (except for KNN). Overall, we recommend S-CV over R-CV for a reliable assessment of ML model performance in large-scale LSM. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology: 3rd Edition)

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21 pages, 14704 KiB

Open AccessArticle

Effectiveness Trade-Off Between Green Spaces and Built-Up Land: Evaluating Trade-Off Efficiency and Its Drivers in an Expanding City

by Xinyu Dong, Yanmei Ye, Tao Zhou, Dagmar Haase and Angela Lausch

Remote Sens. 2025, 17(2), 212; https://doi.org/10.3390/rs17020212 - 9 Jan 2025

Viewed by 259

Abstract

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological [...] Read more.

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km² grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou’s periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion. Full article

(This article belongs to the Section Ecological Remote Sensing)

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