Remote Sensing

29 pages, 14662 KB

Open AccessArticle

Assessment of Long-Term Photovoltaic (PV) Power Potential in China Based on High-Quality Solar Radiation and Optimal Tilt Angles of PV Panels

by Wenbo Zhao, Xiaotong Zhang, Shuyue Yang, Yanjun Duan, Lingfeng Lu, Xinpei Han, Lingchen Bu, Run Jia and Yunjun Yao

Remote Sens. 2025, 17(18), 3235; https://doi.org/10.3390/rs17183235 (registering DOI) - 18 Sep 2025

Abstract

Solar photovoltaic (PV) plays a crucial role in China’s pursuit of carbon neutrality. Assessing the PV power potential over China is essential for future energy planning and policy making. Surface solar radiation and panel tilt angle are critical factors influencing PV power generation. [...] Read more.

Solar photovoltaic (PV) plays a crucial role in China’s pursuit of carbon neutrality. Assessing the PV power potential over China is essential for future energy planning and policy making. Surface solar radiation and panel tilt angle are critical factors influencing PV power generation. However, existing solar radiation datasets cannot fully meet assessment needs due to insufficient temporal coverage and limited accuracy, and the impact of panel tilt angles on PV potential is largely overlooked. This study developed a PV power estimation framework to assess the long-term (1980–2019) PV power potential at 609 stations across China, based on reconstructed high-quality solar radiation and optimized tilt angles. The validation of PV power estimates using ground measured outputs from four operational PV power stations indicated a correlation coefficient of 0.67 and a root mean square error of 0.07 for estimated daily capacity factor (CF). The assessment results revealed that the multi-year mean CF of China is 0.149 ± 0.031, with higher potentials in northern provinces and lower in southern provinces. The mean annual CF shows a declining trend of −7 × 10⁻⁴ per decade during 1980–2019, with significant decreases primarily in heavily polluted regions. In addition, we propose an optimal tilt angle estimation model based on diffuse fraction, achieving higher accuracy than previously released models. The estimated optimal tilt angle results in an increase in PV energy yield by 14.9 TWh/year for China compared with latitude-based schemes, based on China’s cumulative PV capacity by 2023 (609 GW). Our findings provide valuable insights for the effective implementation of solar PV projects in China. Full article

39 pages, 6963 KB

Open AccessArticle

Closed and Structural Optimization for 3D Line Segment Extraction in Building Point Clouds

by Ruoming Zhai, Xianquan Han, Peng Wan, Jianzhou Li, Yifeng He and Bangning Ding

Remote Sens. 2025, 17(18), 3234; https://doi.org/10.3390/rs17183234 (registering DOI) - 18 Sep 2025

Abstract

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from [...] Read more.

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from incomplete and fragmented contours, with missing or misaligned intersections. To overcome these limitations, this study proposes a patch-level framework for 3D line extraction and structural optimization from building point clouds. The proposed method first partitions point clouds into planar patches and establishes local image planes for each patch, enabling a structured 2D representation of unstructured 3D data. Then, graph-cut segmentation is proposed to extract compact boundary contours, which are vectorized into closed lines and back-projected into 3D space to form the initial line segments. To improve geometric consistency, regularized geometric constraints, including adjacency, collinearity, and orthogonality constraints, are further designed to merge homogeneous segments, refine topology, and strengthen structural outlines. Finally, we evaluated the approach on three indoor building environments and four outdoor scenes, and experimental results show that it reduces noise and redundancy while significantly improving the completeness, closure, and alignment of 3D line features in various complex architectural structures. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

29 pages, 9737 KB

Open AccessArticle

ER-PASS: Experience Replay with Performance-Aware Submodular Sampling for Domain-Incremental Learning in Remote Sensing

by Yeseok Lee, Donghyeon Lee, Taehong Kwak and Yongil Kim

Remote Sens. 2025, 17(18), 3233; https://doi.org/10.3390/rs17183233 - 18 Sep 2025

Abstract

In recent years, deep learning has become a dominant research trend in the field of remote sensing. However, due to significant domain discrepancies among datasets collected from various platforms, models trained on a single domain often struggle to generalize to other domains. In [...] Read more.

In recent years, deep learning has become a dominant research trend in the field of remote sensing. However, due to significant domain discrepancies among datasets collected from various platforms, models trained on a single domain often struggle to generalize to other domains. In domain-incremental learning scenarios, such discrepancies often lead to catastrophic forgetting, hindering the practical deployment of deep learning models. To address this, we propose ER-PASS, an experience replay-based continual learning algorithm that incorporates a performance-aware submodular sampling strategy. ER-PASS balances adaptability across domains and retention of knowledge by combining the strengths of joint learning and experience replay, while maintaining practical efficiency in terms of training time and memory usage. We validated our method on two remote sensing applications—building segmentation and land use/land cover (LULC) classification—using UNet and DeepLabV3+. Experimental results show that ER-PASS consistently outperforms existing continual learning methods in average incremental accuracy (AIA) and backward transfer (BWT), ensuring generalization across domains and mitigating catastrophic forgetting. While these results were obtained under restricted conditions, limited to a sequence of domains from high to low resolution and two applications, they underscore the potential of ER-PASS as a practical and general-purpose solution for continual learning in remote sensing. Full article

21 pages, 12581 KB

Open AccessArticle

An Efficient RMA with Chunked Nonlinear Normalized Weights and SNR-Based Multichannel Fusion for MIMO-SAR Imaging

by Jingjing Wang, Hao Chen, Haowei Duan, Rongbo Sun, Kehui Yang, Jing Fang, Huaqiang Xu and Pengbo Song

Remote Sens. 2025, 17(18), 3232; https://doi.org/10.3390/rs17183232 - 18 Sep 2025

Abstract

Millimeter-wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) has been widely used in many scenarios such as geological exploration, post-disaster rescue, and security inspection. When faced with large complex scenes, the signal suffers from distortion problems due to amplitude-phase nonlinear aberrations, resulting in undesired [...] Read more.

Millimeter-wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) has been widely used in many scenarios such as geological exploration, post-disaster rescue, and security inspection. When faced with large complex scenes, the signal suffers from distortion problems due to amplitude-phase nonlinear aberrations, resulting in undesired artifacts. Many previous studies eliminate artifacts but result in missing target structures. In this paper, we propose to use chunked nonlinear normalized weights in conjunction with signal-to-noise ratio-based (SNR-based) multichannel fusion to address the above-mentioned problems. The chunked nonlinear normalized weights make use of the scene’s characteristics to separately perform the optimization of different regions of the scene. This approach significantly mitigates the effects of amplitude-phase distortion on signal quality, thereby facilitating the effective suppression of noise and artifacts. Applying SNR-based multichannel fusion solves the problem of missing target structures caused by the chunked weights. With the proposed techniques, we can effectively suppress artifacts and noise while maintaining the target structures to enhance the robustness of system. Based on practical experiments, the proposed techniques achieve the image entropy (IE) value, which reduces by approximately 1, and the image contrast (IC) value is increased by approximately 2~4. Furthermore, the computational time is only about 1.3 times that needed by the latest reported algorithm. Consequently, imaging resolution and system robustness are improved by implementing these techniques. Full article

(This article belongs to the Special Issue Role of SAR/InSAR Techniques in Investigating Ground Deformation)

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25 pages, 2727 KB

Open AccessArticle

Seventeen-Year Reconstruction of Tropical Forest Aboveground Biomass Dynamics in Borneo Using GEDI L4B and Multi-Sensor Data Fusion

by Chao Yang, Aobo Liu and Yating Chen

Remote Sens. 2025, 17(18), 3231; https://doi.org/10.3390/rs17183231 - 18 Sep 2025

Abstract

Forest aboveground biomass (AGB) is a key component of terrestrial carbon storage, essential for understanding the carbon cycle and evaluating carbon sink potential. However, estimating long-term AGB in tropical forests and detecting its spatial and temporal trends remain challenging due to observational gaps [...] Read more.

Forest aboveground biomass (AGB) is a key component of terrestrial carbon storage, essential for understanding the carbon cycle and evaluating carbon sink potential. However, estimating long-term AGB in tropical forests and detecting its spatial and temporal trends remain challenging due to observational gaps and methodological constraints. Here, we integrate GEDI L4B gridded biomass data with features from MODIS, PALSAR/PALSAR-2, SRTM, and climate datasets, and apply the AutoGluon ensemble learning framework to develop AGB retrieval models. We generated annual AGB maps at 1 km resolution for Borneo’s forests from 2007 to 2023, achieving high predictive accuracy (R² = 0.92, RMSE = 32.84 Mg/ha, rRMSE = 21.06%). Residuals were generally balanced and close to a symmetric distribution, indicating no strong bias within the moderate biomass range (50–350 Mg/ha). However, in very high-biomass stands, the model tended to underestimate AGB, reflecting saturation effects that persist despite clear improvements over existing products. Estimated mean AGB values ranged from 180.52 to 214.09 Mg/ha, with total AGB varying between 13.05 and 14.10 Pg. Trend analysis using Sen’s slope and the Mann–Kendall test revealed significant AGB trends in 31.31% of forested areas, with 68.76% showing increases. This study offers a robust and scalable framework for continuous tropical forest carbon monitoring, providing critical support for carbon accounting, forest management, and policy-making. Full article

(This article belongs to the Special Issue Advances in Multi-Sensor Remote Sensing for Vegetation Monitoring)

18 pages, 9202 KB

Open AccessArticle

Fine-Scale Mapping and Uncertainty Quantification of Intertidal Sediment Grain Size Using Geostatistical Simulation Integrated with Machine Learning and High-Resolution Remote Sensing Imagery

by No-Wook Park and Dong-Ho Jang

Remote Sens. 2025, 17(18), 3230; https://doi.org/10.3390/rs17183230 - 18 Sep 2025

Abstract

This study presents a geostatistical simulation approach for fine-scale grain size mapping in tidal flats, which complements sparse field survey data with high-resolution optical satellite imagery and quantifies prediction uncertainty at unsampled locations. Within a multi-Gaussian regression kriging (MGRK) framework, a random forest [...] Read more.

This study presents a geostatistical simulation approach for fine-scale grain size mapping in tidal flats, which complements sparse field survey data with high-resolution optical satellite imagery and quantifies prediction uncertainty at unsampled locations. Within a multi-Gaussian regression kriging (MGRK) framework, a random forest (RF) regression model is used to estimate the trend component of grain size variability in Gaussian space. Residual components are estimated using kriging, and the trend and residual components are combined to construct conditional cumulative distribution functions for uncertainty modeling. Sequential Gaussian simulation based on the CCDFs generates alternative realizations of grain size, allowing for quantification of prediction uncertainty. The potential of this integrated approach was tested on the Baramarae tidal flat in Korea using KOMPSAT-2 imagery. Three spectral features, the green band, red band, and normalized difference water index (NDWI), explained 42.74% of the grain size variability, with NDWI identified as the most influential feature, contributing 40.8% compared with 31.7% for the red band and 27.5% for the green band. MGRK effectively captured local grain size variations, reducing the mean absolute error from 0.554 to 0.280 compared with univariate kriging based solely on field survey data, corresponding to an improvement of approximately 49.5%. The benefit of the proposed approach was validated by a reduction in prediction uncertainty, with the mean standard deviation decreasing from 0.743 in simulations based solely on field data to 0.280 in MGRK-based simulations. These findings indicate that the proposed geostatistical approach, integrating satellite-derived features, is a reliable method for fine-scale mapping of intertidal sediment grain size by providing both predictions and associated uncertainty estimates. Full article

(This article belongs to the Section Environmental Remote Sensing)

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17 pages, 2714 KB

Open AccessArticle

Examining the Characteristics of Drought Resistance Under Different Types of Extreme Drought in Inner Mongolia Grassland, China

by Jiaqi Han, Jian Guo, Xiuchun Yang, Weiguo Jiang, Wenwen Gao, Xiaoyu Xing, Dong Yang, Min Zhang and Bin Xu

Remote Sens. 2025, 17(18), 3229; https://doi.org/10.3390/rs17183229 - 18 Sep 2025

Abstract

Extreme drought events may become more frequent with climate change. Understanding the impact of extreme drought on grassland ecosystems is therefore crucial for the long-term sustainability of ecosystems. Here, we identified extreme drought events in the Inner Mongolia grasslands of China using long-term [...] Read more.

Extreme drought events may become more frequent with climate change. Understanding the impact of extreme drought on grassland ecosystems is therefore crucial for the long-term sustainability of ecosystems. Here, we identified extreme drought events in the Inner Mongolia grasslands of China using long-term standardized precipitation evapotranspiration index (SPEI) data and evaluated drought resistance of the vegetation under extreme drought based on net primary production (NPP). The impact of consecutive extreme drought events and multiple discontinuous one-year extreme drought events on grasslands were further analyzed to investigate the response strategies of different grassland types to different drought conditions. We found that the frequency and area of extreme drought in 2000–2011 were significantly higher than those in 2012–2020, and the Xilingol League region showed the highest frequency of extreme drought events. Under extreme drought, vegetation resistance was positively correlated, where annual precipitation > 300 mm. The mean resistance of different grassland types followed the order: upland meadow (UM) > lowland meadow (LM) > temperate meadow steppe (TMS) > temperate desert (TD) > temperate steppe (TS) > temperate steppe desert (TSD) > temperate desert steppe (TDS). In the analysis of two cases of consecutive two-year extreme drought, all grassland types except TSD and TD showed obvious decreased resistance in the final drought year, with the highest reduction (0.16) in LM during 2010–2011, implying the widespread and significant inhibition of grassland growth by continuous drought. However, under the multiple discontinuous extreme drought events, the resistance of all grassland types showed a fluctuating but an overall increasing trend, suggesting the adaptability of grassland to drought. The results emphasize that management departments should pay more attention to regions with low resistance and enhance the stability of grassland production by increasing the proportion of drought-resistant plants in reaction to future extreme drought scenarios. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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24 pages, 52572 KB

Open AccessArticle

Investigation of Bored Piles Under Deep and Extensive Plinth Foundations: Method of Prospecting and Mapping with Pulse Georadar

by Donato D’Antonio

Remote Sens. 2025, 17(18), 3228; https://doi.org/10.3390/rs17183228 (registering DOI) - 18 Sep 2025

Abstract

Ground-penetrating radar surveys on structures have a wide range of applications, and they are very useful in solving engineering problems: from detecting reinforcement, studying concrete characteristics, unfilled joints, analyzing brick elements, detecting water content in building bodies, and evaluating structural deformation. They generally [...] Read more.

Ground-penetrating radar surveys on structures have a wide range of applications, and they are very useful in solving engineering problems: from detecting reinforcement, studying concrete characteristics, unfilled joints, analyzing brick elements, detecting water content in building bodies, and evaluating structural deformation. They generally pursued small investigation areas with measurements made in direct contact with target structures and for small depths. Detecting deep piles presents specific challenges, and surveys conducted from the ground level may be unsuccessful. To reach great depths, medium-low frequencies must be used, but this choice results in lower resolution. Furthermore, the pile signals may be masked when they are located beneath massive reinforced foundations, which act as an electromagnetic shield. Finally, GPR equipment looks for differences in the dielectric of the material, and the signals recorded by the GPR will be very weak when the differences in the physical properties of the investigated media are modest. From these weak signals, it is difficult to identify information on the differences in the subsurface media. In this paper, we are illustrating an exploration on plinth foundations, supported by drilled piles, submerged in soil, extensive, deep and uninformed. Pulse GPR prospecting was performed in common-offset and single-fold, bistatic configuration, exploiting the exposed faces of an excavation around the foundation. In addition, three velocity tests were conducted, including two in common mid-point and one in zero-offset transillumination, in order to explore the range of variation in relative dielectric permittivity in the investigated media. Thanks to the innovative survey on the excavation faces, it is possible to perform profiles perpendicular to the strike direction of the interface. The electromagnetic backscattering analysis approach allowed us to extract the weighted average frequency attribute section. In it, anomalies emerge in the presence of drilled piles with four piles with an estimated diameter of 80 cm. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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34 pages, 12343 KB

Open AccessArticle

A Spatially Comprehensive Water Balance Model for Starch Potato from Combining Multispectral Ground Station and Remote Sensing Data in Precision Agriculture

by Thomas Piernicke, Matthias Kunz, Sibylle Itzerott, Jan Lukas Wenzel, Julia Pöhlitz and Christopher Conrad

Remote Sens. 2025, 17(18), 3227; https://doi.org/10.3390/rs17183227 - 18 Sep 2025

Abstract

The measurement of available water for agricultural plants is a crucial parameter for farmers, particularly to plan irrigation. However, an area-wide measurement is often not trivial as there are several inputs and outputs of water into the system. Here, we present a high-resolution, [...] Read more.

The measurement of available water for agricultural plants is a crucial parameter for farmers, particularly to plan irrigation. However, an area-wide measurement is often not trivial as there are several inputs and outputs of water into the system. Here, we present a high-resolution, remote sensing-based water balance model for starch potato cultivation, combining multispectral ground station data with UAV and satellite imagery. Over a three-year period (2021–2023), data from Arable Mark 2 ground stations, DJI Phantom 4 MS drones, PlanetScope satellites, and Sentinel-2 satellites were collected in Mecklenburg–Western Pomerania, Germany. The model utilizes NDVI-based crop coefficients (R² = 0.999) to estimate evapotranspiration and integrates on-farm irrigation and precipitation data for precise water balance calculations. A correlation with reference NDVI observations by Arable Mark 2 systems can be shown for UAV (R² = 0.94), PlanetScope satellite data (R² = 0.94), and Sentinel-2 satellite data (R² = 0.93). We demonstrate the model’s ability to capture intra-site heterogeneity on a precision farming scale. Our spatially comprehensive model enables farmers to optimize irrigation strategies, reducing water and energy use. Although the results are based on sprinkler irrigation, the model remains adaptable for advanced irrigation methods such as drip and subsurface systems. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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33 pages, 17725 KB

Open AccessArticle

Bias Correction of SMAP L2 Sea Surface Salinity Based on Physics-Informed Neural Network

by Minghui Wu, Zhenyu Liang, Senliang Bao, Huizan Wang, Yulin Liu, Ziyang Zhang and Qitian Xuan

Remote Sens. 2025, 17(18), 3226; https://doi.org/10.3390/rs17183226 - 18 Sep 2025

Abstract

Sea surface salinity (SSS) observations play a crucial role in the study of ocean circulation, climate variability, and marine ecosystems. However, current satellite SSS products suffer from systematic biases due to factors such as radio frequency interference (RFI) and land contamination, resulting in [...] Read more.

Sea surface salinity (SSS) observations play a crucial role in the study of ocean circulation, climate variability, and marine ecosystems. However, current satellite SSS products suffer from systematic biases due to factors such as radio frequency interference (RFI) and land contamination, resulting in fundamental limitations to their application for SSS monitoring. To address this issue, we propose a physics-informed neural network (PINN) approach that directly integrates radiative transfer physical processes into the neural network architecture for SMAP L2 SSS bias correction. This method ensures oceanographically consistent corrections by embedding physical constraints into the forward propagation model. The results demonstrate that PINN achieved a root mean square error (RMSE) of 0.249 PSU, representing a 5.3% to 8.5% relative performance improvement compared to conventional methods—GBRT, ANN, and XGBoost. Further temporal stability analysis reveals that PINN exhibits significantly reduced RMSE variations over multi-year periods, demonstrating exceptional long-term correction stability. Meanwhile, this method achieves more uniform bias improvement in contaminated nearshore regions, showing distinct advantages over the inconsistent correction patterns of conventional methods. This study establishes a physics-constrained machine learning framework for satellite SSS data correction by integrating oceanographic domain knowledge, providing a novel technical pathway for reliable enhancement of Earth observation data. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography (2nd Edition))

27 pages, 15513 KB

Open AccessArticle

Detection of Small-Scale Potential Landslides in Vegetation-Covered Areas of the Hengduan Mountains Using LT-1 Imagery: A Case Study of the Luding Seismic Zone

by Hang Jiang, Xianhua Yang, Hui Wen, Xiaogang Wang, Chuanyang Lei and Rui Zhang

Remote Sens. 2025, 17(18), 3225; https://doi.org/10.3390/rs17183225 - 18 Sep 2025

Abstract

The rugged terrain and dense vegetation in the mountainous area of Luding after the strong earthquake have made geologic hazards hidden and difficult to verify, and there are limitations in the fine-resolution monitoring of small-scale landslides, especially in the area covered by high [...] Read more.

The rugged terrain and dense vegetation in the mountainous area of Luding after the strong earthquake have made geologic hazards hidden and difficult to verify, and there are limitations in the fine-resolution monitoring of small-scale landslides, especially in the area covered by high vegetation. Currently, there is a lack of research on the application of L-band LuTan-1 (LT-1) for landslide detection in the dense vegetation-covered area of the Luding strong earthquake zone, and it is necessary to carry out the analysis of the detection capability of LT-1 for small-scale landslide hazards under the complex terrain and dense vegetation area. In this study, the Stacking-InSAR method was employed using LT-1 and Sentinel-1 satellites to conduct deformation monitoring and landslide detection in the Luding seismic area and to investigate the small-scale landslide detection capability of LT-1 in vegetation-covered areas. The results show that LT-1 and Sentinel-1 identified 23 landslide hazards, and their obvious deformation and landslide characteristics indicate that they are still in an unstable state with a continuous deformation trend. At the same time, through the detection analysis of LT-1’s landslide detection capability under high vegetation cover and small-scale landslide detection capability, the results show that the long wavelength LT-1 can be more effective in landslide hazard identification and monitoring than the short wavelength, and LT-1 with high spatial resolution can be more refined to depict the landslide deformation characteristics in space, which demonstrates the great potential of LT-1 in the refinement of landslide detection. It shows the significant potential of the LT-1 satellite data in landslide detection. Finally, the effects of geometric distortion on landslide detection under different satellite orbits are analyzed, and it is necessary to adopt the combined monitoring method of elevating and lowering orbits for landslide detection to ensure the integrity and reliability of landslide detection. This study highlights the capability of the LT-1 satellite in monitoring landslides in complex mountainous terrain and underscores its potential for detecting small-scale landslides. The findings also offer valuable insights for future research on landslide detection using LT-1 data in similar challenging environments. Full article

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20 pages, 11133 KB

Open AccessArticle

Geospatial Analysis of the Roman Site of Munigua Based on RGB Airborne Imagery

by Emilio Ramírez-Juidias and Daniel Antón

Remote Sens. 2025, 17(18), 3224; https://doi.org/10.3390/rs17183224 - 18 Sep 2025

Abstract

This study investigates the use of high-resolution RGB aerial imagery from Spain’s National Aerial Orthophotography Plan (PNOA) for archeological feature detection through spectral index analysis and unsupervised clustering. Focusing on the Roman site of Munigua, eight orthophotographs acquired between 2014 and 2024 were [...] Read more.

This study investigates the use of high-resolution RGB aerial imagery from Spain’s National Aerial Orthophotography Plan (PNOA) for archeological feature detection through spectral index analysis and unsupervised clustering. Focusing on the Roman site of Munigua, eight orthophotographs acquired between 2014 and 2024 were analyzed to compute five RGB-based spectral indices: VARI, GLI, ExG, CSI, and BI. These indices were used to detect surface spectral anomalies potentially linked to buried archeological structures. A multi-temporal approach was employed, with Principal Component Analysis (PCA) and K-Means clustering applied independently to each image. This allowed for the identification of temporally persistent anomalies (areas that remained within the same spectral cluster across multiple years), suggesting the presence of underlying anthropogenic features. Despite the lack of near-infrared data, the combination of RGB-based indices and temporal clustering proved effective for non-invasive prospection. The methodology is scalable, repeatable, and relies entirely on open-access datasets, making it suitable for broader applications in heritage monitoring and landscape archeology. The results underscore the potential of RGB imagery and time-series clustering in detecting subtle archeological signals within complex vegetated environments. Full article

(This article belongs to the Special Issue Remote Sensing and Geophysical Tools for Land and Water System Analysis)

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24 pages, 9488 KB

Open AccessArticle

Quantifying the Relationship Between the FPAR and Vegetation Index in Marsh Wetlands Using a 3D Radiative Transfer Model and Satellite Observations

by Anhao Zhong, Xiangyuan Duan, Wenping Jin and Meng Zhang

Remote Sens. 2025, 17(18), 3223; https://doi.org/10.3390/rs17183223 - 18 Sep 2025

Abstract

Wetland ecosystems, particularly marsh wetlands, are vital for carbon cycling, yet the accurate estimation of the fraction of absorbed photosynthetically active radiation (FPAR) in these environments is challenging due to their complex structure and limited field data. This study employs the large-scale remote [...] Read more.

Wetland ecosystems, particularly marsh wetlands, are vital for carbon cycling, yet the accurate estimation of the fraction of absorbed photosynthetically active radiation (FPAR) in these environments is challenging due to their complex structure and limited field data. This study employs the large-scale remote sensing data and image simulation framework (LESS), a 3D radiative transfer model, to simulate FPAR and vegetation indices (VIs) under controlled conditions, including variations in vegetation types, soil types, chlorophyll content, solar and observation angles, and plant density. By simulating 8064 wetland scenes, we overcame the limitations of field measurements and conducted comprehensive quantitative analyses of the relationship between the FPAR and VI (which is essential for remote sensing-based FPAR estimation). Nine VIs (NDVI, GNDVI, SAVI, RVI, EVI, MTCI, DVI, kNDVI, RDVI) effectively characterized FPAR, with the following saturation thresholds quantified: inflection points (FPAR.inf, where saturation begins) ranged from 0.423 to 0.762 (mean = 0.594) and critical saturation points (FPAR.sat, where saturation is complete) from 0.654 to 0.889 (mean = 0.817). The Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI) showed the highest robustness against saturation and environmental variability for FPAR estimation in reed (Phragmites australis) marshes. These findings provide essential support for FPAR estimation in marsh wetlands and contribute to quantitative studies of wetland carbon cycling. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring and Assessment of Forest, Grassland, Wetland and Urban Ecosystem)

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27 pages, 14009 KB

Open AccessArticle

Stacking-Based Solar-Induced Chlorophyll Fluorescence Downscaling for Soil EC Estimation

by Kuangda Cui, Jianli Ding, Jinjie Wang, Jiao Tan and Jiangtao Li

Remote Sens. 2025, 17(18), 3222; https://doi.org/10.3390/rs17183222 - 18 Sep 2025

Abstract

The Xinjiang Province of China, characterized as a typical arid to semi-arid region, is increasingly facing severe issues related to soil salinization. Timely and accurate estimation of soil salinization in this region is crucial for the sustainable development of agriculture and food security. [...] Read more.

The Xinjiang Province of China, characterized as a typical arid to semi-arid region, is increasingly facing severe issues related to soil salinization. Timely and accurate estimation of soil salinization in this region is crucial for the sustainable development of agriculture and food security. However, current methods for detecting soil salinization primarily rely on various environmental covariates, which assess the extent of soil salinization by analyzing the relationship between environmental factors and the accumulation of soil salts. Nonetheless, these conventional environmental covariates often suffer from response delays, making it challenging to promptly reflect the dynamic changes in soil salinity. Solar-induced chlorophyll fluorescence (SIF) has been widely used to assess vegetation photosynthetic efficiency and is considered a direct indicator of plant photosynthetic activity. In contrast, SIF provides a timely means of monitoring the status of plant photosynthesis, indirectly reflecting the impact of soil salinization on plant growth. However, the spatial resolution of SIF products derived from satellites is typically low, which significantly limits the accurate estimation of soil salinization in Xinjiang. This study proposes a novel method for monitoring soil salinization, based on SIF data. The approach employs a Stacking ensemble learning model to downscale SIF data, thereby improving the spatial resolution of soil salinity monitoring. Using the GOSIF dataset, combined with environmental covariates, such as MODIS, the Stacking framework facilitates the fine-scale downscaling of SIF data, generating high-resolution SIF products, ranging from 0.05° to 0.005°, with a spatial resolution of 30 m. This refined SIF data is then used to predict soil electrical conductivity (EC). The experimental results demonstrate that: (1) the proposed Stacking-based SIF downscaling method is highly effective, with a high degree of fit to reference SIF data (R² > 0.85); (2) the high-resolution SIF data, after downscaling, more accurately reflects the spatial heterogeneity of soil salinization, especially in shallow soils (r < −0.6); and (3) models combining SIF and environmental covariates exhibit superior accuracy compared to models that rely solely on SIF or traditional environmental covariates (R² > 0.65). This research provides new data support and methodological advancements for precision agriculture and ecological environmental monitoring. Full article

(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Ecosystem Differences and Multi-Scale Comparisons)

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2 pages, 129 KB

Open AccessCorrection

Correction: Yu et al. Mean Inflection Point Distance: Artificial Intelligence Mapping Accuracy Evaluation Index—An Experimental Case Study of Building Extraction. Remote Sens. 2023, 15, 1848

by Ding Yu, Aihua Li, Yinping Long, Yan Xu, Jinrui Li and Xiongwu Xiao

Remote Sens. 2025, 17(18), 3221; https://doi.org/10.3390/rs17183221 - 18 Sep 2025

Abstract

The authors would like to add Xiongwu Xiao to the authorship list, as he was not included in the original publication [...] Full article

26 pages, 1658 KB

Open AccessArticle

LEO Augmentation Effect on BDS Precise Positioning in High-Latitude Maritime Regions

by Yangyang Liu, Ju Hong, Rui Tu, Shengli Wang, Fangxin Li, Yulong Ge and Ke Su

Remote Sens. 2025, 17(18), 3220; https://doi.org/10.3390/rs17183220 - 18 Sep 2025

Abstract

The economic and strategic value of high-latitude maritime regions is increasingly significant, yet traditional Global Navigation Satellite Systems remain constrained by unfavorable geometric configurations and slow convergence speeds at high latitudes, failing to meet the growing demand for real-time centimeter-level high-precision positioning in [...] Read more.

The economic and strategic value of high-latitude maritime regions is increasingly significant, yet traditional Global Navigation Satellite Systems remain constrained by unfavorable geometric configurations and slow convergence speeds at high latitudes, failing to meet the growing demand for real-time centimeter-level high-precision positioning in these areas. Benefiting from their rapid motion and superior coverage over high-latitude zones, Low Earth Orbit (LEO) satellites offer an effective means to enhance positioning performance in such regions. This paper uses the real BDS data collected by an unmanned surface vessel in the high-latitude waters of the Southern Hemisphere, jointly simulates polar and medium-inclination LEO constellations, and systematically assess the enhancement effects of LEO augmentation on Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) techniques. The results demonstrate that the polar-orbiting constellation markedly improves the observation environment, increasing the number of visible satellites by 70.2% and reducing the Position Dilution of Precision from 2.4 to 1.7, whereas the medium-inclination orbit constellation offered negligible improvement due to insufficient visibility. The rapid geometric change brought by LEO constellations is the core key to achieving fast convergence. Incorporating LEO observations drastically shortened the BDS PPP convergence time from 45.3 min to under 1 min, achieving a reduction of over 97%. Simultaneously, it improved the three-dimensional Root Mean Square accuracy by 54.7%, from 0.086 m to 0.039 m. Convergence within one minute was consistently achieved when at least 5.4 LEO satellites were included in the solution. Moreover, the addition of LEO signals increased the fixed solution rate of short-baseline RTK from 96.5% to 100%, while improving horizontal and vertical accuracy by 31.5% and 12.3%, respectively. This study confirms that LEO constellations, especially those in polar orbits, can substantially enhance BDS precise positioning performance in high-latitude maritime environments, thereby providing critical technical support for related navigation applications. Full article

(This article belongs to the Special Issue LEO-Augmented PNT Service)

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27 pages, 8010 KB

Open AccessArticle

Deep Learning-Based Short- and Mid-Term Surface and Subsurface Soil Moisture Projections from Remote Sensing and Digital Soil Maps

by Saman Rabiei, Ebrahim Babaeian and Sabine Grunwald

Remote Sens. 2025, 17(18), 3219; https://doi.org/10.3390/rs17183219 - 18 Sep 2025

Abstract

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and [...] Read more.

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm³ cm⁻³ with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring. Full article

(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)

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28 pages, 9916 KB

Open AccessArticle

Understanding Surface Water Dynamics in Post-Mining Area Through Multi-Source Remote Sensing and Spatial Regression Analysis

by Anna Buczyńska, Dariusz Głąbicki, Anna Kopeć and Paulina Modlińska

Remote Sens. 2025, 17(18), 3218; https://doi.org/10.3390/rs17183218 - 17 Sep 2025

Abstract

Despite successful land reclamation efforts, post-mining areas are still prone to secondary effects of mineral extraction. These effects include surface deformations, damage to infrastructure and buildings, and periodic or permanent changes to surface water resources. This study focused on analyzing a former copper [...] Read more.

Despite successful land reclamation efforts, post-mining areas are still prone to secondary effects of mineral extraction. These effects include surface deformations, damage to infrastructure and buildings, and periodic or permanent changes to surface water resources. This study focused on analyzing a former copper mine in southwest Poland in terms of surface water changes, which may be caused by the restoration of groundwater conditions in the region after mine closure. The main objective of the study was to detect areas with statistically significant changes in surface water between 2015 and 2024, as well as to identify the main factors influencing the observed changes. The methodology integrated open remote sensing datasets from Landsat and Sentinel-1 missions for deriving spectral indices—Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Moisture Index (NDMI), as well as Surface Soil Moisture index (SSM); spatial statistics methods, including Emerging Hot Spot analysis; and regression models—Random Forest Regression (RFR) and Geographically Weighted Regression (GWR). The results obtained indicated a general increase in vegetation water content, a reduction in the extent of surface water, and minor soil moisture changes during the analyzed period. The Emerging Hot Spot analysis revealed a number of new hot spots, indicating regions with statistically significant increases in surface water content in the study area. Out of the investigated regression models, global regression (RFR) outperformed local (GWR) models, with R2 ranging between 74.7% and 87.3% for the studied dependent variables. The most important factors in terms of influence were the distance from groundwater wells, surface topography, vegetation conditions and distance from active mining areas, while surface geology conditions and permeability had the least importance in the regression models. Overall, this study offers a comprehensive framework for integrating multi-source data to support the analysis of environmental changes in post-mining regions. Full article

(This article belongs to the Special Issue New Advances in Remote Sensing Techniques Applied in Surface and Underground Mine Operations)

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21 pages, 1930 KB

Open AccessArticle

Improved Multi-View Graph Clustering with Global Graph Refinement

by Lingbin Zeng, Shixin Yao, You Huang, Yong Cheng and Yue Qian

Remote Sens. 2025, 17(18), 3217; https://doi.org/10.3390/rs17183217 - 17 Sep 2025

Abstract

The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view remote sensing data increase structural heterogeneity while simultaneously hindering effective [...] Read more.

The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view remote sensing data increase structural heterogeneity while simultaneously hindering effective information extraction and fusion. Existing MVGC methods cannot selectively integrate and fully refine both graph structure and node attribute information for consensus representation learning. Furthermore, current methods tend to overlook distant nodes, thus failing to capture the global graph structure. To solve these issues, we propose a novel method called Improved Multi-View Graph Clustering with Global Graph Refinement (IMGCGGR). Specifically, we first design a view-specific fusion network (VSFN) to extract and integrate node attribute and structural information into view-specific representation for each view. VSFN not only utilizes a global self-attention mechanism to enhance the global properties of structural information but also constructs a clustering loss through a self-supervised strategy to guide the view-specific clustering distribution assignment. Moreover, to enhance the capability of view-specific representation, a learnable attention-driven aggregation strategy is introduced to flexibly fuse the attribute and structural feature. Then, we adopt a cross-view fusion module to adaptively merge multiple view-specific representations for generating the final consensus representation. Comprehensive experiments show that IMGCGGR achieves significant clustering performance improvements over baseline methods across various benchmark datasets. Full article

(This article belongs to the Topic Geographic Information and Remote Sensing Technology (GIRST))

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