Remote Sensing

17 pages, 23770 KB

Open AccessArticle

Air–Sea Interaction During Ocean Frontal Passage: A Case Study from the Northern South China Sea

by Ruichen Zhu, Jingjie Yu, Xingzhi Zhang, Haiyuan Yang and Xin Ma

Remote Sens. 2025, 17(17), 3024; https://doi.org/10.3390/rs17173024 (registering DOI) - 1 Sep 2025

The northern South China Sea has abundant frontal systems near coastal and island regions, which play crucial roles in regional ocean dynamics and ecosystem. While previous studies have established preliminary understanding of their spatial distribution, seasonal variability, and dynamic characteristics, the atmospheric response [...] Read more.

The northern South China Sea has abundant frontal systems near coastal and island regions, which play crucial roles in regional ocean dynamics and ecosystem. While previous studies have established preliminary understanding of their spatial distribution, seasonal variability, and dynamic characteristics, the atmospheric response to these frontal systems remains poorly understood. This study integrates observations from a moored buoy deployed on the continental shelf of the South China Sea with satellite remote sensing data to analyze oceanic and atmospheric variations during frontal passage. The results reveal that the ocean front can not only induce pronounced oceanic changes characterized by significant cooling, saltiness, and surface current acceleration, but also exert substantial influence on the overlying atmosphere, with consistent decreasing trends in air temperature, humidity, and atmospheric pressure, all of which rapidly recovered following frontal retreat. Notably, when the front directly traversed the buoy location, diurnal temperature cycles were markedly suppressed, while turbulent heat flux and downfront wind-stress curl reached peak magnitudes. These findings demonstrate that ocean fronts and associated sea surface temperature gradients can trigger intense air–sea exchange processes at the ocean–atmosphere interface. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring)

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17 pages, 4114 KB

Open AccessArticle

Non-Precipitation Echo Identification in X-Band Dual-Polarization Weather Radar

by Zihang Zhao, Hao Wen, Lei Wu, Ruiyi Li, Ting Zhuang and Yang Zhang

Remote Sens. 2025, 17(17), 3023; https://doi.org/10.3390/rs17173023 (registering DOI) - 31 Aug 2025

Abstract

This study proposes a novel quality control method combining fuzzy logic and threshold discrimination for processing X-band dual-polarization radar data from Beijing. The method effectively eliminates non-precipitation echoes, including electromagnetic interference, clear-air echoes, and ground clutter through five key steps: (1) Identifying electromagnetic [...] Read more.

This study proposes a novel quality control method combining fuzzy logic and threshold discrimination for processing X-band dual-polarization radar data from Beijing. The method effectively eliminates non-precipitation echoes, including electromagnetic interference, clear-air echoes, and ground clutter through five key steps: (1) Identifying electromagnetic interference using continuity of reflectivity across adjacent elevation angles, radial mean correlation coefficient, and differential reflectivity; (2) Preserving precipitation data in ground clutter-mixed regions by jointly utilizing the difference in reflectivity before and after clutter suppression by the signal processor, and characteristic value proportions; (3) Developing a fuzzy logic algorithm with six parameters (e.g., reflectivity texture, depolarization ratio) for ground clutter and clear-air echoes removal; (4) Filtering echoes with missing dual-polarization variables using cross-elevation mean reflectivity, mean correlation coefficient, and valid range bin proportion; (5) Removing residual noise via radial/azimuthal reflectivity continuity analysis. Validation with 635 PPI scans demonstrates high identification accuracy across echo types: 93.5% for electromagnetic interference, 98.4% for ground clutter, 97.7% for clear-air echoes, and 98.2% for precipitation echoes. Full article

27 pages, 11413 KB

Open AccessArticle

HyperVTCN: A Deep Learning Method with Temporal and Feature Modeling Capabilities for Crop Classification with Multisource Satellite Imagery

by Xiaoqi Huang, Minzi Fang, Weilang Kong, Jialin Liu, Yuxin Wu, Zhenjie Liu, Zhi Qiao and Luo Liu

Remote Sens. 2025, 17(17), 3022; https://doi.org/10.3390/rs17173022 (registering DOI) - 31 Aug 2025

Abstract

Crop distribution represents crucial information in agriculture, playing a key role in ensuring food security and promoting sustainable agricultural development. However, existing methods for crop distribution primarily focus on modeling temporal dependencies while overlooking the interactions and dependencies among different remote sensing features, [...] Read more.

Crop distribution represents crucial information in agriculture, playing a key role in ensuring food security and promoting sustainable agricultural development. However, existing methods for crop distribution primarily focus on modeling temporal dependencies while overlooking the interactions and dependencies among different remote sensing features, thus failing to fully exploit the rich information contained in multisource satellite imagery. To address this issue, we propose a deep learning-based method named HyperVTCN, which comprises two key components: the ModernTCN block and the TiVDA attention mechanism. HyperVTCN effectively captures temporal dependencies and uncovers intrinsic correlations among features, thereby enabling more comprehensive data utilization. Compared to other state-of-the-art models, it shows improved performance, with overall accuracy (OA) improving by approximately 2–3%, Kappa improving by 3–4.5%, and Macro-F1 improving by about 2–3%. Additionally, ablation experiments suggest that both the attention mechanism(Time-Feature Dual Attention, TiVDA) and the targeted loss optimization strategy contribute to performance improvements. Finally, experiments were conducted to investigate HyperVTCN’s cross-feature and cross-temporal modeling. The results indicate that this joint modeling strategy is effective. This approach has shown potential in enhancing model performance and offers a viable solution for crop classification tasks. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing (2nd Edition))

23 pages, 26514 KB

Open AccessArticle

LEO Navigation Augmentation Signal-Based Passive Radar: System Model and Performance Analysis

by Mingxu Zhang, Bin Sun and Qilei Zhang

Remote Sens. 2025, 17(17), 3021; https://doi.org/10.3390/rs17173021 (registering DOI) - 31 Aug 2025

Abstract

As the next generation of time–space infrastructure, low-earth-orbit navigation augmentation (LEO-NA) technology has become a hot research topic, since it can overcome the vulnerabilities and limitations of global navigation satellite systems (GNSSs). Meanwhile, a LEO-NA signal can serve as a better cooperative illuminator [...] Read more.

As the next generation of time–space infrastructure, low-earth-orbit navigation augmentation (LEO-NA) technology has become a hot research topic, since it can overcome the vulnerabilities and limitations of global navigation satellite systems (GNSSs). Meanwhile, a LEO-NA signal can serve as a better cooperative illuminator to build more powerful passive radar (PR). This paper proposes and investigates a new and promising PR system, LEO-NA signal-based PR (LNAS-PR), which utilizes LEO-NA signals as the illuminator and utilizes an unmanned aerial vehicle (UAV) to carry the receiver. Taking advantage of higher landing power and global coverage, LNAS-PR can be used to detect maritime targets with benefits of low cost and high efficiency. However, new technical challenges of information capture and processing need to be dealt with. Therefore, this paper presents the system model, signal model, and performance analyses within a maritime monitoring scenario, providing a foundation for future in-depth research. Full article

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20 pages, 5971 KB

Open AccessArticle

A Novel UAV- and AI-Based Remote Sensing Approach for Quantitative Monitoring of Jellyfish Populations: A Case Study of Acromitus flagellatus in Qinglan Port

by Fang Zhang, Shuo Wang, Yanhao Qiu, Nan Wang, Song Sun and Hongsheng Bi

Remote Sens. 2025, 17(17), 3020; https://doi.org/10.3390/rs17173020 (registering DOI) - 31 Aug 2025

Abstract

The frequency of jellyfish blooms in marine ecosystems has been rising globally, attracting significant attention from the scientific community and the general public. Low-altitude remote sensing with Unmanned Aerial Vehicles (UAVs) offers a promising approach for rapid, large-scale, and automated image acquisition, making [...] Read more.

The frequency of jellyfish blooms in marine ecosystems has been rising globally, attracting significant attention from the scientific community and the general public. Low-altitude remote sensing with Unmanned Aerial Vehicles (UAVs) offers a promising approach for rapid, large-scale, and automated image acquisition, making it an effective tool for jellyfish population monitoring. This study employed UAVs for extensive sea surface surveys, achieving quantitative monitoring of the spatial distribution of jellyfish and optimizing flight altitude through gradient experiments. We developed a “bell diameter measurement model” for estimating jellyfish bell diameters from aerial images and used the Mask R-CNN algorithm to identify and count jellyfish automatically. This method was tested in Qinglan Port, where we monitored Acromitus flagellatus populations from mid-April to mid-May 2021 and late May 2023. Our results show that the UAVs can monitor jellyfish with bell diameters of 5 cm or more, and the optimal flight height is 100–150 m. The bell diameter measurement model, defined as L = 0.0103 × H × N + 0.1409, showed no significant deviation from field measurements. Compared to visual identification by human experts, the automated method achieved high accuracy while reducing labor and time costs. Case analysis revealed that the abundance of A. flagellatus in Qinglan Port initially increased and then decreased from mid-April to mid-May 2021, displaying a distinct patchy distribution. During this period, the average bell diameter gradually increased from 15.0 ± 3.4 cm to 15.5 ± 4.3 cm, with observed sizes ranging from 8.2 to 24.5 cm. This study introduces a novel, efficient, and cost-effective UAV-based method for quantitative monitoring of large jellyfish populations in surface waters, with broad applicability. Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition))

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21 pages, 11808 KB

Open AccessArticle

A Slope Adaptive Bathymetric Method by Integrating ICESat-2 ATL03 Data with Sentinel-2 Images

by Jizhe Li, Sensen Chu, Qixin Hu, Ziyang Qu, Jinghao Zhang and Liang Cheng

Remote Sens. 2025, 17(17), 3019; https://doi.org/10.3390/rs17173019 (registering DOI) - 31 Aug 2025

Abstract

The detection of seafloor signal photons in various topographies is challenging. Previous research has divided photons into clusters based solely on their density, which is closely related to the settings of the empirical parameters. Inappropriate parameters may mistakenly identify the water column noise [...] Read more.

The detection of seafloor signal photons in various topographies is challenging. Previous research has divided photons into clusters based solely on their density, which is closely related to the settings of the empirical parameters. Inappropriate parameters may mistakenly identify the water column noise photons as seafloor photons. To overcome these limitations, this study introduces a novel slope iterative adaptive filter (SIAF) method that innovatively integrates ICESat-2 ATL03 photon data with Sentinel-2-derived topographic slopes. Inspired by satellite-derived bathymetry, we extracted topographic slopes from multispectral images as auxiliary information to guide the photon extraction. The initial slope estimation was derived from the multispectral images, and the optimal slope direction was determined iteratively, using the detected signal photons in each step. The average and maximum overall accuracies of SIAF were 93.43% and 95.7%, respectively. The validation of the extraction results with sonar data indicated that the SIAF achieved an average root mean square error (RMSE) of 0.49 m. Crucially, the SIAF resolves critical shortcomings of prior techniques: (1) it avoids the isotropic assumption of density-based methods, (2) it mitigates AVEBM’s vulnerability to noise in steep-slope regions, and (3) it enables robust automation without manual parameter tuning. Consequently, SIAF proved to be an efficient approach for the automatic mapping of water depths in shallow-water zones. Full article

(This article belongs to the Special Issue Space-Geodetic Techniques (Third Edition))

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20 pages, 9752 KB

Open AccessArticle

Satellite Remote Sensing Reveals Global Dam Impacts on Riparian Vegetation Dynamics Under Future Climate Scenarios

by Yunlong Liu, Mengxi He, Zhucheng Zhang, Tong Sun, Yanyi Li and Li He

Remote Sens. 2025, 17(17), 3018; https://doi.org/10.3390/rs17173018 (registering DOI) - 30 Aug 2025

Abstract

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into [...] Read more.

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into how dams influence RV dynamics worldwide. Here, we integrated satellite-derived environmental indicators, including Normalized Difference Vegetation Index (NDVI), to quantify and compare riparian vegetation trends upstream and downstream of dams globally. By applying paired linear regression analyses to pre- and post-construction NDVI time series, we identified dams associated with significant RV degradation following impoundment. Furthermore, we employed Gradient Boosting Regression Models (GBRM), calibrated using current observational data and driven by CMIP6 climate projections, to forecast global riparian vegetation trends through the year 2100 under various climate scenarios. Our analysis reveals that, although widespread vegetation degradation was not evident up to 2017—and many regions showed slight improvements—future projections under higher-emission pathways (SSP3-7.0 and SSP5-8.5) indicate substantial RV declines after 2040, particularly in high-latitude forests, grasslands, and arid regions. Conversely, tropical and subtropical riparian forests are predicted to maintain stable or increasing NDVI under moderate emission scenarios (SSP1-2.6). These results highlight the potential for adaptive dam development strategies supported by continued satellite-based monitoring to help reduce climate-related risks to riparian vegetation in regions. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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28 pages, 16915 KB

Open AccessArticle

The Analysis of Spatial and Temporal Changes in Ecological Quality and Its Drivers in the Baiyangdian Watershed

by Haoyang Wang, Chunyi Li, Meng Li, Yangying Zhan, Kexin Liu and Junxuan Li

Remote Sens. 2025, 17(17), 3017; https://doi.org/10.3390/rs17173017 (registering DOI) - 30 Aug 2025

Abstract

As a critical ecological security node in North China, the Baiyangdian Basin underpins regional water resources, biodiversity conservation, and environmental risk mitigation. Its ecological integrity is fundamental to the sustainable development of the Beijing–Tianjin–Hebei (BTH) megaregion. This study leveraged Google Earth Engine (GEE) [...] Read more.

As a critical ecological security node in North China, the Baiyangdian Basin underpins regional water resources, biodiversity conservation, and environmental risk mitigation. Its ecological integrity is fundamental to the sustainable development of the Beijing–Tianjin–Hebei (BTH) megaregion. This study leveraged Google Earth Engine (GEE) to quantify spatiotemporal ecosystem dynamics within the Baiyangdian watershed from 1990 to 2023, utilizing the Remote Sensing Ecological Index (RSEI). The primary drivers influencing the watershed’s ecological and environmental quality were subsequently analyzed. The results show that the ecological quality of the Baiyangdian Basin showed fluctuating changes from 1990 to 2023. Overall, the northwestern part of the Baiyangdian Basin improved significantly, while the southeastern part was slightly degraded, and the intensity of the change between different RSEI grades was low, mainly fluctuating between poor, medium, and good grades. Both anthropogenic and natural factors have high explanatory power for the ecological quality of the Baiyangdian watershed, and the land use type in particular is the main driver of changes in the RSEI area. The explanatory power of these factors was significantly enhanced by the interaction between them, especially the interaction between the land use type and other drivers. Within the drivers of the land use type, the cropland area, woodland area, shrub area, and grassland area have a significant influence. In summary, the area change in different land use types is the main factor influencing the ecological quality of the Baiyangdian watershed. This study has demonstrative value and implications for large-scale shallow lakes and wetlands, ecological barriers in rapidly urbanizing regions, the integrated management of cross-administrative watersheds, and the use of the GEE platform for long time-series and large-scale ecological monitoring and assessment. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Land-Use/Land-Cover Change and Impacts on Ecosystem Service)

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20 pages, 460 KB

Open AccessArticle

Towards Comprehensive Characterization of GaoFen-3: Polarimetric Radar Performance and Data Quality Assessment

by Weibin Liang, Lihong Kang and Shijie Ren

Remote Sens. 2025, 17(17), 3016; https://doi.org/10.3390/rs17173016 (registering DOI) - 30 Aug 2025

Abstract

Although synthetic aperture radar (SAR) performance and polarimetric data quality are closely related, they represent fundamentally different concepts. This paper delineates their distinctions, investigates their interdependence, and introduces a comprehensive set of technical metrics for evaluating radar system performance and assessing polarimetric data [...] Read more.

Although synthetic aperture radar (SAR) performance and polarimetric data quality are closely related, they represent fundamentally different concepts. This paper delineates their distinctions, investigates their interdependence, and introduces a comprehensive set of technical metrics for evaluating radar system performance and assessing polarimetric data quality. Specifically, radar performance is quantified by seven independent parameters, whereas data quality is characterized by a three-component channel imbalance vector and a twelve-element channel crosstalk matrix. The paper details the measurement methods for these parameters and outlines the associated technical requirements, including calibrator specifications and test-site conditions. To improve operational applicability, an approximate method for data quality assessment is proposed, and its associated errors are analyzed. Special attention is given to the

γ

factor, which is highlighted as a critical and irreplaceable indicator of radar performance. Using field data from the GaoFen-3 (GF-3) satellite, the proposed metrics are applied to evaluate both radar performance and data quality. The results provide insights into the polarimetric characteristics of the system and offer practical guidance for the calibration and application of GF-3 polarimetric SAR data. Full article

(This article belongs to the Special Issue Cutting-Edge PolSAR Imaging Applications and Techniques)

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24 pages, 4385 KB

Open AccessArticle

HTMNet: Hybrid Transformer–Mamba Network for Hyperspectral Target Detection

by Xiaosong Zheng, Yin Kuang, Yu Huo, Wenbo Zhu, Min Zhang and Hai Wang

Remote Sens. 2025, 17(17), 3015; https://doi.org/10.3390/rs17173015 (registering DOI) - 30 Aug 2025

Abstract

Hyperspectral target detection (HTD) aims to identify pixel-level targets within complex backgrounds, but existing HTD methods often fail to fully exploit multi-scale features and integrate global–local information, leading to suboptimal detection performance. To address these challenges, a novel hybrid Transformer–Mamba network (HTMNet) is [...] Read more.

Hyperspectral target detection (HTD) aims to identify pixel-level targets within complex backgrounds, but existing HTD methods often fail to fully exploit multi-scale features and integrate global–local information, leading to suboptimal detection performance. To address these challenges, a novel hybrid Transformer–Mamba network (HTMNet) is proposed to reconstruct the high-fidelity background samples for HTD. HTMNet consists of the following two parallel modules: the multi-scale feature extraction (MSFE) module and the global–local feature extraction (GLFE) module. Specifically, in the MSFE module, we designed a multi-scale Transformer to extract and fuse multi-scale background features. In the GLFE module, a global feature extraction (GFE) module is devised to extract global background features by introducing a spectral–spatial attention module in the Transformer. Meanwhile, a local feature extraction (LFE) module is developed to capture local background features by incorporating the designed circular scanning strategy into the LocalMamba. Additionally, a feature interaction fusion (FIF) module is devised to integrate features from multiple perspectives, enhancing the model’s overall representation capability. Experiments show that our method achieves AUC(P_F, P_D) scores of 99.97%, 99.91%, 99.82%, and 99.64% on four public hyperspectral datasets. These results demonstrate that HTMNet consistently surpasses state-of-the-art HTD methods, delivering superior detection performance in terms of AUC(P_F, P_D). Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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19 pages, 2785 KB

Open AccessArticle

A Study of Global Hourly Sea Surface Temperature Fusion Based on the Triple-Collocation Fusion Algorithm

by Lan Zhao and Haiyong Ding

Remote Sens. 2025, 17(17), 3014; https://doi.org/10.3390/rs17173014 - 29 Aug 2025

Abstract

Sea surface temperature (SST) is vital for climate monitoring and extreme weather forecasting. Existing global SST datasets are typically provided at daily to seasonal resolutions, while hourly data remain limited to regional scales. Polar-orbiting satellites offer global coverage but low temporal resolution, providing [...] Read more.

Sea surface temperature (SST) is vital for climate monitoring and extreme weather forecasting. Existing global SST datasets are typically provided at daily to seasonal resolutions, while hourly data remain limited to regional scales. Polar-orbiting satellites offer global coverage but low temporal resolution, providing only 1–2 observations per day. Geostationary satellites provide high temporal resolution but cover only part of the region. These limitations create a gap in the availability of global, hourly SST data. To address this, we propose a Triple-Collocation (TC)-based fusion algorithm for generating accurate global hourly SST data through multi-source integration. The method includes data preprocessing (quality control and linear interpolation), merging five geostationary SST datasets into two global sets by priority, applying TC fusion to three polar-orbiting datasets, and finally combining all sources via multi-source TC fusion. Results show improved temporal resolution and increased spatial coverage to 32%. The fused dataset achieves high accuracy, with a daily mean Bias below 0.0427 °C, RMSE about 0.5938 °C to 0.6965 °C, and R² exceeding 0.9879. These outcomes demonstrate the method’s reliability and its potential for supporting climate and environmental research. Full article

(This article belongs to the Section Ocean Remote Sensing)

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22 pages, 23389 KB

Open AccessArticle

A Novel Laser Detection Method Against Soot Interference Based on Pulse-Width Comparison

by Yuran Tan, Bingting Zha, Zhen Zheng, Jiaqi Li, Rui He and Junyang Weng

Remote Sens. 2025, 17(17), 3013; https://doi.org/10.3390/rs17173013 - 29 Aug 2025

Abstract

Traditional pulsed laser detection systems predominantly utilize threshold-based decision methods that rely on the peak voltage of echo signals for target recognition. However, in environments with soot (smoke and dust) interference, strong backscattering effects lead to severe signal distortion, resulting in false alarms [...] Read more.

Traditional pulsed laser detection systems predominantly utilize threshold-based decision methods that rely on the peak voltage of echo signals for target recognition. However, in environments with soot (smoke and dust) interference, strong backscattering effects lead to severe signal distortion, resulting in false alarms or missed detections, thereby significantly degrading recognition accuracy. To overcome this limitation, through comprehensive simulations and experimental trials, we propose a novel pulse-width comparison method that utilizes the characteristic broadening of echoes induced by soot particles. The method exploits the differences in pulse width between target and soot-induced echoes and achieves effective interference suppression. The target-detection accuracy increases from 71.25% to 93.75% in environments with different concentrations (0–350 mg/m

^{3}

), corresponding to a 31.58% relative improvement. This approach leverages pulse-width differences between soot-induced and target echoes to enhance anti-interference capability in dusty environments. Full article

28 pages, 3199 KB

Open AccessReview

Assessing the Suitability of Available Global Forest Maps as Reference Tools for EUDR-Compliant Deforestation Monitoring

by Juliana Freitas Beyer, Margret Köthke and Melvin Lippe

Remote Sens. 2025, 17(17), 3012; https://doi.org/10.3390/rs17173012 - 29 Aug 2025

Abstract

Deforestation monitoring is critical to support compliance with regulatory frameworks such as the EU Deforestation Regulation (EUDR), which requires that products containing or derived from beef, cocoa, coffee, palm oil, rubber, soy, and timber are deforestation-free after 31 December 2020. Earth observation (EO) [...] Read more.

Deforestation monitoring is critical to support compliance with regulatory frameworks such as the EU Deforestation Regulation (EUDR), which requires that products containing or derived from beef, cocoa, coffee, palm oil, rubber, soy, and timber are deforestation-free after 31 December 2020. Earth observation (EO) offers a means to assess deforestation, yet map-based verification remains technically limited and uncertain. This study addresses the lack of a systematic assessment of global Forest/Non-Forest (FNF), Tree Cover/Non-Tree Cover (TC/NTC) and Land Use/Land Cover (LULC) datasets by identifying and evaluating 21 publicly available global forest/tree cover reference maps for their alignment with EUDR criteria. This goes beyond merely treating these datasets as simply “fit” or “not fit” for the purpose of the EUDR, but rather aims to assess how well each dataset meets the needs compared to others, acknowledging strengths, weaknesses, and trade-offs. The 21 datasets are reviewed based on EUDR-related parameters (temporal proximity, spatial resolution, and forest definition) as well as accuracy metrics. From this broader review, eight datasets are shortlisted based on their alignment with key regulatory requirements. However, most datasets fail to fully meet all EUDR requirements, particularly forest definitions, with only two datasets satisfying all indicators. Notably, all datasets are unable to distinguish forests from other non-forest, tree-based systems. Reported accuracy metrics reveal a general overestimation of forest areas, while canopy height-based maps tend to underestimate tree cover, potentially excluding forested regions. Regional comparisons show more consistent estimates in South America, while Europe and North America display greater variability. These findings support informed decision-making by companies and policymakers for selecting suitable datasets, while also highlighting conflicts and challenges associated with the use of global forest/tree cover maps for regulatory compliance. Full article

(This article belongs to the Special Issue Using Earth Observation Data to Monitor Deforestation and Forest Degradation to Ensure Compliance with Regulatory Frameworks)

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19 pages, 54218 KB

Open AccessArticle

Estimation of Forest Stock Volume in Complex Terrain Using Spaceborne Lidar

by Yiran Zhang, Qingtai Shu, Xiao Zhang, Zeyu Li and Lianjin Fu

Remote Sens. 2025, 17(17), 3011; https://doi.org/10.3390/rs17173011 - 29 Aug 2025

Abstract

In forest remote sensing monitoring of complex terrain, spaceborne lidar data has become a key technology for obtaining large-scale forest structure parameters due to its uniquethree-dimensional observation capabilities. However, in complex terrain conditions, there are still many challenges for spaceborne lidar. Particularly in [...] Read more.

In forest remote sensing monitoring of complex terrain, spaceborne lidar data has become a key technology for obtaining large-scale forest structure parameters due to its uniquethree-dimensional observation capabilities. However, in complex terrain conditions, there are still many challenges for spaceborne lidar. Particularly in mountainous forest areas with significant topographic relief, overcoming the limitations imposed by complex terrain conditions to achieve high-precision forest stock volume estimation has emerged as one of the most challenging and cutting-edge research areas in vegetation remote sensing. Objective: This study aims to explore the feasibility and methods of forest stock volume estimation using spaceborne lidar data ICESat-2/ATL08 in complex terrain and to compare the effectiveness of three machine learning regression models for this purpose. Method: Based on the ATL08 product from ICESat-2/ATLAS data, a sequential Gaussian conditional simulation was used for spatial interpolation of forest areas in Jingdong Yi Autonomous County, Pu’er City, Yunnan Province. XGBoost, LightGBM, and Random Forest methods were then employed to develop stock volume models, and their estimation capabilities were analyzed and compared. Results: (1) Among the 57 ICESat-2/ATLAS footprint parameters extracted, 13 were retained for interpolation after analysis and screening. (2) Based on sequential Gaussian conditional simulation, three parameters demonstrating lower interpolation accuracy were eliminated, with the remaining ten parameters allocated for inversion model development. (3) In terms of inversion model accuracy, XGBoost outperformed LightGBM and Random Forest, achieving an R² of 0.89 and an rRMSE of 10.5912. The average forest stock volume derived from the inversion was 141.00 m³/hm². Conclusions: Overall, large-area forest stock volume estimation through spaceborne Lidar inversion using ICESat-2/ATLAS photon-counting footprints proved feasible for mountainous environments with complex terrain. The XGBoost method demonstrates strong forest stock volume inversion capabilities. This study provides a case study for investigating forest structure parameters in complex mountainous terrain using spaceborne lidar ICESat-2/ATLAS data. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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25 pages, 4747 KB

Open AccessArticle

Land Cover Change as a Critical Driver of Vegetation Restoration in Water-Scarce Northern China

by Lihua Lan, Zhenbo Wang and Fei He

Remote Sens. 2025, 17(17), 3010; https://doi.org/10.3390/rs17173010 - 29 Aug 2025

Abstract

The implementation of vegetation restoration projects in the Three-North Sandstorm Region has provided a critical opportunity for ecological rehabilitation. A thorough exploration of the mutual feedback mechanisms between land use and vegetation dynamics holds significant guiding value for ecological restoration practices. This study [...] Read more.

The implementation of vegetation restoration projects in the Three-North Sandstorm Region has provided a critical opportunity for ecological rehabilitation. A thorough exploration of the mutual feedback mechanisms between land use and vegetation dynamics holds significant guiding value for ecological restoration practices. This study innovatively integrates the Hurst index, trend analysis, and significance testing to systematically analyze the characteristics of vegetation dynamics and their responses to land-use changes from 1990 to 2020. By constructing a multidimensional evaluation system, including the comprehensive land-use index (L) and land-use change rate (R), this research is the first to quantitatively reveal the relationship between land-use characteristics and the Normalized Difference Vegetation Index (NDVI) across different desertification regions. Key findings include: (1) Vegetation in the study area exhibited a significant improvement trend, with 92.12% of regions showing a Hurst index > 0.5, indicating strong persistence in vegetation recovery. (2) Land-use transitions revealed a consistent increase in construction land across all sandy regions, accompanied by the conversion of unused land to grassland. (3) The fluctuation of R gradually narrowed from 1990 to 2020, while the L demonstrated a significant upward trend. (4) The R generally exerted a negative influence on NDVI, whereas the L exhibited a threshold effect. Specifically, NDVI increased with rising L when L < 215 (e.g., MWSD, KBQD regions), but declined when L exceeded 215 (e.g., SNS region). This suggests that frequent land-use changes hinder vegetation growth, while moderate increases in land-use intensity initially promote biomass accumulation—until a critical threshold is surpassed, beyond which further intensification exerts adverse effects. One-way ANOVA and significance testing further demonstrated that climate predominantly drove NDVI increases in cropland, forest, shrubland, and grassland, whereas human activities played a decisive role in vegetation growth within unused or sparsely vegetated areas. This study not only quantitatively identifies critical thresholds for land-use impacts on vegetation but also provides a scientific foundation for targeted ecological restoration strategies, offering practical insights for reconciling ecological conservation with land-use demands. Full article

26 pages, 8278 KB

Open AccessArticle

Radiative Forcing and Albedo Dynamics in the Yellow River Basin: Trends, Variability, and Land-Cover Effects

by Long He, Qianrui Xi, Mei Sun, Hu Zhang, Junqin Xie and Lei Cui

Remote Sens. 2025, 17(17), 3009; https://doi.org/10.3390/rs17173009 - 29 Aug 2025

Abstract

Climate change results from disruptions in Earth’s radiation energy balance. Radiative forcing is the dominant factor of climate change. Yet, most studies have focused on radiative effects within the calculated actual albedo, usually overlooking the angle effect of regions with large-scale and highly [...] Read more.

Climate change results from disruptions in Earth’s radiation energy balance. Radiative forcing is the dominant factor of climate change. Yet, most studies have focused on radiative effects within the calculated actual albedo, usually overlooking the angle effect of regions with large-scale and highly varied terrain. This study produced the actual albedo databases by using albedo retrieval look-up tables. And then we investigated the spatiotemporal variations in land surface albedo and its corresponding radiative effects in the Yellow River Basin from 2000 to 2022 using MODIS-derived reflectance data. We employed time-series, trend, and anomaly detection analyses alongside surface downward shortwave radiation measurements to quantify the radiative forcing induced by land-cover changes. Our key findings reveal that (i) the basin’s average surface albedo was 0.171, with observed values ranging from 0.058 to 0.289; the highest variability was noted in the Loess Plateau during winter—primarily due to snowfall and low temperatures; (ii) a notable declining trend in the annual average albedo was observed in conjunction with rising temperatures, with annual values fluctuating between 0.165 and 0.184 and monthly averages spanning 0.1595 to 0.1853; (iii) land-cover transitions exerted distinct radiative forcing effects: conversions from grassland, shrubland, and wetland to water bodies produced forcings of 2.657, 2.280, and 2.007 W/m², respectively, while shifts between barren land and cropland generated forcings of 4.315 and 2.696 W/m². In contrast, transitions from cropland to shrubland and from grassland to shrubland resulted in minimal forcing, and changes from impervious surfaces and forested areas to other cover types yielded negative forcing, thereby exerting a net cooling effect. These findings not only deepen our understanding of the interplay between land-cover transitions and radiative forcing within the Yellow River Basin but also offer robust scientific support for regional climate adaptation, ecological planning, and sustainable land use management. Full article

(This article belongs to the Special Issue Time-Series Mapping and Analysis of Land Surface Parameters and Changes Using Remote Sensing Data)

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26 pages, 7561 KB

Open AccessArticle

Satellite Optical Target Edge Detection Based on Knowledge Distillation

by Ying Meng, Luping Zhang, Yan Zhang, Moufa Hu, Fei Zhao and Xinglin Shen

Remote Sens. 2025, 17(17), 3008; https://doi.org/10.3390/rs17173008 - 29 Aug 2025

Abstract

Edge detection of space targets is vital in aerospace applications, such as satellite monitoring and analysis, yet it faces challenges due to diverse target shapes and complex backgrounds. While deep learning-based edge detection methods dominate due to their powerful feature representation capabilities, they [...] Read more.

Edge detection of space targets is vital in aerospace applications, such as satellite monitoring and analysis, yet it faces challenges due to diverse target shapes and complex backgrounds. While deep learning-based edge detection methods dominate due to their powerful feature representation capabilities, they often suffer from large parameter sizes and lack explicit geometric prior constraints for space targets. This paper proposes a novel edge detection method for satellite targets based on knowledge distillation, namely STED-KD. Firstly, a multi-stage distillation strategy is proposed to guide a lightweight, fully convolutional network with fewer parameters to learn key features and decision boundaries from a complex teacher model, achieving model efficiency. Next, a shape prior guidance module is integrated into the student branch, incorporating geometric shape information through shape prior model construction, similarity metric calculation, and feature reconstruction, enhancing adaptability to space targets and improving detection accuracy. Additionally, a curvature-guided edge loss function is designed to ensure continuous and complete edges, minimizing local discontinuities. Experimental results on the UESD space target dataset demonstrate superior performance, with ODS, OIS, and AP scores of 0.659, 0.715, and 0.596, respectively. On the BSDS500, STED-KD achieves ODS, OIS, and AP scores of 0.818, 0.829, and 0.850, respectively, demonstrating strong competitiveness and further confirming its stability. Full article

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29 pages, 15321 KB

Open AccessArticle

Ground-Based Evaluation of Hourly Surface Ozone in China Using CAM-Chem Model Simulations and Himawari-8 Satellite Estimates

by Peng Zhou, Jieming Chou, Li Dan, Jing Peng, Fuqiang Yang, Kai Li, Younong Li, Fugang Li and Hong Wang

Remote Sens. 2025, 17(17), 3007; https://doi.org/10.3390/rs17173007 - 29 Aug 2025

Abstract

Surface ozone pollution poses a significant threat to human health and ecosystems. However, its highly variable spatiotemporal distribution, especially at hourly scales across China, complicates effective risk management. This variability presents substantial challenges for accurate estimation and forecasting, underscoring the importance of evaluating [...] Read more.

Surface ozone pollution poses a significant threat to human health and ecosystems. However, its highly variable spatiotemporal distribution, especially at hourly scales across China, complicates effective risk management. This variability presents substantial challenges for accurate estimation and forecasting, underscoring the importance of evaluating current hourly surface ozone estimation methods. Therefore, this study collaboratively evaluated the performance of chemical transport model simulations and satellite-based estimates of hourly surface ozone concentrations over mainland China in 2019. Using data from 3185 ground monitoring stations operated by the Ministry of Ecology and Environment, as well as six independent observation sites in Hong Kong, Xianghe, Nam Co, Akedala, Longfengshan, and Waliguan, this study found that both datasets exhibited systematic biases and lacked spatiotemporal consistency. The Community Atmosphere Model with Chemistry simulation results exhibited an average relative bias of 23.17%, generally overestimated ozone concentrations in high-altitude regions, but outperformed the satellite-based estimates at the independent sites, while consistently underestimating ozone concentrations in densely populated urban areas. In contrast, the satellite-based estimates performed better in regions with dense monitoring sites, with mean biases typically within 10% of observations, but their accuracy was limited in remote areas due to sparse ground-based calibration. It is particularly noteworthy that both datasets showed deficiencies in capturing extremely high-value events, nighttime ozone variations, and dynamic transport processes, underscoring challenges in the representation of photochemical processes in the model and in the design of satellite estimation algorithms. The results highlight the importance of optimizing model parameterization schemes, improving satellite estimation algorithms, and integrating multi-source data to enhance the accuracy and stability of hourly ozone estimates. This study provides multi-scale quantitative insights into the relative strengths and limitations of different ozone estimation methods, laying a solid scientific foundation for future data integration, regional air quality management, and policy development. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Observation of Aerosol Properties and Assessment of Their Effects)

19 pages, 3020 KB

Open AccessArticle

Prediction of Sandstorm Moving Path in Mongolian Plateau Based on CNN-BiLSTM

by Daoting Zhang, Wala Du, Shan Yu, Zhimin Hong, Dashtseren Avirmed, Mingyue Li and Yu’ang He

Remote Sens. 2025, 17(17), 3006; https://doi.org/10.3390/rs17173006 - 29 Aug 2025

Abstract

The frequent occurrence of sandstorms on the Mongolian Plateau has become a critical factor influencing the stability of regional ecosystems and social activities. In this study, a deep learning framework was developed for predicting sandstorm paths on the Mongolian Plateau. A spatio-temporal feature [...] Read more.

The frequent occurrence of sandstorms on the Mongolian Plateau has become a critical factor influencing the stability of regional ecosystems and social activities. In this study, a deep learning framework was developed for predicting sandstorm paths on the Mongolian Plateau. A spatio-temporal feature dataset was established using remote sensing imagery and meteorological observations. Spatial features were extracted through a convolutional neural network (CNN), while the temporal evolution of sandstorms was modeled using a bidirectional long short-term memory (BiLSTM) network. A random forest algorithm was employed to assess the relative importance of meteorological and geographical factors. The results indicate that the proposed CNN-BiLSTM model achieved strong performance at prediction intervals of 1, 6, 12, 18, and 24 h, with overall accuracy, F1-score, and AUC all exceeding 0.80. The 24 h prediction yielded the best results, with evaluation metrics of 0.861, 0.878, and 0.898, respectively. Compared with the individual CNN and BiLSTM models, the CNN-BiLSTM model demonstrated superior performance. The findings suggest that the model provides high predictive accuracy and stability across different time steps, thereby offering strong support for dust storm path prediction on the Mongolian Plateau and contributing to the reduction of disaster-related risks and losses. Full article

(This article belongs to the Section Ecological Remote Sensing)

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