最新刊期
卷 30 ， 期 5 ， 2026
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摘要：Lava tubes are one of the important sites for insights into the Moon's internal geological features, volcanic eruptions, and lunar soil formation history. Due to stable internal temperature, radiation shielding capabilities, and ability to avoid impacts from small celestial bodies, lava tubes are ideal locations for future human lunar base construction. The Moon is the closest natural celestial body to Earth, and lunar lava tubes are likely to become one of the preferred targets for human on-site exploration and base construction. Radar is one of the key means to detect and identify lava tubes. Summarizing the research results of radar data on lunar lava tubes has significant scientific and engineering meaning. Based on the radar imaging results, the present study reviews the echo features of lunar lava tubes from two aspects: Synthetic Aperture Radar (SAR) imaging for lunar surface and radar imaging for subsurface internal structure. Among them, SAR is mainly conducive to identifying lunar pits formed by the collapse of the top of lava tubes, where SAR image exhibits specific backscattering characteristics due to the differences of pit degradation degree; the key to the internal structure imaging radar identifying hidden lava tubes is the phase inversion characteristics of the echoes from the cave top and bottom interfaces. Based on these, this paper proposes four prospects for future scientific and engineering-related research on lunar lava tubes. It is hoped that the present content will provide scientific basis and reference for related work such as sites selection, scientific exploration, and utilization of subsurface space for lunar bases in the planning of future deep space exploration missions.关键词：Moon;lava tube;skylight;Earth-based radar;orbiter radar;rover radar;backscatter;phase inversion595|78|0更新时间：2026-05-13
Research Progress
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摘要：Tropical mangroves are one of the most productive and biodiverse forest resources but face several challenges such as monoculture planting structures, low ecosystem quality, low survival rates of planted mangroves, and threats from extreme weather and pests. Conducting surveys on mangrove forest resources provides essential data for the scientific management and conservation of these resources. Accurate segmentation of individual trees is a prerequisite for the inventory of such forest resources. Terrestrial Laser Scanning (TLS) can provide massive, high-precision, and high-resolution 3D point cloud data. However, the point cloud data are characterized by irregularities, varying densities due to distance, and incompleteness due to occlusions. Furthermore, the mangrove scene is complex with interlaced large and small trees and tree occlusions, making precise individual tree segmentation a considerable challenge. Traditional methods such as local maximum detection based on Canopy Height Models (CHM), have demonstrated good performance in simple plot scenarios. However, in the complex canopy interwoven environments of mangroves, where the upper canopy features are weak, these methods are less effective. Currently, there is a lack of research on individual tree segmentation algorithms for mangroves based on TLS point clouds. To address these issues, we aim to propose an individual tree segmentation algorithm applicable for complex mangrove scenes. This study innovatively combines deep learning and traditional algorithms to propose a high-precision individual tree segmentation framework for TLS point clouds in complex mangrove scenes. The framework initially employs the deep learning network RandLA-Net for ground filtering and wood-leaf separation. Subsequently, mangrove main stems are segmented using a connected component segmentation method. Finally, individual tree segmentation is achieved through the multiple tree tops constraint module. To assess the accuracy of the algorithm, we use three measures: completeness, correctness, and accuracy. We also conduct a comparative analysis with two classical algorithms. The experimental results demonstrate that the completeness of the proposed method across different mangrove plots is greater than 0.85, with an average of 0.90; the correctness of the proposed method is greater than the two classic algorithms in four plots; the mean accuracy of the proposed method in different sample plots reaches 0.87, which is significantly higher than the two classic algorithms, thus proving the effectiveness and reliability of our method. This paper proposes an individual tree segmentation framework for TLS point clouds in complex mangrove scenes. Seven sample plots with various data characteristics were annotated to assess accuracy. The experimental results show that, compared to other algorithms, the proposed method achieved the highest accuracy. Despite the differing characteristics of the sample plots, the overall accuracy of the proposed method exceeded 0.8, demonstrating its effectiveness and robustness.关键词：tropical mangroves;terrestrial laser scanning;point cloud;individual tree segmentation;deep learning1868|2437|0更新时间：2026-05-13 -
摘要：Forests represent one of the most critical natural resources in the pursuit of global sustainability, fulfilling indispensable roles in climate regulation, biodiversity conservation, and the promotion of ecological civilization. In China, subtropical forests account for approximately 44% of the nation’s total forest coverage, highlighting their essential function in maintaining regional ecological security and supporting national sustainable development goals. Over the past three decades, however, the accelerating convergence of natural stressors and anthropogenic interventions has resulted in the severe and widespread degradation of these fragile ecosystems. To investigate meticulously the spatiotemporal evolution and underlying mechanisms of forest degradation across subtropical China, this study leveraged a multidimensional analytical framework that incorporated the annual China land cover dataset, a high—resolution, longitudinally consistent remote sensing product, with geospatial computational modeling on the Google Earth Engine platform. A comprehensive suite of methodologies, including spectral index—based mask extraction, spatial autocorrelation analysis by using global and local Moran’s I indices, and geographical detector modeling, was employed to quantify individual and interactive driver influences that spanned the period from 1990 to 2020. The analysis yields several critical and nuanced insights. (1) Since 1990, the cumulative degraded forest area in subtropical China has amounted to 25.3963 million ha, equivalent to 17.96% of the country’s total forest inventory. Within this degraded area, 38% has been transformed into cropland and grassland, illustrating the profound effects of agricultural expansion and economic development on forest cover change. (2) Spatially, the degradation exhibits a pronounced increasing gradient from northeastern to southwestern subregions, with cartographic patterns revealing significant clustering behavior, specifically high-high clusters (hotspots of degradation), low-low clusters (coldspots), and spatial outliers, such as high-low and low-high associations. These patterns, validated through local indicators of spatial association, indicate not only regional aggregation of forest loss but also strong spatial dependency, which is characteristic of environmentally contagious degradation processes. (3) Topographic analysis indicates that forest degradation occurs predominantly at elevations below 2,250 m and on slopes less than 50°. The distribution of the degradation area relative to elevation and slope manifests a distinct inverted U—shape, peaking at 150 m and 8°, respectively. This condition suggests that moderate terrain, which is characterized by higher accessibility and suitability for human modification, is most susceptible to ecological disturbance. (4) While individual biophysical and socioeconomic factors, including slope, aspect, population density, and gross domestic product (GDP) per capita, demonstrate limited explanatory power when considered in isolation, their pairwise interactions exhibit substantially stronger influences. The interaction between slope and GDP per capita is particularly salient, with a q—value of 0.96 in geographical detector analysis, underscoring the synergistic effect of economic development and terrain conditions in driving forest degradation. The current study elucidates the complex, multifaceted nature of forest degradation in subtropical China, weaving topographic, economic, and demographic factors into an integrated explanatory framework. It emphasizes the necessity of adopting holistic forest governance strategies that harmonize economic development with ecological preservation and advocates for regionally differentiated conservation policies. The findings provide a robust scientific foundation for enhancing spatial planning, guiding ecological restoration initiatives, and facilitating sustainable forest management practices across subtropical China in an era of rapid global change.关键词：Chinese subtropics;Google Earth Engine (GEE);forest degradation;geographic detector;Moran’s I;influencing factors318|94|0更新时间：2026-05-13 -
摘要：In the context of global climate warming, rising temperatures have increased the frequency and severity of drought in subtropical regions, leading to widespread vegetation mortality and posing a substantial threat to vegetation ecosystems and forest carbon sink. The accurate quantification and understanding of vegetation drought are critical for regulating vegetation mortality rates during drought events. However, the precise quantification of vegetation drought remains controversial, particularly in terms of variations among different vegetation covers and within the same vegetation cover, especially in subtropical regions with complex terrain and dense vegetation. This study aims to develop a reliable approach for the quantitative monitoring of vegetation drought in subtropical regions, addressing “same vegetation cover with different drought degrees” and “same drought degree with different vegetation covers.”To achieve precise quantification, a Vegetation Drought Response (VDR) module, which characterized the spatiotemporal response of vegetation to soil moisture over time, was developed. The module utilizes spatiotemporal features that are constructed from the multispectral-based modified vegetation index and land surface temperature. Drought boundaries were scientifically defined using the general nature rationale that decreasing soil moisture leads to vegetation withering, while increasing soil moisture promotes vegetation flourishing, allowing the identification of time intervals during the Vegetation Drought Process (VDP). The sensitivity of vegetation response to soil moisture within these intervals was used to determine VDR characteristics at the beginning of a drought event, informing the establishment of the Vegetation Drought Threshold (VDT). By applying VDT to VDR, a Process-Cognizant Vegetation Drought Model (PCVDM) was constructed, enabling the quantitative inversion of vegetation drought. The method was applied to the Hunan-Jiangxi region, which is located in the middle of China’s subtropical region, by using remote sensing techniques to retrieve spatiotemporal changes in vegetation drought from 2000 to 2023. A spatiotemporal differentiation analysis was conducted by integrating altitude and lithology conditions to investigate causal factors.Results demonstrate that PCVDM effectively captures the spatiotemporal dynamics of vegetation drought in the Hunan–Jiangxi region, where a clear spatial differentiation is observed. High-altitude areas (>800 m) exhibit increased greenness due to rising temperatures, while low-altitude areas (<200 m) experience intensified vegetation drought as a result of enhanced evapotranspiration. Moderate-altitude areas (approximately 400 m) present mixed responses that are influenced by lithology and slope difference, with increased greenness coexisting alongside vegetation drought phenomena. These findings suggest that vegetation drought patterns are shaped by the combined effects of climatic factors and topographic or edaphic conditions, resulting in distinct responses across elevation gradients and lithological types.The constructed PCVDM provides a practical tool for remote sensing-based monitoring of vegetation drought in subtropical regions, enabling spatiotemporal differentiation across elevations and lithologies. The model reveals long-term vegetation trends in the Hunan–Jiangxi region and supports strategies for drought management, ecosystem conservation, and carbon sink study under climate warming.关键词：subtropical remote sensing;vegetation drought monitoring;process-cognizant vegetation drought model;Mann-Kendall Test;Hunan-Jiangxi Region258|204|0更新时间：2026-05-13 -
摘要：Atmospheric water vapor is a crucial component of the troposphere, playing a decisive role in the global energy budget and the formation of severe weather events. Hunan province, located in a subtropical monsoon humid climate zone with complex topography, frequently experiences extreme weather disasters such as severe convection and rainstorms driven by intense water vapor activity. Precisely monitoring the high-dynamic three-dimensional (3D) water vapor field is of great significance for early warning of these disasters. However, Global Navigation Satellite System (GNSS) water vapor tomography, a primary remote sensing technique, faces significant challenges. Traditional voxel-based models assume uniform water vapor distribution within a grid, failing to describe continuous spatial variations. Moreover, limited by station density, these methods often require long time windows (e.g., 30 minutes) to accumulate observations, resulting in low temporal resolution that misses the rapid evolution of water vapor during extreme weather. This study aims to develop a novel tomography framework to reconstruct water vapor fields with high spatiotemporal resolution (e.g., 5 minutes) in high-dynamic environments.A High-dynamic Node-parameterized Water Vapor Tomography Two-step Method (HNT-TSM) is proposed. Unlike discrete voxel models, this method employs a node-based parameterization strategy where the wet refractivity at any spatial point is determined by the interpolation of eight surrounding node parameters, ensuring spatial continuity. To address the ill-posed problem in high-resolution retrieval, the method incorporates an adaptive vertical constraint, introducing a vertical variation parameter that updates automatically during iteration to optimize the design matrix. The core reconstruction follows a "two-step" framework: (1) a 30-minute window is used to reconstruct the linear variation trend (background field) to ensure model stability; (2) based on the modeling residuals from the first step, a residual tomography model inverts high-frequency deviation terms within short 5-minute intervals. This approach effectively separates stable background signals from rapid dynamic fluctuations, implemented using the Algebraic Reconstruction Technique (ART).The method was validated using GNSS data from 123 stations in the Hunan Continuously Operating Reference Stations (HNCORS) network during June 2022. Four schemes were designed for comparison: Scheme 1 (traditional voxel-based model with uniform distribution), Scheme 2 (standard node-parameterized model), Scheme 3 (linear time-varying node-parameterized model), and Scheme 4 (the proposed HNT-TSM). Evaluation using independent Slant Wet Delays (SWD) and ERA5 reanalysis demonstrated that HNT-TSM significantly outperforms the benchmarks. Specifically, in terms of external validation accuracy, the proposed method improved by 35%, 29%, and 26% compared to Scheme 1, Scheme 2, and Scheme 3, respectively. Furthermore, in a heavy rainstorm case study on June 19, the 5-minute resolution products generated by HNT-TSM successfully captured rapid water vapor convergence and dissipation processes missed by low-resolution models, showing high consistency with the precipitation distribution.The HNT-TSM effectively resolves the conflict between temporal resolution and model stability in GNSS tomography. By integrating node parameterization with a two-step reconstruction strategy, it achieves high-precision 3D monitoring at a 5-minute level. This method demonstrates significant advantages in regions with complex terrain and active water vapor changes, such as the subtropical monsoon region. The resulting high-spatiotemporal-resolution products provide robust data for analyzing extreme weather mechanisms and hold great potential for data assimilation in numerical weather prediction systems.关键词：water vapor tomography;wet refractivity;node parameterization;high-dynamic;subtropical66|33|0更新时间：2026-05-13
Subtropical Remote Sensing
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摘要：Global Aboveground forest Biomass (AGB) products have become increasingly abundant in recent years, providing valuable data for assessing carbon stocks and fluxes. However, substantial spatiotemporal inconsistencies among these products have led to large uncertainties in the estimation of global carbon storage and carbon sink strength. This study aims to evaluate systematically the interannual consistency of major global AGB products and identify their strengths and limitations for long-term biomass monitoring and carbon accounting. We integrated multi-source forest biomass data of AGB data, including satellite-derived products, e.g., European Space Agency Climate Change Initiative (CCI), NASA Jet Propulsion Laboratory (JPL), dynamic global vegetation model (DGVM) simulations (net biome poductivity, carbon in vegetation), and ground-based validation datasets. A multidimensional evaluation framework was designed from three perspectives: (1) spatial consistency, assessed using correlation coefficients and spatial agreement metrics among products; (2) interannual variability, analyzed through temporal correlation and trend consistency; (3) ground validation, performed using field observations to quantify product accuracy in regions of biomass increase and decrease. The results show the following: (1) different remote sensing products exhibit pronounced differences in spatial consistency. Single-epoch products based on Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) show relatively high spatial consistency ( -
摘要：Forest aboveground biomass (AGB) is a crucial component of the global carbon cycle, playing a key role in monitoring global carbon dynamics and effectively mitigating climate change. The segmentation of individual tree components to distinguish wood and leaf structures effectively is an important foundation for accurately estimating the key structural parameters of trees and precisely inferring AGB. With advancements in technology, the advent of terrestrial LiDAR has provided a novel nondestructive method for estimating tree structural parameters and AGB. However, current algorithms for individual tree component segmentation that use point clouds exhibit limited universality across different tree species, and their capability to segment fine branches remains relatively constrained.Therefore, this study constructs a large individual tree component segmentation dataset, namely, ITS-3D, which contains 713 tree samples to address the issue of insufficient high-quality training samples for individual tree component segmentation. In addition, the state-of-the-art point transformer-V3 deep learning network is employed on the ITS-3D dataset for the segmentation of individual tree components, including the main trunk, branches, and leaf categories. Furthermore, this study optimizes the segmentation performance of the point transformer-V3 network for individual tree point clouds by constructing individual tree prior geometric features (SoD index, PCA2 index, and verticality index). Subsequently, a comparative study will be conducted with several current major deep learning algorithms. Finally, this study also validates the effectiveness of the introduced prior geometric features through ablation experiments.Experimental results demonstrate that the segmentation performance achieved an Overall Accuracy (OA) of 0.946, mean accuracy of 0.872, and mean intersection over union (mIoU) of 0.806, reflecting high segmentation precision. Even smaller, higher-order branches within the tree crown are successfully extracted. Moreover, the model’s OA and mIoU values for various TLS subsets of ITS-3D exceed 0.928 and 0.737, respectively, showing high generalization capability across tree species with large geometric structural differences. Furthermore, compared with other mainstream deep learning networks, the point transformer-V3 network adopted in this study attains the highest values for segmentation accuracy metrics. In addition, in the performance comparison with traditional leaf-wood separation algorithms, such as LeWoS, the point transformer-V3 network increases OA and mIoU by 7.9% and 17.2%, respectively. This result fully demonstrates the excellent performance and generalization capability of high-performance deep learning techniques in individual tree component segmentation across multiple tree species. Finally, when all the prior geometric features of trees are incorporated as input in the ablation experiments, the OA and mIoU of the segmentation results reach their peak values of 94.63% and 80.59%, respectively. This finding represents an increase of 0.29% and 0.42% compared with the scenario where only the 3D coordinates of point clouds are used as input, indicating that introducing effective individual tree prior features during training can better promote the segmentation effect of a deep learning network.Through this research, the application of state-of-the-art deep learning technology to individual tree component segmentation can be improved further, providing technical support for the precise estimation of tree structural parameters.关键词：Individual tree component;LiDAR point clouds;semantic segmentation;prior features;deep learning;Leaf-wood separation;Branch extraction;Serialized attention mechanism204|171|0更新时间：2026-05-13 -
摘要：Climate change has intensified precipitation variability in arid regions, disrupting hydrological regimes and increasing flood risk. Flood control systems in these regions are frequently designed primarily for drought mitigation and water storage, limiting their capability to withstand extreme rainfall. In late July 2025, the Tumochuan Plain in Inner Mongolia experienced basin-wide flooding triggered by an unprecedented prolonged rainfall event. Multiple embankment breaches subsequently occurred along the Hasuhai drainage canal, causing severe damage to agricultural production and infrastructure. Accordingly, this study aims to provide timely and reliable information on flood evolution and quantify flood effects on croplands, particularly maize inundation and associated yield loss risk. A refined flood mapping method was developed by integrating a composite water index with morphological operations to enable the efficient automated extraction of surface water information. To reduce misclassification caused by terrain and building shadows, a rule-based discrimination scheme was implemented during post-processing. The proposed method was applied to Sentinel-2 imagery to extract surface water across the Tumochuan Plain for 2024 and 2025. By using water occurrence frequency in 2024, permanent and seasonal water bodies were distinguished to establish a baseline water distribution. This baseline was then used as a reference to delineate the 2025 flood extent and monitor its temporal evolution. To investigate maize growth responses to inundation duration, a dynamic time warping (DTW)-k-means model was constructed by combining DTW with the k-means algorithm to cluster the normalized difference vegetation index (NDVI) phenological trajectories. Cluster centroids represent NDVI recovery trajectories under different inundation durations. The trained model was applied across the study area to assess maize yield loss risk. The proposed automated water detection method outperformed the optimal threshold-based approach derived from Sentinel-1 data, achieving an overall classification accuracy of 97.4% and a kappa coefficient of 0.947. Time-series analysis indicated that flood extent peaked around August 25, with the total water area reaching 880.01 km2, approximately 2.2 times the normal extent in 2024. Flood recession was slow, with the inundated area decreasing by approximately 53% over the following month. Among major crops, maize was the most severely affected, with an inundated area of 192.6 km2. Approximately 39.4% of croplands remained waterlogged for more than 30 days, mostly in low-lying lands and along river systems. The DTW-k-means model clustered NDVI recovery trajectories into three types associated with flood duration. The subsequent yield loss risk assessment indicated high-risk areas that covered 238.9 km2, primarily corresponding to prolonged inundation. Maize exposed to flooding for more than 2 weeks faces an elevated risk of lodging and potential mortality, indicating limited resilience to prolonged inundation. These findings demonstrate the capability of remote sensing to link flood dynamics with abnormal crop growth responses in arid regions. The proposed framework enables rapid flood extent mapping and maize yield loss risk assessment, and it can be transferred to other regions affected by similar flood events.关键词：remote sensing;Flood disaster;automated flood mapping;crop anomaly monitoring;time-series analysis378|165|0更新时间：2026-05-13 -
摘要：As an important part of the terrestrial ecosystem, vegetation exerts a profound influence on global climate variation. Accurately characterizing vegetation dynamics is crucial for capturing and predicting changes in ecosystem structure and function in the context of global warming. On the basis of existing long-time series and multisource vegetation remote sensing products, this study uses the spectrum analysis method to characterize the spatial pattern of global continental surface vegetation on timescales and analyzes the dynamic trend characteristics of vegetation from the perspective of multiple timescales.Using long-term, multi-source remote sensing vegetation products, this study decomposes vegetation dynamics into three key timescales: interannual, annual, and intra-annual through by using spectral analysis. The amplitude obtained from Fourier analysis is used to characterize the spatial pattern of vegetation dynamics, and the phase value is used to evaluate the coupling relationship with phenological parameters. In addition, the temporal trend of vegetation indices is evaluated to explore its relationship with the observed “global greening” phenomenon.(1) Vegetation growth dynamics in most areas of the global land surface (78.4%) are largely dominated by the annual scale. The areas dominated by the intra-annual scale account for 16.1% and are mostly distributed in tropical areas. Areas dominated by the interannual scale constitute the smallest proportion (5.5%) and are mostly located in semiarid shrublands. (2) A significant correlation exists between the annual scale amplitude of vegetation and the peak value of vegetation growth. This correlation is mostly distributed in regions with more significant characteristics of vegetation seasonal growth. Only 28.19% of the global vegetation cover areas exhibited a significant positive correlation between the phase values at the annual scale and the phenological parameters in the time domain (P<0.1). Among these, the correlation between the peak vegetation period and annual-scale phase values is the strongest. (3) The amplitude temporal trend of the vegetation index on multiple timescales (Low frequency, Middle frequency, High frequency) exhibits a large-scale growth trend, which is generally consistent with the phenomenon of “global vegetation greening” (i.e., increasing annual mean vegetation index values). However, the trend characteristics exhibit considerable spatial heterogeneity across scales. Notably, amplitude dynamics at the three scales explain only 66% of the annual mean dynamic characteristics, highlighting the temporal scale dependence of the “global vegetation greening” phenomenon.This study employs spectral analysis to conduct a multi-temporal scale analysis of global vegetation dynamics, offering a novel perspective for related research. The findings contribute significantly to a deeper scientific understanding of global vegetation greening and its response mechanisms within broader ecosystem functions.关键词：terrestrial vegetation;timescales;spectrum analysis;climate change;vegetation phenology80|38|0更新时间：2026-05-13 -
摘要：Leaf Area Index (LAI) is a key biophysical parameter that characterizes the canopy structure and growth status of crops. The accurate and timely monitoring of LAI by using remote sensing technology is crucial for field water and fertilizer management, food security assurance, and the assessment of agricultural production potential. As a spectrally sensitive band that indicates leaf physiology and canopy structural changes, the red-edge region has been introduced into multiple medium- to high-resolution (10—30 m) satellite sensors and widely applied to crop parameter estimation, providing opportunities to improve further the accuracy of LAI retrieval. However, existing studies have demonstrated considerable differences in the application of red-edge bands for LAI inversion, and the effective means to leverage red-edge information for improving LAI retrieval remains unclear due to variations in study regions. To address this issue, this study proposed a hybrid method for crop LAI retrieval by integrating the PROSAIL model with Machine Learning (ML) algorithms.This study developed a hybrid LAI retrieval framework by coupling the PROSAIL model with ML algorithms, using Sentinel-2 multispectral imagery and in-situ LAI measurements of major cereal crops (rice, wheat, and maize) provided by the Chinese National Ecosystem Research Network. First, the PROSAIL model was used to generate LAI-canopy reflectance simulation dataset, and sensitivity analysis of the model parameters was conducted to clarify their contributions to the reflectance of different bands. Then, five ML algorithms, namely, multilayer perceptron regression (MLPR), support vector regression (SVR), Gaussian process regression (GPR), random forest regression (RFR), and gradient boosting decision tree (GBDT), were used to develop hybrid inversion models based on the generated simulation dataset, and the optimal ML algorithm was selected based on the validation dataset. Subsequently, the performance of the retrieval models that use eight band combinations was evaluated to identify the optimal combination that incorporated red-edge bands. Finally, on the basis of the optimization of the ML algorithm and band combination, we further evaluated the accuracy and applicability of the proposed method under different scenarios.Results demonstrated that the integration of red-edge bands effectively improved LAI retrieval performance, with the joint utilization of Red-Edge 1 (RE1) and Red-Edge 3 (RE3) contributing most significantly. MLPR performed the best in the validation and testing datasets, highlighting its capability to capture the nonlinear relationship between canopy reflectance and LAI. Compared with the Z1 combination (Green+Red+NIR+SWIR1+SWIR2) without red-edge bands, the Z7 combination (Green+Red+NIR+SWIR1+SWIR2+RE1+RE3) achieved the highest inversion accuracy (R2=0.784, RMSE=0.826), with R2 increasing by 4.9% and RMSE decreasing by 15.6%. Further analysis indicated that the appropriate incorporation of the red-edge bands not only reduced systematic bias in LAI estimation but also effectively mitigated the saturation effect in the medium to high LAI range (4<LAI<5), with |Bias| and RMSE decreasing by 52.2% and 41.4%, respectively. In addition, different crops showed varied responses to red-edge information, with the most significant improvement in maize (R2 increased by 17.9% and RMSE decreased by 29.1%), followed by wheat and then rice. These differences may be attributed to variations in canopy structure and leaf distribution among crop types.Overall, the combination of the optimal ML algorithm and red-edge bands can significantly improve the accuracy and robustness of crop LAI inversion. This study provides methodological support and practical guidance for fully utilizing red-edge information in the large-scale and long-term precise monitoring of crop growth.关键词：crop leaf area index;red-edge bands;prosail model;machine learning;band selection307|249|0更新时间：2026-05-13 -
摘要：The accurate estimation of Leaf Chlorophyll Content (LCC) is of considerable significance for crop physiological monitoring and precision agricultural management. However, traditional Vegetation Indices (VIs) based on visible-near infrared canopy reflectance experience notable challenges in LCC retrieval, as follows: (1) the spectral response is highly coupled with target information (LCC) and structural noise due to the confounding effects of canopy structural signals; and (2) canopy structural heterogeneity across different crop types further exacerbates the sensitivity of VIs to structural parameters, significantly limiting the generalization and applicability of inversion models for major food crops, such as rice, wheat, and maize, under diverse growth conditions.To address these issues, this study proposes a hybrid LCC retrieval framework that synergistically integrates remote sensing physical mechanisms with deep learning techniques, aiming to mitigate effectively the confounding influence of canopy structure and enhance model generalizability across diverse crop types. Specifically, a set of Vegetation Index Ratio Features (VIRFS) that exhibit low sensitivity to Leaf Area Index (LAI) is constructed by simulating a wide range of LAI-LCC combinations by using the PROSAIL radiative transfer model. Furthermore, an active learning-based strategy is incorporated into the transfer learning pipeline to optimize the selection of informative samples, enabling efficient model fine-tuning with limited labeled field data. The proposed method is systematically validated on multi-crop, multiregional datasets that are collected from three major agricultural zones in China: Northeast China (maize, rice, soybean), the Huang-Huai-Hai Plain (wheat), and the Yangtze River Basin (rice).The results show the following(1) the proposed hybrid method, which integrates radiative transfer mechanisms with deep learning, achieves excellent performance in cross-regional LCC retrieval for major staple crops. It exhibits strong stability and generalizability across diverse crop types with R² consistently exceeding 0.69 and root mean square error (RMSE) lower than 4.77 μg/cm². (2) Compared with the conventional vegetation index feature set, the newly constructed VIRFS is designed to be less sensitive to LAI, significantly mitigating canopy structural interference under optimal fine-tuning conditions. Across the three major agricultural regions (Northeast China, the Huang-Huai-Hai Plain, and the Yangtze River Basin), VIRFS improves R² by 0.18—0.23 and reduces RMSE by 1.85—2.51 μg/cm² for various staple crops. (3) The transfer learning framework that incorporates active learning enables high-accuracy LCC estimation by using only 30% of locally labeled samples, achieving R² values of 0.69—0.74 and RMSE of 4.98—5.76 μg/cm² across different staple crops. This result represents a notable improvement over random sampling-based transfer learning, with R² gains of 0.02—0.16 and RMSE reductions of 0.05—1.42 μg/cm², substantially enhancing model adaptability and inversion efficiency under limited label conditions.In conclusion, the proposed inversion framework, which coupled physical principles with data-driven methods, significantly improves the accuracy and robustness of multi-crop LCC estimation. It provides a universal solution for nondestructive LCC monitoring across diverse crops and regions.关键词：leaf chlorophyll content;UAV-based multispectral remote sensing;canopy structural heterogeneity;vegetation index ratio features;transfer learning;active learning238|405|0更新时间：2026-05-13
Forestry and Agriculture
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摘要：Tomographic Synthetic Aperture Radar (TomoSAR) utilizes multi-baseline synthetic aperture radar (SAR) observations to synthesize an elevation aperture, enabling the retrieval of the vertical distribution of radar scatterers within a single range–azimuth resolution cell. By extending conventional 2D SAR imaging into the elevation dimension, TomoSAR provides a unique capability for the 3D reconstruction of complex scenes, particularly in forested, urban, and cryospheric environments where volumetric or multilayer scattering dominates. Over the past two decades, TomoSAR has evolved from a primarily theoretical concept into a mature research field that is supported by airborne and spaceborne SAR missions, accompanied by rapid advances in inversion algorithms and application-driven studies.This study presents a comprehensive review of TomoSAR imaging theory, height inversion methodologies, and representative application scenarios. Starting from the fundamental signal model, the TomoSAR observation process is described as an elevation-domain spectral estimation problem, where discrete and frequently nonuniform baselines sample the vertical reflectivity function. On the basis of this formulation, existing inversion approaches are systematically categorized into several major classes, including Fourier-based spectral analysis and classical beamforming, adaptive nonparametric methods (e.g., Capon and singular value decomposition), parametric subspace-based techniques, sparse reconstruction and compressive sensing approaches, and recent data-driven deep learning methods. For each category, the underlying principles, algorithmic assumptions, and typical implementation characteristics are summarized to provide a unified methodological perspective.A critical comparison of these methods is conducted in terms of vertical resolution, robustness to baseline sparsity and temporal decorrelation, radiometric fidelity, and computational efficiency. Conventional Fourier-based and beamforming techniques are computationally efficient and physically interpretable but inherently limited by the effective elevation aperture. Adaptive and parametric methods offer improved resolution and sidelobe suppression under favorable conditions, but their performance strongly depends on accurate covariance estimation, model order selection, and coherence preservation. Sparse reconstruction techniques demonstrate strong super-resolution capabilities under limited and irregular baseline configurations at the cost of increased computational complexity and sensitivity to regularization parameters. Deep learning-based TomoSAR inversion has emerged as a promising alternative, providing fast inference and adaptability to nonideal acquisition geometries, although its generalization capability, physical interpretability, and dependence on representative training data remain active research concerns.The review further discusses key challenges that continue to limit the practical deployment of TomoSAR systems. These include baseline nonuniformity and insufficient sampling, mixed scattering mechanisms within resolution cells, temporal decorrelation and phase disturbances caused by atmospheric or platform-related effects, and scalability issues associated with large-area or high-resolution processing. Existing mitigation strategies, such as optimized baseline design, physics-aware modeling, phase error compensation, and regularized inversion, are summarized and critically assessed in terms of their effectiveness and limitations.Finally, emerging trends and future research directions are outlined. These include multifrequency and multi-polarization TomoSAR for enhanced physical interpretation, hybrid inversion frameworks that combine physical models with learning-based techniques, standardized validation protocols supported by external reference data such as LiDAR, and computational acceleration strategies for operational-scale applications. By consolidating theoretical foundations, methodological developments, and practical insights, this review aims to provide a coherent reference for researchers and practitioners, and to support the continued advancement of TomoSAR toward reliable and large-scale 3D Earth observations.关键词：TomoSAR;tomographic height inversion algorithm;spectral estimation algorithm;compressive sensing;three-dimensional reconstruction;baseline distribution238|166|0更新时间：2026-05-13 -
摘要：This study proposes a novel method for mapping landfast ice in polar regions using Sentinel-1 synthetic aperture radar (SAR) imagery. Landfast ice, which is attached to coastal boundaries, plays a crucial role in influencing polar climate with its heat exchange and radiation processes. Mapping landfast ice extent by using regularly acquired remote sensing SAR imageries, such as those from Sentinel-1, is necessary due to climate change and increasing commercial activities in polar coastal areas. However, the scattering properties of landfast ice and surrounding rough sea ice or open water can be similar, and thus, distinguishing between fixed and non-fixed ice regions based solely on SAR backscatter characteristics is a challenging task. The repeat-pass interferometric SAR (InSAR) method identifies in accordance with a coherence map. However, the results are affected by temporal decorrelation due to displacement and/or its melting and refreezing surface, particularly during early winter and/or the melting season. To address this challenge, this study introduces a method based on multiple aperture coherence (MAC). This method effectively improves the accuracy and temporal resolution of landfast ice identification based on Sentinel-1 imagery.The proposed method utilizes sub-aperture decomposition of single Sentinel-1 interferometric wide swath single look complex images to generate MAC index images. By splitting SAR image into forward and backward sub-apertures, coherence maps that are sensitive to the stability of the ice features are produced. These MAC index images are then classified into seawater and sea ice by using Otsu’s thresholding algorithm. The sea ice regions are further segmented into connected components to identify landfast ice, which is defined as ice connected to the coast. The method was applied to four distinct study areas in Greenland (Melville Bugt and Jokel Bay) and Antarctica (Amundsen Coast and Banzare Coast), covering various polar environments. For validation, Sentinel-2 and Landsat imageries acquired within 24 h of the corresponding Sentinel-1 scenes were used to assess accuracy.Validation achieved an Overall Accuracy (OA) of 96.84% for the proposed method, a kappa coefficient of 0.9338, and an F1-score of 0.9666 (averaged across the four study sites), demonstrating its effectiveness under diverse geographic conditions. Results revealed extensive landfast ice coverage in each region, with significant seasonal variations driven by environmental factors. Compared with repeat-pass InSAR, the proposed method is less susceptible to temporal decorrelation, ensuring reliable landfast ice extraction even during rapid changes (e.g., the melting season). Although a clear difference in the MAC index was observed between sea ice and seawater, the method occasionally produced sharp edges at burst boundaries during the acquisition of terrain observation by progressive scans. This condition may affect sea ice/water separation and warrants further investigation. Moreover, future research may focus on applying this method to assess seasonal and interannual variations in landfast ice extent, deepening our understanding of the mechanisms that influence polar sea ice stability.关键词：polar remote sensing;landfast ice;sea ice;SAR coherence;multi aperture coherence;Sentinel-1;TOPS;InSAR208|189|0更新时间：2026-05-13 -
摘要：Hydrological connectivity is generally defined as the extent of interaction among rivers, lakes, their floodplains, or other water bodies. Changes in hydrological connectivity profoundly affect the structure and function of aquatic ecosystems, the maintenance of biodiversity, and water-related human production and livelihood activities. Human interventions, such as dam, levee construction, and land reclamation, have significantly altered connectivity in many floodplain lake regions. Therefore, exploring scientific and effective methods for quantifying hydrological connectivity, analyzing its dynamic changes, and assessing its potential ecological and environmental effects is of considerable significance.The Connectivity Function (CF) effectively characterizes hydrological connectivity and its dynamic changes. However, connectivity values calculated using this method vary with the statistical window and the connecting direction, leading to incomparable results. Building on this method, our study proposes a connection frequency method at optimal scales (Fcon) based on remote sensing-derived surface water extraction results. This method comprehensively considers the scale characteristics of water bodies in different regions and calculates temporal changes in hydrological connectivity by using the optimal scale as a baseline. In contrast with existing CF methods, this approach considers connection frequency in all directions within the study area rather than only a single direction, addressing their limitations. This approach enables comparative analyses of hydrological connectivity changes across multiple regions. Comparisons with connectivity indices from landscape ecology have demonstrated that this method effectively represents inter-patch and intra-patch connectivity. The proposed approach has unique application value, offering a robust tool for analyzing hydrological connectivity changes across different regions.As one of the regions with the highest concentration of surface water in China, the Middle and Lower Yangtze River Plain is a critical ecological and economic area. In recent decades, the connectivity of water bodies in this region has been progressively disrupted, triggering a series of ecological security issues, such as lake water quality deterioration and wetland degradation. On the basis of the Fcon method, we employed monthly water history data from seven different periods between 1984 and 2021 to calculate the connectivity of water bodies in the six subregions of the Middle and Lower Yangtze River Plain, aiming to provide scientific support for regional ecological conservation and restoration.Results indicate that hydrological connectivity in the plains of Poyang Lake Basin, Dongting Lake Basin, Hanjiang River Basin and the Lower Yangtze River Mainstream, experienced significant changes, generally exhibiting a trend of initial decline followed by recovery. For Poyang Lake, Dongting Lake and the Low Reaches of Yangtze River Mainstream plains, hydrological connectivity maintained at relatively high levels, but despite the partial recovery observed in the later periods, all three regions exhibited an overall decline compared with the initial period. By contrast, the Hanjiang River Basin Plain showed substantially lower hydrological connectivity than the other regions and exhibited a generally weaker degree of hydrological linkage. The Taihu Lake Basin and the Middle Reaches of Yangtze River demonstrated a persistent decline in hydrological connectivity, with minor fluctuations, resulting in an overall downward trend during the study period. These differences suggest that hydrological connectivity may be influenced by various factors, particularly ecological and management measures during specific periods, which can exert significant effects on connectivity dynamics.关键词：optimal scale;Hydrological connectivity;Geostatistical analysis;remote sensing;the Middle and Lower Yangtze River Plain222|176|0更新时间：2026-05-13 -
摘要：The Ku/Ka Dual-Frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) satellite provides valuable data for studying precipitation characteristics. Comparing DPR data with X-band ground-based radar (GR) data is essential for understanding their similarities and differences, which in turn supports the integrated application of space-borne and ground-based radar systems and facilitates the calibration and correction of X-band ground-based radar.In this study, a total of 8,483 matched samples of reflectivities from Fengyang X-band ground-based radar located in the Huai River Basin and GPM KuPR were collected, from May to September 2023. These samples were obtained after undergoing rigorous quality control, spatio-temporal matching, and frequency calibration. Based on these samples, the consistency of reflectivities between the Fengyang X-band radar and GPM KuPR was conducted. Furthermore, the impact of varying precipitation intensities, precipitation types, and precipitation phases on the consistency between the two instruments was analyzed.Results indicate that the precipitation echo patterns detected by GR and GPM KuPR are generally consistent, although GR tends to detect stronger echo intensities. The reflectivities of GR and GPM KuPR show a positive correlation, with an overall correlation coefficient of 0.73. The reflectivities of GR are higher than that of GPM KuPR, and the overall average deviation is 2.71 dB. During light and moderate precipitation, the reflectivities of GR and GPM KuPR agree well, with average deviations (GR - KuPR) within ±5 dB. However, during heavy precipitation, the absolute deviation increases significantly. When precipitation consists of small particles, the reflectivities measured by the two radars exhibit good agreement, with average deviations (GR - KuPR) within ±5 dB. In the case of large precipitation particles, the absolute deviation increases significantly. In stratiform precipitation and liquid precipitation below the bright band, the consistency between GR and GPM KuPR is relatively high, with correlation coefficients of 0.72 and 0.73, and average deviations of 3.28 dB and 2.82 dB, respectively. Conversely, in convective precipitation, mixed precipitation within the bright band, and ice-phase precipitation above the bright band, the consistency is relatively lower, with correlation coefficients below 0.65.Overall, X-band ground-based radar and GPM DPR demonstrate potential for combined application in stratiform and liquid precipitation, but further calibration and processing are required for heavy precipitation and complex meteorological conditions.关键词：GPM KuPR;X-band ground-based radar;Reflectivity;Consistency analysis;spatio-temporal matching;Huai River Basin65|23|0更新时间：2026-05-13 -
摘要：River discharge is a fundamental basis for water resource allocation and ecological conservation. Conventional discharge observation methods are labor-intensive and time-consuming; thus, they fail to meet the requirements of modern hydrological monitoring in terms of spatial coverage and timeliness. The continuous development of remote sensing technology has provided efficient and wide-coverage data resources and technical approaches for river discharge inversion studies.Based on the Google Earth Engine (GEE) cloud computing platform, this study utilized Landsat-5/7/8 and Sentinel-1/2 satellite imagery to batch extract river width data for the Waizhou reach of the Ganjiang River Basin from 1990 to 2019 and for the Zhangshu, Xiajiang, and Ji’an reaches from 2003 to 2019. The river discharge for the four reaches was estimated using the power law and linear functions.The results indicate that river widths extracted from multisource remote sensing imagery using GEE can effectively support river discharge inversion. During the period of 1990-2003, the inverted discharge for the Waizhou reach showed good agreement with the observed discharge. However, after 2003, the inverted discharge significantly underestimated high-discharge conditions and overestimated low-discharge conditions, resulting in relatively low overall accuracy (R2=0.69; NSE=0.66). By contrast, the inversion results for Zhangshu, Xiajiang, and Ji’an reached a stable state from 2003 to 2019 and consistently showed high agreement with the observed discharge, with the R2 and NSE exceeding 0.90. In addition to the backwater effects induced by the downstream outlet of the Ganjiang River, channel morphological evolution was identified as a key factor contributing to the low inversion accuracy at the Waizhou reach. Influenced by illegal sand mining activities and sediment transport, the hydrological cross section at Waizhou experienced pronounced morphological changes from 1990 to 2019 while generally undergoing three stages of degradation, aggradation, and stabilization, with a maximum incision depth of 2.56 m. The inversion accuracy was significantly improved by dividing the Waizhou reach into three periods (1990—1999, 2000—2011, and 2012—2019) and recalibrating the discharge inversion models; in particular, the R2 and NSE values exceeded 0.85 for all three periods and reached 0.90 during 2012—2019.This study confirms the effectiveness of integrating multisource remote sensing data with the GEE platform for large-scale and long-term river discharge inversion. It also provides valuable technical support for hydrological monitoring, flood assessment, and water resources management in data-scarce regions.关键词：Multi-source satellite;Discharge estimation;Remote sensing technology223|727|0更新时间：2026-05-13 -
摘要：Existing federated remote sensing scene classification methods generally assume a fixed client network architecture, disregarding the significant heterogeneity in computing power, storage, and energy supply among devices such as satellites and drones. These limitations make deploying models on resource-constrained terminals difficult, restricting the practical efficiency of federated systems. Therefore, a novel federated remote sensing paradigm that is capable of dynamically adapting to the heterogeneity of client data and computational resources must be urgently developed. While safeguarding the data privacy of all parties, this paradigm should collaboratively explore optimal network architectures that are lighter, more computationally efficient, and tailored to the specific characteristics of each client’s local data. To address this bottleneck, this study proposes a federated collaborative framework with the autonomous evolution of neural network architectures. It proposes a neural architecture search mechanism based on path and data collaborative sampling to achieve lightweight, efficient, and personalized model construction that adapts to local data characteristics. During the collaborative training phase, each client dynamically screens key subnet architectures through gradient norm-guided path sampling and focuses on high-value samples through data sampling based on gradient upper bounds, significantly reducing supernetwork training and communication overhead. In the multiparty deployment phase, evolutionary algorithms are used to search for and validate the personalized subnet with the optimal verification accuracy on the basis of local data. The proposed framework’s foundational breakthrough lies in its integrated approach wherein gradient norm-guided path sampling dynamically identifies and prioritizes architecturally critical subnets during federated training. Simultaneously, gradient-capped data sampling concentrates computational resources on samples with significant effect on training outcomes. These mechanisms collectively form a synergistic strategy that substantially reduces gradient variance and training overhead across the hypernetwork while respecting client resource constraints. Following this collaborative hypernetwork development, each client autonomously executes evolutionary optimization, extracting customized subnetworks that are precisely tailored to local data distributions through adaptation-driven architecture exploration. By unifying adaptive sampling during training with evolutionary personalization while on deployment, the framework achieves unprecedented efficiency in generating lightweight but high-performance models. These models are optimized for diverse edge devices and their unique remote sensing environments while remaining within strict privacy-preserving federated parameters. Experiments on four benchmark heterogeneous remote sensing classification datasets, namely, AID, NWPU-RESISC45, PatternNet, and MEET, demonstrate that this method significantly outperforms fixed architectures and mainstream pruning methods under nonindependent and identically distributed conditions. It can evolve lightweight, high-performance, and dedicated network architectures for heterogeneous clients while protecting data privacy, effectively enhancing the deployment feasibility and overall performance of federated remote sensing systems. Ablation studies have confirmed that the integrated path-data sampling strategy is pivotal to these gains, reducing gradient variance and improving subnet consistency. This work resolves key bottlenecks in federated remote sensing systems by enabling resource-constrained clients to evolve specialized architectures autonomously. The integration of gradient-guided path sampling and data sampling optimizes training efficiency, while evolutionary optimization facilitate personalized subnet deployment. The proposed framework demonstrably enhances deployment feasibility and overall system performance without compromising data privacy, establishing an effective solution for heterogeneous federated learning in remote sensing scene classification.关键词：federated learning;remote sensing scene classification;neural architecture search76|28|0更新时间：2026-05-13 -
摘要：Large-scale 3D scene reconstruction based on remote sensing images provides critical support for smart city development, map navigation, virtual reality, and digital twin systems. Existing 3D reconstruction algorithms predominantly rely on feature matching techniques and demonstrate satisfactory performance in small-scale or structurally simple scenes. Given the intricate terrain features and noise interference in complex or large-scale environments, significant challenges, such as suboptimal reconstruction accuracy and incomplete modeling, exist. These challenges hinder the effectiveness of these methods. Therefore, this study proposes a diffusion-constrained multiview stereo network comprising a multiscale feature enhancement feature pyramid network (MFE-FPN), an adaptive feature aggregation module (AFA), and a diffusion-constrained module (DCM) to address the issues of low-feature matching accuracy, high noise in predicted depth maps, and incomplete edge reconstruction in multiview stereo for remote sensing images.The proposed method consists of several steps. First, the network takes N multiview remote sensing images as input, with the first image serving as the reference and the remaining N-1 as source images. It adopts a three-stage coarse-to-fine strategy to predict depth maps progressively. The network utilizes the MFE-FPN module to extract multiscale features from the input images, thereby generating hierarchical feature representations. Second, the top-level features from the FPN are mapped through an edge-aware network to compute edge-aware features, which are subsequently fused with the multiscale features. Third, an AFA is designed to aggregate the multiscale features, thereby forming a matching cost volume. Fourth, a diffusion constraint module is introduced to integrate cost volume features with edge-aware features. Fifth, an edge-guided transformer is employed to enhance the representation of edge details during the denoising stage. Sixth, the cost volume features are regularized and regressed to estimate depth, resulting in the final reconstructed depth map. Seventh, an edge-aware loss function is constructed during training to preserve the edge information in the predicted depth maps effectively.Experimental results show that compared with other methods, the DiffusionMVS network shows an improved mean absolute error metric on the WHU-TLC and LuoJia-MVS datasets by 28.11% and 3.37%, respectively, thereby demonstrating superior reconstruction performance. However, in terms of inference time, the proposed method does not achieve the best performance because of the relatively low operational efficiency of the diffusion constraint module. Nevertheless, it achieves an optimal balance between accuracy and efficiency, thereby making it highly suitable for remote sensing stereo reconstruction tasks. The results on the self-constructed dataset of oil and gas stations verify the model’s capability to reconstruct detailed geometric features. This capability benefits from the model’s excellent performance in edge preservation and generalization in unseen scenarios. Moreover, ablation experiment results confirm that the proposed MFE-FPN, AFA, and DCM modules can effectively enhance the accuracy of depth map reconstruction.The proposed diffusion-constrained multiview stereo network significantly improves edge-processing capability and overall reconstruction accuracy through a multiscale feature enhancement module and a diffusion constraint module. Results indicate the model is well-suited for reconstructing mountains, forests, and buildings, because of its superior performance on weak-texture regions and depth map denoising challenges. It effectively addresses the reduced reconstruction accuracy of remote sensing images under noise interference. Future work will explore incorporating the Segment Anything Model into the MVS framework to leverage its rich semantic information, thereby refining the matching process and further improving reconstruction efficiency and accuracy.关键词：remote sensing images;multi-view stereo;multi-scale feature extraction;adaptive feature aggregation;diffusion model;edge-guided transformer76|26|0更新时间：2026-05-13
Models and Methods
Remote Sensing Encyclopedia
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