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Rotation-Transform-Based Scattering Characteristics Extraction and Classification in Agricultural Areas Using Dual-Polarization SAR Data AI Introduction
Abstract：Objective In the field of polarimetric synthetic aperture radar (PolSAR) remote sensing, the capability to conduct all-weather, day-and-night observations makes it a pivotal tool for crop classification. Currently, establishing correlations between decomposition features and crop types via machine learning algorithms has become a mainstream approach. However, existing dual-polarimetric decomposition methodologies, such as Stokes or Cloude decompositions, are constrained by their reliance on predefined and fixed scattering models. These conventional approaches are limited to extracting discrete scattering features, which often fail to sufficiently characterize the geometric structures and scattering processes of complex agricultural vegetation, thereby hindering the achievement of higher classification accuracy.Method To effectively address these issues, the paper proposes a rotation transformation methodology specifically designed for dual-polarimetric data. By using the polarimetric scattering angle
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-07-07Multimodal High-Resolution Remote Sensing Image Registration via Modality Translation and 3D Spatial Relationship Constraints AI Introduction
Abstract：In complex urban environments, dense buildings and significant height variations amplify nonlinear radiometric differences and geometric distortions among multimodal remote sensing images. These challenges severely hinder high-precision registration of high-resolution imagery. Existing registration approaches predominantly rely on two-dimensional features such as texture, often neglecting the valuable three-dimensional spatial information inherent in such scenes. To address these limitations, this study proposes a novel multimodal high-resolution remote sensing image registration method that integrates modality transformation with three-dimensional spatial relationship constraints, aiming to improve robustness and accuracy in complex urban scenarios. The proposed method consists of three main components. First, a cross-modal image translation technique is employed to reduce radiometric discrepancies between multimodal images, effectively narrowing the modality gap and facilitating subsequent feature extraction. Second, monocular depth estimation is introduced to efficiently generate depth maps from single images. These depth maps provide essential spatial priors and serve as a foundation for constructing more informative feature descriptors and enforcing spatial constraints. Finally, a matching strategy based on depth-guided 3D spatial relationship constraints is developed. This strategy includes multi-feature map keypoint detection to capture potential salient features, the construction of depth-enhanced joint descriptors to improve feature distinctiveness, and the incorporation of 3D spatial relationship constraints to ensure geometrically consistent matching. Together, these steps enable reliable detection, robust description, and accurate matching of feature points across multimodal images. The proposed method was compared with several traditional and deep learning methods on four representative multimodal remote sensing datasets. Experimental results show that the proposed method achieves an average Number of Correct Matches (NCM) of 510, which improves upon existing methods by a factor of 0.92 to 5.22. The average Root Mean Square Error (RMSE) is 1.58 pixels, and the registration accuracy is improved by a factor of 4.12 to 4.78 compared to state‑of‑the‑art methods. These results demonstrate that the proposed method has clear advantages in registration accuracy and overall performance, effectively suppressing nonlinear radiometric differences and geometric distortions in urban multimodal images and achieving high‑precision registration. This study presents a robust solution for multimodal high-resolution remote sensing image registration in complex urban environments. By combining cross-modal translation, monocular depth estimation, and depth-based three-dimensional spatial relationship constraints, the proposed method successfully addresses both nonlinear radiometric differences and geometric distortions. The integration of 3D spatial information significantly improves feature matching robustness and registration precision compared to conventional 2D-based approaches. Experimental validation confirms that the method achieves superior performance in terms of both accuracy and stability, establishing a solid foundation for downstream applications such as urban mapping, change detection, and multi-sensor data fusion.Keywords：image registration;multimodal imagery;3D spatial relationships;complex urban scenes;depth maps282|113|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-06-12
Abstract：Vegetation phenology reflects the integrated responses of ecosystems to climatic conditions, land surface modifications, and anthropogenic disturbances, and serves as a critical indicator for assessing ecosystem feedbacks to urbanization and climate change. Rapid urban expansion has substantially altered surface thermal environments, vegetation structure, and ecological processes, leading to differentiated phenological responses among urban cores, towns, and surrounding rural areas. Although previous studies have documented urban–rural phenological contrasts, most analyses have relied on moderate-resolution remote sensing products, which are subject to mixed-pixel effects and limited capacity to resolve fine-scale urban heterogeneity. Systematic investigations based on high spatial resolution data across large spatial extents remain insufficient. This study aims to (1) extract high-resolution vegetation phenology metrics for major Chinese cities using Sentinel-2 imagery, (2) quantify phenological differences along the urban–town–rural gradient, and (3) examine how these differences vary across vegetation types, climatic zones, and city-size categories. This study aims to (1) extract high-resolution vegetation phenology metrics for major Chinese cities using Sentinel-2 imagery, (2) quantify phenological differences along the urban–town–rural gradient, and (3) examine how these differences vary across vegetation types, climatic zones, and city-size categories. Using Sentinel-2 imagery from the Copernicus program (2019-2024), Enhanced Vegetation Index (EVI) time series were constructed for 128 cities across China and their adjacent town and rural areas. A dynamic threshold approach was applied to extract the Start of Season (SOS) and End of Season (EOS) from annual EVI trajectories. To ensure biological plausibility, phenological metrics were constrained within reasonable day-of-year ranges. The derived phenological dates were validated against ground-based observations and compared with the moderate-resolution MCD12Q2 phenology product to assess absolute accuracy using MAE and RMSE metrics. Urban–town–rural differences were quantified for each city and subsequently analyzed across vegetation types, climatic zones, and city-size classes. Statistical comparisons and regression analyses were employed to evaluate spatial heterogeneity and scaling patterns. Validation results indicate that Sentinel-2-derived phenology exhibits substantially lower MAE and RMSE values than the MCD12Q2 product, demonstrating improved absolute accuracy under heterogeneous urban landscapes. Nationally, urban vegetation shows a consistent spring advancement and autumn delay relative to surrounding areas. On average, urban SOS occurs 1.26 days earlier than in towns and 1.49 days earlier than in rural areas, while urban EOS is delayed by 1.51 days and 1.25 days relative to towns and rural areas, respectively, indicating an extended growing season in urban environments. Phenological responses vary significantly among vegetation types. Forest ecosystems exhibit the strongest sensitivity to urbanization, showing the largest magnitude of spring advancement and autumn delay. In contrast, other vegetation types display comparatively moderate responses. Climatic background further modulates urbanization effects. The temperate climate zone shows the most pronounced urban-rural phenological contrasts, whereas subtropical and tropical zones exhibit weaker and less stable differences. City size also influences phenological patterns. The advancement of urban SOS generally intensifies with increasing city size, suggesting a scaling effect associated with enhanced urban heat island intensity. However, the delay of EOS is more evident in small and medium sized cities and may weaken or even reverse in megacities, possibly due to complex interactions among thermal stress, vegetation management, and land surface heterogeneity. Overall, this study provides high-resolution, large-scale evidence of differentiated vegetation phenological responses to urbanization in China. Urban expansion systematically modifies growing season dynamics, characterized by earlier spring onset and delayed autumn senescence, although the magnitude and direction of these effects depend on vegetation type, climatic background, and city size. By leveraging Sentinel-2 imagery and a dynamic threshold extraction framework, this research improves the quantitative reliability of urban phenology assessment compared with conventional moderate-resolution products. The findings enhance understanding of how urbanization reshapes ecosystem seasonal dynamics and contribute to clarifying the socio-ecological implications of phenological shifts. These results provide scientific support for sustainable urban planning, ecological infrastructure optimization, and climate adaptation strategies under continued urban expansion.Keywords：remote sensing;urbanization;vegetation phenology;sentinel-2;dynamic threshold method;gradient difference239|101|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-06-12
Abstract：Objective Accurate monitoring of forest resources at the individual tree level is fundamental for forest ecosystem management. Unmanned aerial vehicle (UAV) visible light (RGB) imagery provides a cost-effective and high-spatial-resolution data source for these wide-area monitoring tasks. High-spatial-resolution imagery comprehensively records the fine contours of trees and the background habitat of the forest. Utilizing panoptic segmentation technology for unified interpretation enables the synchronous extraction of all forest elements. Nevertheless, interpreting highly closed-canopy forest scenes remains a critical challenge. Traditional deep learning approaches often decouple semantic segmentation for background elements and instance segmentation for individual trees, leading to severe pixel-level classification conflicts and spatial topology inconsistencies. Furthermore, the limited spectral information in RGB imagery frequently causes severe spectral confusion among adjacent trees. To systematically address these challenges, this study proposes an end-to-end forest panoptic segmentation model named FSC-Mask2Former.Method The proposed FSC-Mask2Former builds upon the Mask2Former baseline by introducing two core architectural improvements tailored to the unstructured features of forests. First, a Frequency-domain Texture Awareness (FTA) module is incorporated into the feature extraction pathway to compensate for the loss of micro-texture details caused by spatial downsampling, essentially functioning as a learnable high-pass filter in the feature space to retain critical edge gradients. Second, an Instance-aware Query Contrastive (IQC) head is integrated at the output of the Transformer decoder to maximize the inter-class feature distance between spectrally similar tree species, imposing an anisotropic constraint on the feature distribution to enlarge decision boundaries and fundamentally suppress category assignment conflicts. To evaluate the model, a densely annotated dataset was constructed using UAV RGB imagery from Gaofeng Forest Farm in the Guangxi Zhuang Autonomous Region, supplemented by data from Genhe City in the Inner Mongolia Autonomous Region, Jixi County in Anhui Province, and Hengzhou City in the Guangxi Zhuang Autonomous Region to validate model transferability.Result Comprehensive experiments demonstrate that FSC-Mask2Former significantly outperforms existing mainstream networks. The model achieves an overall Panoptic Quality (PQ) of 57.0%, a substantial gain of 11.0 percentage points over the baseline. Most notably, the foreground Recognition Quality (RQ) reaches 56.0%, representing a 12.0 percentage point increase. Visualizations confirm that FSC-Mask2Former effectively separates touching instances in high-canopy-closure forest areas, precisely delineates boundaries for morphologically irregular canopies, and maintains the spatial coherence of background elements. Furthermore, multi-region experiments indicate robust generalization capabilities across different geographical and ecological conditions.Conclusion The proposed FSC-Mask2Former successfully overcomes the bottlenecks of spectral homogeneity and task separation in UAV-based forest interpretation. This research proves that accurate full-element forest mapping can be realized using universally accessible UAV RGB imagery, providing a practical, robust, and highly cost-effective technical paradigm for modern forest resource monitoring.Keywords：UAV remote sensing;Panoptic segmentation;Mask2Former;Frequency domain analysis;contrastive learning;Individual tree recognition165|89|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-06-12Cloud Detection of Simulated Geostationary Microwave Sounding Satellite （FengYun-4/GeoMWS） over Oceans AI Introduction
Abstract：Objective Leveraging the advantage of high-frequency observations, geostationary satellites effectively compensate for the limitations of polar-orbiting meteorological satellites in terms of long revisit cycles and insufficient timeliness in monitoring short-lived severe weather. Compared to infrared bands, microwave radiation is less affected by clouds and exhibits excellent penetration through non-precipitating clouds. Consequently, deploying microwave sounding instruments on geostationary orbits has become a crucial development direction in meteorological observation. The Fengyun-4 Geostationary Microwave Sounder (GeoMWS), currently under development in China, aims to achieve all-weather, all-day, and high-frequency three-dimensional observations of cloud and precipitation systems and their internal structures. This capability is of great significance for monitoring rapidly evolving weather systems and improving Numerical Weather Prediction (NWP). However, a prerequisite for satellite data assimilation (whether clear-sky or all-sky) is the accurate identification of clear-sky and cloudy pixels, along with the assignment of appropriate observation errors. Given that existing cloud detection algorithms are predominantly designed for polar-orbiting satellites and are difficult to directly adapt to the characteristics of geostationary microwave observations, this study aims to develop a specialized cloud detection method for GeoMWS to facilitate the effective assimilation of its data.Method This study proposes a fast and efficient cloud detection algorithm specifically tailored for the GeoMWS instrument. The method constructs a cloud detection index based on two window channels (Channel 2 and Channel 4). It fully accounts for the sensitivity differences of these channels across various latitudinal regions and introduces brightness temperature normalization to eliminate background noise caused by temperature variations. Furthermore, the optimal threshold for the cloud detection index is scientifically determined by analyzing the evolution characteristics of the index under different classification criteria.Result Evaluation results demonstrate that the proposed method achieves robust detection performance across different time periods. The Probability of Detection for clouds (POD_cld) and the Hit Rate (HR) both exceed 75%, indicating a high capability in identifying cloudy scenes. Meanwhile, the False Alarm Rate for clouds (FAR_cld) is effectively controlled below 20%. Compared to previous methods applied to this context (which maintain a detection rate of approximately 60%), the proposed method exhibits significant advantages in accuracy.Conclusion This study successfully develops a cloud detection algorithm optimized for GeoMWS. This method not only provides high-precision cloud detection information for data assimilation under both clear-sky and cloudy conditions but also offers reliable theoretical and methodological support for future operational applications. Particularly in critical areas such as typhoon monitoring and NWP data assimilation, it will significantly enhance the application effectiveness of geostationary microwave satellite data.Keywords：geostationary orbit;microwave sounding;geostationary microwave sounding;cloud detection over ocean;simulated data92|72|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-06-12
Abstract：The micro laser retroreflector array INRRI (INstrument for Landing-Roving Laser Retroreflector Investigations) is one of the international payloads onboard the Chang’e-6 mission. It receives laser signals emitted by the laser equipment onboard lunar orbiters and utilizes the characteristic of parallel reflection of incident laser on the reflective surface to achieve high-precision ranging. Through repeated observations and calculations, INRRI has become the very first absolute control point on the far side of the Moon, providing fundamental support for lunar geodesy, remote sensing mapping and positioning, high-precision orbit determination and navigation of lunar orbiters. This study presents mechanical structure and optical design of INRRI, and analyzes its key performance. In terms of design, a spherical dome structure and coating technology are adopted to enable INRRI to effectively receive the orbiter’s laser beam within a wider field of view. In terms of performance analysis, an effective reflection area model is established to analyze the reflecting signal intensity under different incident angles. The results show that the effective reflection area of a single corner cube reflector (CCR) reaches approximately 1 cm² under normal incidence, and INRRI can maintain stable effective reflection performance within a 60° half-aperture field of view. Furthermore, combined with the velocity aberration effect and far-field diffraction theory, the detectability of INRRI by the laser equipment onboard lunar orbiters is comprehensively evaluated. The dihedral angles of the CCR measured by a ZYGO interferometer are (0.37″, 0.64″, 0.37″), corresponding to a total beam deviation angle of approximately 3.01″. By establishing a far-field diffraction optical path and conducting simulation analysis, it is found that the far-field diffraction pattern exhibits spots with varying intensities distributed over an angular range of 40 μrad, thereby covering the calculated maximum velocity aberration offset angle of 11.01 μrad and satisfying the requirements of orbital observation and compensation. The observability of INRRI has been further confirmed by multiple successful detections using the Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar Reconnaissance Orbiter (LRO). This demonstrates the feasibility of employing micro laser retroreflector arrays as absolute control points on the lunar surface and provides a reference for the design optimization and future application of micro laser retroreflectors in subsequent deep-space exploration missions.Keywords：Laser retroreflector;laser ranging;effective reflection area;velocity aberration;far-field diffraction236|65|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-05-21The Influence of Micrometer-level Broadband Selection Method for Thermal Infrared Emissivity on Hyperspectral Reconstruction AI Introduction
Abstract：Objective Hyperspectral reconstruction (HSR) from multi-spectral information is an ill-posed inverse problem. At present, hyperspectral reconstruction mainly focuses on the visible and near infrared spectral bands. And there is almost no research on thermal infrared HSR, due to the absence of hyperspectral thermal infrared remote sensing data, which limits the development of thermal infrared remote sensing. Simultaneously, previous studies often utilize existing satellite multispectral band and focus on the study of HSR models, while the issues of multispectral partitioning and band selection for HSR is seldom focused. We aim to explore the ideal thermal infrared multispectral partitioning and band selection method with a certain HSR model to improve the hyperspectral reconstruction precision of thermal infrared.Method We use in-situ measured 727 hyperspectral thermal infrared emissivity samples of seven land surface cover materials，inc. asphalt roads, marble, gray ground tiles, painted surfaces, green ground tiles, slate paths, and brick-concrete pavements and so on, in this study. The broadband thermal infrared emissivity is calculated from hyperspectral thermal infrared emissivity using energy conservation law，after the broadband range is determined by the following proposed method. We proposed several broadband partitioning methods， such as，traditional equal wavelength interval method, cluster analysis method based on the correlation coefficient between temperature and emissivity and optimization method based on Quantum Genetic Algorithm (QGA) and HSR model. Simultaneously，total seven HSR model, such as non-regular multiple linear regression (MLR) and stepwise linear regression (SLR), regularized ridge regression (RR), LASSO regression, and elastic network regression (ENR), non-linear Support Vector Machine regression (SVM) and neural network regression (NNR) are introduced to compare and analyze the effects of different broad band partitioning methods on above seven HSR models.Result Non-linear HSR models and non- regularized linear HSR models have the higher error than the linear regularized HSR models for the thermal infrared emissivity HSR. LASSO and ENR models are not sensitive to broadband partitioning methods, while RR is more sensitive to broadband partitioning methods. The average error of the linear regularized RR model is the smallest, and the maximum error of ENR is the lowest for the seven land cover material. The distribution of thermal infrared broadband affect the error variation of HSR results at the wavelength direction through the changes of central wavelength and band width. For example, the QGA-SLR broadband optimization results can improve the error difference of HSR at the wavelength direction and enhance the overall performance of HSR.Conclusion The performance of a certain HSR model could be improved by the optimal broadband selection method, which changes with the changes of HSR model. Simultaneously, the ideal thermal infrared broadband not only improve the comparability of multi-source thermal infrared remote sensing products but provide technical support for the research and development of thermal infrared remote sensing sensors. The optimized combination of broadband selection methods and HSR models provides methodological support for full band HSR.Keywords：Hyperspectral Reconstruction;Thermal Infrared Emissivity;Broadband;Quantum Genetic Algorithm;Machine Learning Method149|65|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-05-21A remote sensing method for plot-level rice distribution extraction based on visual foundation models and knowledge fusion AI Introduction
Abstract：Objective Deep learning methods have demonstrated considerable potential for the fine-scale extraction of plot-level crop distributions from remote sensing imagery. However, their performance is heavily reliant on large volumes of high-quality annotated samples, leading to significant challenges, including high labeling costs, poor timeliness, and limited cross-regional generalization capability. These limitations are particularly pronounced in fragmented agricultural landscapes where plot boundaries are complex. This study aims to address these bottlenecks by proposing a novel framework that achieves accurate plot-level rice mapping without the need for region-specific model training and with minimal dependence on manual annotations. By integrating visual foundation models with agricultural prior knowledge, this research seeks to establish a robust, automated solution for crop mapping in diverse environments.Method We propose a plot segmentation prompt optimization method that couples the Segment Anything Model (SAM) with domain knowledge fusion. The core of our framework is an adaptive, iterative learning closed-loop system comprising "segmentation → screening → knowledge update → re-prompting." First, prior knowledge derived from time-series vegetation indices—specifically the Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI)—is applied to mask out non-vegetated areas, substantially reducing the computational load. The SAM is then employed for initial segmentation. Crucially, the framework performs statistical analysis on high-confidence segmented plots to dynamically update the thresholds of prior knowledge (e.g., spectral characteristics during flooding and peak growth stages, and geometric area thresholds). These updated thresholds are automatically translated into refined prompt information, including plot center points and boundary points, which are fed back into the SAM model to guide finer segmentation. Furthermore, we innovatively introduce the Intersection over Union (IoU) metric and area change rate as convergence criteria to automatically terminate the iterations, ensuring both algorithmic stability and computational efficiency.Result The proposed method was systematically validated across three geographically and agronomically diverse rice cultivation regions: Ninghe District (Tianjin, China), Fujin City (Heilongjiang, China), and Niigata City (Japan). The results demonstrated high mapping accuracy and strong generalization capabilities. In Ninghe, the method achieved an Overall Accuracy (OA) of 94.44%, a Kappa coefficient of 0.89, and an F1-score of 94.90%. Similarly, high performance was observed in Fujin (OA: 96.80%, Kappa: 0.91) and Niigata (OA: 94.50%, Kappa: 0.86). Comparative analysis of multi-temporal imagery identified the rice harvest period as the optimal phenological window for extraction due to maximized spectral and textural contrast. Ablation studies revealed that the iterative mechanism significantly improved the Kappa coefficient from 0.79 to 0.89 compared to the baseline. Moreover, in a direct comparison with typical supervised learning models (U-Net and DeepLabV3+) trained on local samples, our method achieved superior extraction accuracy and boundary completeness despite using no training samples whatsoever, proving its robustness against sample scarcity.Conclusion This study presents an effective framework for high-precision, plot-level rice mapping that significantly reduces dependency on annotated samples. By successfully coupling the powerful, generic segmentation capability of SAM with agricultural remote sensing prior knowledge through an adaptive iterative mechanism, the method overcomes the "sample dependence" bottleneck of traditional deep learning. It offers a promising technical pathway for large-scale, cost-effective, and automated crop mapping, providing essential data support for food security assessment and sustainable precision agricultural management.Keywords：Remote sensing fine extraction;SAM;prior knowledge fusion;rice mapping;adaptive statistical learning;parcel-level segmentation;iterative optimization;prompt information optimization344|377|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-04-21Global-scale comparison of different fusion algorithms of active and passive microwave soil moisture products AI Introduction
Abstract：(Objective)Soil moisture is a key regulatory factor in the energy exchange between multiple layers of the Earth system. Active and passive microwave signals provide complementary information due to their different responses to soil moisture, and integrating these two types of signals to generate high-accuracy soil moisture products with excellent spatiotemporal coverage is a current research focus and challenge. Mathematical error measurement approaches have shown great potential in this integration process, as they can derive product error metrics relative to true values under certain assumptions. However, limited research has compared the performance and discrepancies of these error measurement methods when combined with different fusion algorithms in terms of fusion product accuracy. This study aims to address this research gap.(Method)Three mathematical error measurement methods, namely the Extended Triple Collocation (ETC) method, Double Instrumental Variable (IVd) method, and Three-Cornered Hat (TCH) method, were employed to evaluate the passive microwave-based SMAP and active microwave-based ASCAT soil moisture products on a global scale, and their spatial distribution patterns of accuracy were revealed. Subsequently, three main fusion algorithms (the minimum random error variance method, the maximum correlation coefficient method, and the maximum signal-to-noise ratio method) were used to generate soil moisture fusion products. The impact of different mathematical error measurement methods on the performance of fusion algorithms was systematically analyzed.(Result)The results show that: 1) The spatiotemporal coverage of all fusion products is significantly enhanced. Among them, the combination of the TCH method and the minimum random error variance method outperforms the original single active and passive products in all error metrics (including unbiased root mean square error, root mean square error, and bias) except for the correlation coefficient (R); 2) Most fusion products exhibit superior performance compared to the ASCAT product and comparable accuracy to the SMAP product; 3) Except for the maximum signal-to-noise ratio method based on IVd, the fusion methods based on TCH are generally superior to those based on ETC and IVd.(Conclusion)This study clarifies the impact of different mathematical error metrics on the performance of soil moisture fusion products, providing a theoretical basis and methodological support for the fusion of active and passive microwave soil moisture products.Keywords：soil moisture;fusion algorithm;assessment method;microwave remote sensing;global scale;the minimum random error variance method;the maximum correlation coefficient method;the maximum signal-to-noise ratio method356|239|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-26Synergistic use of satellite and UAV imagery for continuous monitoring of field-scale paddy rice NDVI AI Introduction
Abstract：Objective The real-time and precise monitoring of crop growth status at the field scale is a critical component for achieving modern precision agriculture. Multi-source remote sensing technologies, including satellite platforms and unmanned aerial vehicles (UAVs), have emerged as effective non-destructive tools for this purpose. However, these data sources present a significant spatiotemporal trade-off, limiting their independent utility. Satellite remote sensing, while offering broad-area coverage, is often constrained by adverse weather conditions and insufficient spatial resolution to capture in-field variability. Conversely, UAV remote sensing provides exceptionally high spatial resolution but is hampered by limited battery endurance, making large-scale continuous (e.g., daily) monitoring challenging. Consequently, a single data source is inadequate for supporting the continuous monitoring of crop growth at the field scale. To address this critical gap, this study proposes a cross-platform spatiotemporal fusion method. The objective is to synergistically integrate satellite and UAV data, effectively leveraging their complementary temporal and spatial resolutions to generate a continuous, high-resolution dataset for precision agriculture.Method This research was based on a synergistic combination of multi-platform, multispectral remote sensing data, including high-resolution UAV imagery, Sentinel-2 data and PlanetScope SuperDove data. We developed an improved CACAO (Consistent Adjustment of the Climatology to Actual Observations) algorithm, adapting its core logic for cross-platform data fusion rather than its original climatological application. Two distinct data combination strategies were designed and tested: (1) a baseline “UAV+Sentinel-2” strategy and (2) an enhanced “SuperDove+Sentinel-2+UAV” strategy, which integrates high-frequency commercial satellite data. The CACAO framework was implemented using two distinct modes: a “forward prediction” (FP) mode, designed for near real-time applications, and a “backward updating” (BU) mode, which iteratively refines historical estimates as new data becomes available. The final output of the framework is a near real-time, daily 1-meter resolution normalized difference vegetation index (NDVI) time-series dataset. The accuracy of the fusion results was rigorously evaluated using two methods: (1) Leave-One-Out Cross-Validation (LOOCV), which assesses the model’s predictive power, and (2) a benchmark comparison against the established GLM-STF (Generalized Linear Model-based Spatiotemporal Fusion) algorithm.Result The prerequisite for data fusion was confirmed as NDVI data from the different platforms exhibited good consistency, with a strong correlation between Sentinel-2 and SuperDove (R = 0.97) and a reliable correlation was observed between UAV and satellite data (R > 0.75). In addition, the CACAO algorithm was proven to effectively reconstruct the phenological dynamics of the rice crop. A key finding was that the backward updating (BU) mode produced a significantly smoother and more robust NDVI time series than the forward prediction (FP) mode. Both CACAO-based data combination strategies achieved high overall accuracy (R > 0.94). Critically, the study demonstrated that introducing high-temporal-resolution SuperDove data during key phenological stages can substantially improve accuracy, with the correlation increasing from 0.51 to 0.67 in a specific validation case. Finally, in the comparative analysis, the CACAO algorithm demonstrated greater stability and slightly higher accuracy than the GLM-STF algorithm, particularly showing more robust performance across the entire growing season.Conclusion In conclusion, the cross-platform fusion framework proposed in this study, centered on the improved CACAO algorithm, is an effective and robust solution for generating continuous, high-precision (daily 1-meter) field-scale rice NDVI time series. This approach successfully overcomes the limitations of single-source data platforms. The framework provides strong technical support for the fine-grained monitoring of crop growth and the implementation of precision management strategies in modern agriculture.Keywords：PlanetScope;Sentinel-2;spatiotemporal data fusion;growth monitor;precision agriculture;field scale;phenological curve;near real-time monitoring788|969|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-17Remote sensing inversion of particulate organic carbon in inland waters based on biochemical–optical mechanisms AI Introduction
Abstract：Particulate Organic Carbon (POC) is a critical component of the inland water carbon pool and plays a pivotal role in carbon transport, biogeochemical transformation, mineralization, and greenhouse gas emissions. Quantifying both the concentration and the source composition of POC is essential for understanding inland water carbon cycling and improving regional carbon budget assessments. However, the coexistence of endogenous and terrestrial sources creates substantial variability in optical and biochemical properties, limiting the accuracy and generalizability of existing remote sensing algorithms. This study aims to apply an approach that integrates biochemical source–tracing methods with satellite remote sensing to accurately estimate POC concentration and source composition across diverse aquatic environments. A biochemical end–member mixing model was first applied to water samples collected from the Yangtze River mainstem and Lake Taihu to quantify the contribution of endogenous and terrestrial POC. Based on these source apportionments, a three–band remote sensing reflectance ratio was identified as an effective optical indicator for resolving the proportion of endogenous POC. This ratio was used to classify water bodies into two categories characterized by dominant endogenous or terrestrial POC sources. Building upon this classification, a semi–analytical algorithm using inherent optical properties (IOPs) of the water column was applied to estimate POC concentration. Specifically, the particle absorption coefficient of pigments, aph(674), was used as a proxy for endogenous POC concentration, while the non–algal particle absorption coefficient, anap(443), was used to infer terrestrial POC concentration. Total POC concentration was then derived by integrating the two source–specific estimates according to their fractional contributions. The three–band remote sensing reflectance ratio demonstrated strong performance in estimating the proportion of endogenous POC in the water column, yielding an RMSE of 0.081, an MB of 0.0131, and a MAPE of 43.479%. Endogenous POC concentration estimated using aph(674) achieved an RMSE of 1.000 mg/L, an MB of -0.157 mg/L, and a MAPE of 24.455%. Similarly, terrestrial POC concentration predicted from anap(443) showed an RMSE of 0.346 mg/L, an MB of -0.012 mg/L, and a MAPE of 22.200%. When combined, the overall POC algorithm outperformed existing empirical and semi–analytical models, exhibiting an RMSE of 1.322 mg/L, an MB of -0.177 mg/L, and a MAPE of 29.380%. These results confirm that integrating optical classification with source–specific modeling significantly improves the robustness and generalizability of POC retrievals across heterogeneous inland waters. This study demonstrates that coupling remote sensing techniques with biochemical source–tracing provides a powerful framework for quantifying both POC concentration and source composition at broad spatial and temporal scales. The approach effectively leverages the mechanistic insights offered by biochemical methods and the large–scale observational capability of satellite remote sensing. The resulting algorithm enhances the interpretability and transferability of POC retrievals, offering a promising tool for advancing carbon pool monitoring in inland waters. However, the algorithm has not yet been validated beyond the Yangtze River mainstem and Lake Taihu regions. Future work will focus on acquiring additional datasets from diverse hydrological and optical environments to further refine model performance and assess its broader applicability.Keywords：inland waters;particulate organic carbon;isotopic tracing;n–alkanes;semi–analytical model255|219|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-13Unsupervised Automated Remote Sensing Monitoring of Crop Lodging for the Entire Pre- and Post-Disaster Process AI Introduction
Abstract：Objective Crop lodging poses a significant threat to agricultural productivity and food security, yet existing monitoring approaches often suffer from low automation, insufficient integration of pre- and post-disaster data, and a lack of systematic spatiotemporal coordination. To overcome these limitations, we developed an automated framework, StandardCurve-iForest-RF, which aims to establish a resilient crop growth baseline, distinguish true lodging from noise, and enable precise spatiotemporal mapping for disaster management.Method The approach utilizes time-series Sentinel-2 satellite data to construct a Crop Growth Standard Curve (CGSC) for the target crop. The Soft Dynamic Time Warping (Soft-DTW) algorithm is employed to create this curve, which serves as a resilient reference model capable of accommodating inter-annual climatic variations. To detect lodging, the method calculates cumulative multi-feature anomaly scores by comparing post-disaster satellite observations against the pre-established standard curve. An Isolation Forest (iForest) algorithm is applied for initial anomaly detection across multiple spectral features. Subsequently, a spatiotemporal joint decision mechanism is implemented to refine the results, effectively suppressing false alarms caused by persistent cloud cover, cloud shadows, and other environmental noise. Finally, a Random Forest (RF) classifier is used to accurately map the spatial extent and precise boundaries of the lodged areas, completing the fully automated workflow from dynamic detection to precise mapping.Result The method was validated using a case study of a lodging event that occurred on September 15, 2020, in Bohetai Township, Zhaoyuan County, Daqing City, Heilongjiang Province. It successfully identified the lodging event, demonstrating its effectiveness in distinguishing actual crop damage from noise. The overall detection accuracy reached 80.36%, with a Kappa coefficient of 0.60, confirming a substantial agreement between the automated detection results and ground reference data. The results clearly showed that the integrated use of the standard curve, the anomaly scoring mechanism, and the spatiotemporal decision rules significantly enhanced the reliability of lodging identification and minimized false positives. The entire process, from data processing to the final generation of the lodging map, was executed automatically without manual intervention.Conclusion The StandardCurve-iForest-RF framework presents a significant advancement in automated crop disaster monitoring. Its core innovation lies in the construction of a resilient growth standard curve and a sophisticated spatiotemporal analysis pipeline that effectively differentiates true lodging from interference. The successful application in a real-world case study confirms the method's practical utility and accuracy. This framework provides a valuable tool for agricultural departments and emergency management agencies, enabling rapid assessment of crop damage extent and supporting timely disaster response and loss estimation. The methodology is adaptable and holds promise for application in other regions and for monitoring other types of abrupt agricultural disasters.Keywords：Crop Lodging Monitoring;Standard Growth Curve;time-series analysis;Spatiotemporal Joint Detection;Sentinel-2284|887|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-13Remote sensing inversion of forest aboveground carbon storage based on GAC feature selection and AST regression algorithm AI Introduction
Abstract：Objective Accurately estimating the aboveground carbon stock of forests is crucial for forest management and the sustainable development of forest ecosystems. Since single machine learning models still face issues such as weak generalization, low estimation accuracy, and high uncertainty when estimating forest carbon stocks, this study aims to explore new hybrid models to improve the efficiency of model building and prediction accuracy, thereby enhancing the accuracy of aboveground forest carbon stock estimation.Methods Taking Forest Resources Class II Survey data and Landsat 8 OLI imagery as data sources, a feature selection method integrating Genetic Algorithm (GA) and CatBoost (Genetic Algorithm and CatBoost, GAC) was proposed, combining GA's global optimization capability in feature subset exploration with CatBoost's prominent strengths in mining nonlinear feature relationships and quantitatively evaluating feature importance; on this basis, GAC was systematically compared with the traditional Recursive Feature Elimination (RFE) method to screen remote sensing feature variables that effectively improve model accuracy, while the Hyperopt hyperparameter optimization algorithm was adopted to iteratively search the hyperparameter space of each machine learning regression model to obtain the optimal parameter combinations. Subsequently, a stacked ensemble AST regression algorithm based on base learner mean fusion and meta-learner-based adaptive weighting was constructed, which selects four optimized single machine learning models—Adaptive Boosting (AdaBoost), CatBoost, Random Forest Regression (RFR), and Light Gradient Boosting Machine (LightGBM)—as base learners to fully leverage the unique advantages of each model and significantly enhance the overall performance of the algorithm. Finally, remote sensing estimation models for carbon stock were established based on six single machine learning regression models (AdaBoost, CatBoost, RFR, LightGBM, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)) as well as the AST ensemble model, and after comprehensively comparing the estimation accuracies of all candidate models, the optimal one was selected to conduct high-precision inversion mapping of Pinus densata carbon stock in Shangri-La City.Results 1) RFE selected 9 variables, while GAC selected 7 variables, among which the 7 variables selected by GAC contributed more to the accuracy of Pinus densata AGC inversion; 2) Based on Hyperopt, the hyperparameters of each model were iteratively optimized, and it was found that the optimal feature subset selected by GAC, when combined with the AST algorithm for regression fitting, achieved the best estimation accuracy, with a coefficient of determination R² = 0.885, a root mean square error RMSE = 8.321 t/hm², and a prediction accuracy P = 86.4%; 3) Based on the optimal estimation model, the aboveground carbon stock of Pinus densata in Shangri-La City in 2016 was estimated to be 7.70953 million t, with an average carbon density of 40.015 t/hm²; 4) The directionality of texture features has a significant impact on the estimation accuracy of forest carbon storage by the AST model, and the 45° diagonal direction is the optimal direction for carbon storage estimation under this model.Conclusion The AST algorithm exhibits higher stability and anti-interference ability under multiple cross-validations, which effectively improves the estimation accuracy of nonparametric models and thereby reduces model uncertainty. This method can provide an effective reference for the dynamic monitoring of forest resources in other high-altitude areas.Keywords：Carbon stocks;Hyperopt hyperparameter tuning;machine learning;AST;Pinus densata;GAC;remote sensing inversion;uncertainty analysis302|645|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-13Weakly Supervised Semantic Change Detection in Street View Imagery and Its Application in Urban Renewal Dynamics Mapping AI Introduction
Abstract：Objective Street view imagery (SVI) has emerged as an important geospatial big data source for perceiving the urban built environment. Accurately detecting facade-level changes and identifying their semantic categories is essential for monitoring urban renewal dynamics. However, existing change detection approaches struggle to separate temporal ownership of changed objects (change decomposition) and to directly provide semantic change information, leading to complex workflows and high data preparation costs. This study aims to develop a weakly supervised semantic change detection framework that integrates change decomposition and semantic labeling, and to apply it to dynamic mapping of urban renewal in Guangzhou, China.Method We propose Cross-C2PO, a novel dual-branch architecture designed to achieve end-to-end weakly supervised change decomposition. Unlike traditional single-branch models, Cross-C2PO introduces a cross-comparison mechanism to explicitly model asymmetric temporal differences, ensuring completeness and consistency regardless of input order. The model integrates a differentiable union operator to maintain consistency constraints during weak supervision and employs the proposed Cross-MTF feature fusion function to break commutativity for accurate temporal differentiation. Building on Cross-C2PO outputs, we design a semantic change detection workflow that leverages state-of-the-art segmentation models (e.g., DeepLabV3+) without requiring synthetic datasets. Finally, we introduce an urban renewal dynamic index to quantify facade-level changes and visualize renewal patterns across panoramic and directional views (front, back, left, right) for Guangzhou’s central districts (2013–2019), based on 11,431 pairs of Baidu street view panoramas.Result Traditional change detection methods fail to achieve end-to-end change decomposition and rely on complex multi-stage pipelines, limiting scalability and flexibility. In contrast, the proposed Cross-C2PO framework enables one-stage weakly supervised change decomposition and can seamlessly integrate with mainstream architectures, granting them both improved detection accuracy and the ability to perform temporal ownership splitting without additional labels. Experiments on multiple benchmark datasets demonstrate that our method consistently achieves state-of-the-art performance, outperforming existing approaches in both binary change detection and decomposition tasks. Ablation studies further validate the contribution of the cross-branch structure, Cross-MTF fusion, and the differentiable union operator. Applied to Guangzhou street view imagery, the workflow successfully produced urban renewal dynamic maps, revealing high-intensity updates clustered in Liwan and Baiyun industrial areas, while moderate changes dominate residential zones. Directional view analysis additionally highlights local disparities and micro-scale renewal patterns.Conclusion The proposed Cross-C2PO framework offers a simple yet effective solution for weakly supervised semantic change detection, enabling accurate change decomposition without additional synthetic labels. Combined with an interpretable urban renewal dynamic index, it provides a scalable and cost-effective approach for urban facade change analysis. This study bridges street view imagery and AI-based computer vision for urban analytics, offering new insights into spatiotemporal renewal dynamics. Future work will focus on optimizing computational efficiency and extending the method to multi-source data integration for large-scale applications.Keywords：urban renewal;street view imagery;semantic change detection;scene change detection;weak supervision;dynamic index467|776|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-06GF-1 Wide-Field Multi-temporal Water Body Extraction Dataset AI Introduction
Abstract：Objective This study aims to overcome the limitations of existing public water body datasets, such as single temporal resolution and low annotation accuracy. The objective is to construct a high-quality, multi-temporal lake extraction dataset based on the high-resolution wide-field multispectral imagery from the "GF-1" satellite, which offers improved temporal and spatial coverage of water bodies.Method To achieve this, three study areas with varying levels of dynamic change were selected: Poyang Lake (high dynamic change), Namtso Lake (moderate dynamic change), and Yangcheng Lake (low dynamic change). These areas were covered in four seasons of 2022. The GF-1 wide-field multispectral imagery underwent preprocessing, including radiometric correction, orthorectification, and quick atmospheric correction. For the annotation process, a hybrid strategy combining automated methods with manual visual interpretation was employed to ensure high annotation accuracy.Result The resulting dataset is characterized by multi-temporal data and high annotation accuracy, offering a significant improvement over existing datasets. The overall accuracy of the dataset for all three study areas and across all four seasons exceeded 94%.It provides reliable data for dynamic water body mapping and change monitoring across seasonal variations. Additionally, various water body extraction methods, including threshold segmentation, traditional machine learning algorithms, and deep learning techniques, were employed to validate the dataset’s practical utility. The results demonstrated that the dataset supports the effective training and evaluation of these methods.Conclusion The findings indicate that the constructed multi-temporal lake extraction dataset is highly reliable and can effectively support various water body extraction methods. It provides a robust data foundation for enhancing the performance of dynamic water body extraction algorithms, and contributes valuable data for research in dynamic water body monitoring and mapping using high-resolution remote sensing imagery.Keywords：Gaofen-1;Dynamic Water Body;water body extraction;dataset;feature extraction507|776|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-12-11
Abstract：Objective Quantitative analysis of the coupling coordination relationship between the ecological environment and socioeconomic development in China's poverty-alleviated counties, along with the identification of influencing factors, is of great significance for summarizing poverty alleviation experiences and consolidating the achievements of poverty eradication efforts.Method Based on long-term and high-resolution remote sensing datasets, this study constructed a comprehensive evaluation index system for both the ecological environment and socioeconomic development across 832 poverty-alleviated counties in China. It quantitatively assessed the development levels and coupling coordination status of these two dimensions in 2010, 2015, and 2020. Furthermore, a Boosted Regression Tree model was employed to identify the contribution rates of various indicators to the regional coupling coordination development.Result The results indicated that, overall, the ecological environment in poverty-alleviated counties exhibited a higher level of comprehensive development compared with socioeconomic factors. However, the socioeconomic development progressed at a faster pace than ecological improvements. In addition, the average annual growth rates of both dimensions from 2015 to 2020 were higher than those from 2010 to 2015. Spatially, the coupling coordination degree was the highest in northeastern counties and the lowest in the northwest, showing a distribution pattern of “high in the east and low in the west” and a trend of progressive improvement from coastal to inland areas. Most counties were categorized as “economically lagging”. Among all indicators, population size, gross domestic product, and nighttime light intensity made particularly significant contributions to the coupling coordination.Conclusion Drawing from the poverty alleviation paths and practical experiences of typical regions, the study concludes that implementing context-specific industrial poverty alleviation strategies is crucial for accelerating socioeconomic development in poverty-alleviated counties. Establishing diversified, locally distinctive industries is identified as a key approach for consolidating poverty alleviation achievements and preventing a return to poverty.Keywords：poverty-alleviated counties;ecological environment;socioeconomic development;Remote sensing dataset;coupling coordination;boosted regression tree model;contribution rate;spatial distribution pattern;industrial poverty alleviation367|342|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-11-04Theory and method of ground observation on satellite pixel scales-taking numerical land surface parameters as an example AI Introduction
Abstract：(Objective)Ground observation is the foundation of remote sensing scientific research, providing important data support for the construction of quantitative remote sensing models, accurate and efficient inversion of remote sensing information, and validation of remote sensing products. In particular, with the entrance of era of artificial intelligence, ground observation has been combined with satellite data to drive deep learning models, generating remarkable research results in the field of remote sensing. However, with the combination of satellite data with ground observations, uncertainty is unavoidably introduced to the subsequent results and analysis. This is resulted from the representativeness errors of ground observation partly due to the scale differences between ground observations and satellite pixels and partly due to the complex spatial heterogeneity land surface itself. Ground observation only represents the true value of the measured object at the observation time and in the space it represents, but cannot be directly used as the true value at the scale of satellite pixels.(Method)How to improve the spatiotemporal representativeness of ground observations on satellite pixel scales and obtain the closest representation of reality has alway been the key issue in the field of remote sensing experiments. The acquisition of pixel scale ground truth involves the selection of sample areas, evaluation of spatial heterogeneity, optimization of ground sample layout, ground observation, and scale conversion. Although a large amount of research has been carried out for each aspect, there are still cases of conceptual ambiguity and insufficient understanding in each link, resulting in significant uncertainty in obtaining pixel scale ground truth. How to constrain and control the uncertainty of the pixel scale ground truth in the acquisition process and how to obtain the pixel scale ground “truth” with minimum uncertainty is currently a bottleneck problem that urgently needs to be solved. This article discusses the current challenges and possible solutions in obtaining pixel scale ground “truth”, aiming to provide new insights and theoretical guidance for remote sensing field observation experiments.(Result)Large spatial heterogeneity does not necessarily mean poor spatial representativeness of ground observations. Because representativeness error is not only related to spatial heterogeneity, but also to factors such as the number, location, and observation scale of ground stations. Spatial heterogeneity is the dominant factor affecting the representativeness error of ground observations without optimizing sampling. But it is almost unrelated to spatial representativeness error when the sampling was optimized. It is noteworthy that spatial heterogeneity show strong dependence on spatial scales. At a smaller scale, spatial heterogeneity caused by random factors cannot be ignored. As the sub-pixel scale increases, spatial heterogeneity is mainly influenced by structural factors. The influence of geolocation mismatch needs to be fully considered, whose effect can be eliminate by developing the methods to identify the exact spatial extent of validation pixel.(Conclusion)High-quality ground observation data and effective scale conversion methods are essential prerequisites for obtaining high-quality ground "truth" at the pixel scale. However, there is still a lack of high-precision scale conversion methods, especially for complex terrains such as mountainous regions. In terms of ground observations, it is not only necessary to establish a high-quality observation network but also to ensure effective collaboration among different networks, instruments, observation techniques, and data managers to construct a ground observation dataset with a unified quality standard. In terms of scale conversion, there is a need to develop more universal and accurate scale conversion models, aiming to fully utilize ground observation data globally to construct high-quality remote sensing pixel "truth" datasets.Keywords：Satellite pixel scale ground truth;ground observation;scale difference;spatial heterogeneity;representativeness errors;uncertainty analysis;validation;accuracy assessment;training sample480|991|1<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-07-21Estimation and Spatiotemporal Evolution Analysis of Annual 500-m Anthropogenic Heat Flux in the Guanzhong Plain Urban Agglomeration AI Introduction
Abstract：Anthropogenic Heat Flux (AHF) refers to the total amount of human-generated heat emissions per unit area within a unit of time. As a key factor in the formation of the urban heat island (UHI) effect, anthropogenic heat emissions significantly influence urban thermal environments by directly releasing waste heat into the atmosphere through human activities. Therefore, studying the spatiotemporal distribution of AHF helps to understand the formation and evolutyion of urban thermal environments, and holds important theoretical and practical significance for mitigating and regulating urban ecological issues.In order to obtain the spatial distribution of anthropogenic heat emissions under limited sample data at a regional scale and to analyze the spatiotemporal evolution characteristics of different types of AHFs in the Guanzhong Plain urban agglomeration in China, this study first employed a modified emission inventory method to estimate the AHFs of prefecture-level cities. The modification primarily addresses the overestimation of residential building heat emissions by excluding private vehicle energy consumption, which is already accounted for in residential energy use. Additionally, it refines the calculation of transportation AHF by incorporating heat emissions from public transportation. Subsequently, using multi-source spatial data, including point of interest (POI) data, nighttime light data, building height data from the Global Human Settlement Layer (GHSL), and population distribution data from WorldPop, a multivariate linear regression model was constructed to estimate different types of anthropogenic heat emissions. This approach enabled the acquisition of annual anthropogenic heat emission data from 2016 to 2021 for the Guanzhong Plain urban agglomeration at a 500-meter spatial resolution, followed by a spatiotemporal analysis of emission characteristics.The study results show that (1) Multivariate linear regression is highly feasible for AHF gridding, as the fitted models achieve high accuracy, with R2 values all exceeding 0.9. Among them, the building AHF model has the highest accuracy, with an R2 of 0.98. (2) POI data contributes significantly to the gridded allocation of anthropogenic heat, effectively reflecting the spatial heterogeneity of different types of anthropogenic heat emissions. This makes it an important data source for estimating the spatial distribution of AHF from various heat sources. (3) The spatial distribution of AHF in the Guanzhong Plain urban agglomeration is uneven, with high-value areas concentrated in economically developed, flat, and highly urbanized central regions of the urban agglomeration, particularly in the northern central urban area of Xi’an. Temporally, AHF exhibits an overall upward trend.Keywords：Anthropogenic heat;point of interest (POI) data;nighttime light data;multi-source spatial data;human activities;multiple linear regression;inventory method;Guanzhong plain urban agglomeration;human activities625|1047|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-07-21Correlation analysis of the sea-surface features under eddy modulation in the global ocean using remote sensing data AI Introduction
Abstract：Mesoscale eddies are broadly distributed in the global ocean and have significant effects on the sea surface temperature (SST), sea surface height (SSH), chlorophyll (Chl), wind speed (WS), and other ocean parameters. Therefore, the coupling analysis of mesoscale eddies and ocean key parameters is an important part of ocean research. With the realization of individual eddy identification technology, the anti-cyclonic eddy (AE), cyclonic eddy (CE), and outside eddy (OE) can be better distinguished. This enables us to comprehensively study the distribution and difference of correlation of sea surface features by comparing OE with AE and CE, which provides a theoretical basis for further clarifying the modulation mechanism of the mesoscale eddy on the air-sea interface and improving ocean numerical simulation. On the basis of distinguishing AE, CE, and OE, this study calculated the Pearson correlation coefficient using sea surface temperature anomaly (SSTA), sea level anomaly (SLA), chlorophyll anomaly (CHLA), and wind speed abnormal (WSA) data from 2010 to 2019, and compared the smoothness of the correlation coefficient. The results show that the correlation distribution among the parameters has significant regional characteristics. In CE and AE, the correlation is ±0.5 in most areas of the ocean, and ±0.7 in the Northern Indian Ocean and the Equatorial Pacific. In OE, the correlation is ±0.2 in most regions, and ±0.4 in the Northern Indian Ocean and the Equatorial Pacific. In addition, when a positive (negative) correlation occurs in OE, it generally shows a large range of positive (negative) correlation, and there is a noticeable transition region between the two. However, under the influence of eddy, the extreme correlation regions are smaller and mainly present as scattered points, and the transition regions between positive and negative regions are very narrow. In terms of smoothness, the smoothness coefficient of the correlation coefficient of each parameter in OE is about 15, while the smoothness coefficient of AE and CE is about 450, which is much lower than that in OE area. We conclude that the influence of eddy on the correlation of each parameter is mainly reflected in the value, distribution and smoothness of the correlation coefficient. The correlation coefficient of each parameter in OE is about 0.2 lower than that in CE and AE, and the smoothness of the correlation coefficient in OE is about 30 times of that in AE and CE. The distribution of the correlation also has a strong point feature in the original distribution mode due to the modulation of the eddy.Keywords：Remote sensing observation;mesoscale eddy;coupling analysis;sea surface temperature;sea surface height;Chlorophyll concentration;wind speed211|902|1<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-06-17
Abstract：Objective：Land surface temperature (LST) is a key parameter in the physical process of surface radiant energy balance and water cycle. Obtaining LST data accurately and timely, and mastering its temporal and spatial changes are of great significance to climate change research. Thermal infrared(TIR) measurements are limited in practical applications due to cloud cover and other effects. Passive microwave (PMW) remote sensing measurements can penetrate clouds and are less affected by atmospheric interference, which has the advantage of obtaining all-weather surface radiation information. Among them, the microwave remote sensing data Advanced Microwave Scanning Radiometer for EOS(AMSR-E) can obtain all-weather LST, which can be used as a supplement to the missing LST information in thermal infrared (TIR) products under cloudy conditions. However, the AMSR-E data has the problem of lack of information due to the satellite orbit scanning gap of its own sensor, which causes the obtained AMSR-E LST data to be greatly restricted in practical applications. Therefore, it is necessary to propose an effective method for solving the problem. Method：Based on the superiority of deep learning in solving non-linear problems and the high dynamic variability of LST, this paper proposes a multi-temporal feature-connected convolutional neural network (MTFC-CNN) which uses specific input combinations of multi-temporal information and spatial fusion units. The network structure is based on the characteristics of the temporal and spatial distribution of missing track gaps in AMSR-E LST data and the reconstruction of missing LST values is carried out from the timing information. Result：In the simulation experiment, the 2010 annual data was divided into 8 data subsets in four seasons, day and night. The average root mean square error of the reconstructed LST value is about 1.0k and the coefficient of determination R2 is above 0.88. Compared with the other two methods: spline interpolation (Spline) and time multiple linear regression (Regress), the reconstruction effect of MTFC-CNN method performs better regardless of seasons or day and night, which proves that MTFC-CNN is better than Spline and Regress methods at mining the characteristics of temporal and spatial changes in LST. In real experiments, through comparison with MODIS LST products, the LST value reconstructed at the missing area is basically consistent with it at other areas in temporal and spatial distribution. The reconstruction results show that the LST in mainland China region shows a gradual increasing trend from January to July, and a gradual decreasing trend from August to December. Which is basically consistent with the temperature changes in the four seasons. The change of LST during the day is more significant than that at night. In summer, the temperature in Northwest China is significantly higher than that in other regions. In winter, the temperature in Northeast China is generally lower than that in other regions. At night, the difference between summer and winter is more obvious. The difference in LST changes at night in autumn is relatively close. Conclusion：The experimental results show that the MTFC-CNN method proposed in this paper mines the spatio-temporal variation information of LSTs more effectively than two traditional methods, and achieves better results in reconstructing the orbital gap ah-missing of AMSR-E LST data. It provides the possibility for the reconstruction of missing information from TIR LST data under the cloud. Keywords: Land surface temperature; AMSR-E LST; Reconstruction; Deep learning; MTFC-CNN;Keywords：land surface temperature;AMSR-E LST;reconstruction;deep learning;MTFC-CNN2781|1044|0<HTML><L-PDF><Meta-XML>Updated：2022-03-09
