最新刊期
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摘要：Objective Fine-grained ship detection in optical remote sensing imagery is an important research topic in maritime monitoring and ocean observation. Compared with coarse-grained ship detection, fine-grained ship detection aims to distinguish specific ship types with highly similar visual appearances, which places higher demands on dataset scale, category diversity, and annotation accuracy. However, existing ship detection datasets are generally limited in the number of fine-grained categories, instance scale, and scene diversity, which restricts the performance and generalization ability of deep learning–based oriented object detection models. The objective of this study is to construct a large-scale fine-grained ship detection dataset and to further expand data diversity through controllable synthetic data generation, thereby providing a reliable data foundation and benchmark for fine-grained ship detection in optical remote sensing imagery.Method First, a large-scale fine-grained ship detection dataset, named LAFI, is constructed using high-resolution optical remote sensing images collected from 36 representative ports worldwide. The dataset contains 8,000 images acquired under diverse imaging conditions and complex maritime environments. A total of 49 fine-grained ship categories are defined, and 48,717 ship instances are manually annotated using oriented bounding boxes to accurately describe ship orientation and geometric characteristics. Second, to alleviate the limitations of real data in terms of scale and scene coverage, a controllable diffusion-based data generation framework is designed to extend LAFI into a million-scale synthetic dataset, referred to as LAFI-Diffusion. By incorporating structured textual prompts that describe scene types, weather conditions, and temporal information, the diffusion model is guided to generate realistic ship images under diverse maritime scenarios. The generated synthetic samples are further filtered and combined with real data to form large-scale training sets suitable for fine-grained ship detection. Finally, several representative oriented object detection methods are selected and evaluated on the constructed datasets to analyze the effectiveness of synthetic data augmentation and to establish benchmark results.Result Experimental results show that the proposed LAFI dataset provides improved category richness, instance scale, and scene diversity compared with existing fine-grained ship detection datasets. Moreover, incorporating synthetic data from LAFI-Diffusion into the training process consistently improves detection performance and generalization ability across different oriented object detection models. Performance gains are particularly evident in complex maritime environments, such as crowded ports and scenes with varying sea states and illumination conditions. The benchmark evaluations also indicate that the contribution of synthetic data varies across detection methods, suggesting that appropriate integration strategies are important for fully exploiting synthetic data.Conclusion This study presents a large-scale fine-grained ship detection dataset that integrates real optical remote sensing imagery with controllable diffusion-based synthetic data generation. By substantially expanding data scale and enhancing scene diversity, the proposed LAFI-Diffusion dataset effectively addresses the limitations of existing fine-grained ship detection benchmarks. The experimental results confirm that synthetic data can serve as an effective complement to real-world samples, improving detection accuracy and robustness for oriented ship detection models. The released datasets and benchmark results provide valuable support for future research on fine-grained ship detection and related remote sensing applications.关键词：remote sensing imagery;ship detection;dataset;diffusion model;synthetic data generation;Fine-grained recognition;oriented bounding box;data augmentation166|238|0更新时间：2026-04-21 -
摘要：Objective Shorelines of rivers and lakes constitute the critical interface between inland water bodies and adjacent terrestrial systems, functioning as key transitional zones that regulate flood storage, buffer hydrological extremes, and maintain aquatic–terrestrial ecological system. Accurate and timely monitoring of shoreline dynamics is therefore substantial for practical flood risk mitigation, water resource management, and the planning, protection, and restoration of freshwater ecosystems. This study aims to develop an effective and reliable deep learning (DL) model to identify shorelines from high-resolution remote sensing imagery, supporting systematic quantification of their spatiotemporal variation in response to climate and human intervention.Method Deep learning models based on the Swin-UNet architecture were set-up to identify water extents, boundaries of which were then vectorized to obtain the shorelines of the study area by using the Gaofen and Sentinel-2 remote sensing imagery respectively. The Swin-UNet was adopted in this study due to its performance in capturing both local details and long-range contextual information, enabling robust delineation of complex shoreline morphologies. Annual and monthly shorelines were retrieved from Gaofen and Sentinel-2 respectively for the period 2015–2025 based on the trained and well validated Swin-UNet models. Intra-annual and interannual variations of shorelines in terms of their length, development index, fractal dimension and composition were then assessed.Result The Swin-UNet models, based on both Gaofen (GF) and Sentinel-2 imagery, achieved a classification accuracy of water pixel exceeding 90%, with clear, continuous shoreline boundaries and well-preserved geometric details, significantly outperforming traditional thresholding and machine learning methods. Results based on Gaofen imagery show that the annual mean area of water extent in the study area was around 314.7 km², with a corresponding shoreline length of about 7,306.4 km. The results derived from Gaofen imagery were largely consistent (R2=0.99) with those from Sentinel-2. However, owing to its higher spatial resolution, the water area extracted from Gaofen imagery was about 18.9% larger than that from Sentinel-2, while the shoreline length was approximately 75% longer. Length of the shoreline showed stronger intra-annual variability (CV=12.8%) than inter-annual variability (CV=9.0%). Shorelines of artificial water bodies were found more morphologically stable than that of natural water bodies. Composition of shorelines in the study area exhibited a transition from semi-natural/semi-artificial to artificial shorelines, while natural shorelines remained relatively stable.Conclusion The study demonstrates that Swin-UNet–based deep learning approaches provide highly accurate, spatially detailed, and temporally consistent monitoring of river–lake shoreline dynamics, which is essential for effective management, protection, and restoration of inland aquatic environments. Changes in the composition of the shorelines in the study area were closely associated with urban spatial expansion and strongly influenced by a series of ecological restoration policies, including the “Grain for Green” program and wetland restoration initiatives.关键词：river and lake shorelines;dynamic monitoring;Swin-UNet;Gaofen satellite;Sentinel-2;Chenzhou City274|251|0更新时间：2026-04-21 -
摘要：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.关键词：Remote sensing fine extraction;SAM;prior knowledge fusion;rice mapping;adaptive statistical learning;parcel-level segmentation;iterative optimization;prompt information optimization122|116|0更新时间：2026-04-21 -
摘要：(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.关键词：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 method205|139|0更新时间：2026-03-26 -
摘要：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.关键词：PlanetScope;Sentinel-2;spatiotemporal data fusion;growth monitor;precision agriculture;field scale;phenological curve;near real-time monitoring334|503|0更新时间：2026-03-17 -
摘要：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.关键词：inland waters;particulate organic carbon;isotopic tracing;n–alkanes;semi–analytical model162|127|0更新时间：2026-03-13 -
摘要：Objective Visible light images and Synthetic Aperture Radar (SAR) images are two primary data sources in satellite remote sensing, widely used in fields such as urban planning, disaster prevention and mitigation, and national security. Feature extraction serves as a key bridge connecting remote sensing images with high-level applications, directly influencing the effectiveness of intelligent interpretation in complex scenarios. This paper systematically reviews the evolution of feature extraction methods from traditional handcrafted approaches to data-driven deep learning models, identifies the inherent challenges faced by these methods in processing visible light and SAR images, and introduces a knowledge-driven feature extraction paradigm. The aim is to explore how the integration of domain knowledge can enhance feature robustness, interpretability, and generalization, thereby advancing the field of remote sensing intelligent interpretation.Method The study follows a structured review methodology, focusing on literature from authoritative sources such as IEEE TGRS, ISPRS Journal, CVPR, and ICCV between 2019 and 2025. First, traditional feature extraction methods—including edge detection (e.g., Sobel, Canny, ROEWA), texture analysis (e.g., Gabor filters, GLCM), color features, and keypoint extraction (e.g., SIFT, Harris)—are summarized and analyzed for their strengths and limitations. Second, deep learning-based methods are examined, covering convolutional neural networks (CNNs) for local spatial modeling, graph neural networks (GNNs) for relational reasoning, and vision transformers (ViTs) for global context understanding. Third, to address challenges such as semantic ambiguity, scale variation, imaging condition sensitivity, and speckle noise interference, a knowledge-driven framework is proposed. This framework integrates three categories of knowledge—visual knowledge (e.g., edges, textures), geospatial knowledge (e.g., DEM, topological relations), and physical knowledge (e.g., scattering models, atmospheric physics)—into deep networks through mechanisms such as knowledge-as-architecture, knowledge-as-data, and knowledge-as-loss.Result The analysis demonstrates that traditional methods offer high interpretability and computational efficiency but lack adaptability to complex scenes and nonlinear relationships. Deep learning methods significantly improve feature representation and task performance but are heavily dependent on large annotated datasets and suffer from poor generalization under domain shifts. The proposed knowledge-driven approach effectively mitigates these issues. For example, incorporating edge prior knowledge enhances boundary precision in semantic segmentation; embedding elevation data improves terrain-aware classification; and integrating physical models such as electromagnetic scattering models aids in SAR image interpretation and speckle suppression. Experimental results from cited studies show performance gains across various tasks, such as increased mIoU in segmentation and higher accuracy in object detection, confirming that knowledge-guided features are more discriminative, stable, and interpretable.Conclusion Feature extraction for visible light and SAR images has evolved from manual design to data-driven learning and is now progressing toward a hybrid paradigm that combines data-driven perception with knowledge-driven reasoning. This paper highlights that integrating visual, geospatial, and physical knowledge into deep learning models can address key challenges in remote sensing feature extraction, including semantic ambiguity, scale extremes, imaging variability, and noise interference. Future research should focus on developing large-scale remote sensing foundation models, deepening the synergy between data and knowledge, and promoting cross-disciplinary integration with emerging technologies like vision-language models and diffusion models. Such advancements will further enhance the intelligence, reliability, and applicability of remote sensing image interpretation systems.关键词：remote sensing intelligent interpretation;visible light images;SAR Images;manual feature extraction;depth feature extraction;data and knowledge driven149|1356|0更新时间：2026-03-13 -
摘要：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.关键词：Crop Lodging Monitoring;Standard Growth Curve;time-series analysis;Spatiotemporal Joint Detection;Sentinel-2124|458|0更新时间：2026-03-13 -
摘要：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.关键词：Carbon stocks;Hyperopt hyperparameter tuning;machine learning;AST;Pinus densata;GAC;remote sensing inversion;uncertainty analysis129|421|0更新时间：2026-03-13 -
摘要：Objective Typhoon Yagi (No. 202411) is formed east of the Philippines and moved northwestward. After entering the South China Sea, it rapidly intensified into a super typhoon and brought severe winds, heavy rainfall, and flooding to the Pearl River Delta, including Guangzhou and Haikou. This study uses multi-temporal synthetic aperture radar (SAR) observations to analyze the fine-scale sea surface winds during Yagi’s evolution.Method The 500 m resolution wind products were processed with land masking, noise filtering, scalloping correction, and typhoon center detection.Result Results show that during the rapid intensification phase, both maximum wind speed and wind radii increased notably, and the winds became more asymmetric. After Yagi reached its peak intensity, these parameters stabilized. Spectral analysis based on two-dimensional Fourier transforms reveals clear kilometer-scale roll vortices in the typhoon boundary layer. Their orientations align with the outer shear flow, and the dominant wavelength ranges from 2–3 km. The spatial distribution of these rolls shows little dependence on typhoon intensity, consistent with the idea that roll formation is mainly driven by local shear instability.Conclusion Although the dataset is limited, the results demonstrate that SAR observations are effective for capturing fine-scale wind structures and boundary-layer processes. This work provides a useful reference for future large-sample studies on air–sea interaction mechanisms in different typhoon stages.关键词：synthetic aperture radar (SAR);Typhoon Yagi;Wind structure;Wind radius;Asymmetry;Boundary-layer rolls119|376|0更新时间：2026-03-13 -
摘要：In the field of computer vision, lightweight target detection in remote sensing images is an academically challenging research topic. With the continuous improvement of the accuracy of remote sensing image target detection, the complexity of the model has increased significantly accordingly. The current remote sensing image target detection methods find it difficult to achieve an effective balance between accuracy and model complexity. Unlike conventional natural images, remote sensing images have a large imaging area, limited available information, and a multitude of target types. The vast amount of background information can easily interfere with detection, and the excessive number of target categories makes it difficult to achieve high-precision detection of specific targets. Secondly, targets in remote sensing images are densely packed, arbitrarily rotated, and exhibit significant scale variations, posing a great challenge for target localization. Furthermore, as the accuracy continues to improve in remote sensing image target detection, models have become increasingly complex in pursuit of high precision. These complex models’ heavy computational load and large parameter count severely restrict practical application on resource-constrained devices, model lightweighting has become a key factor in promoting technological application. To address the aforementioned issues, this paper proposes a lightweight remote sensing image target detection method based on Spatial-Channel Reconstruction, aiming to maintain high-precision detection of circular and rectangular-like targets while reducing model complexity. Firstly, in response to the redundant information within feature maps at the spatial dimension, a Spatial Reconstruction Unit (SRU) is adopted. It separates feature maps based on the richness of spatial features into groups with abundant spatial information and groups with redundant spatial information, and then performs cross-reconstruction operations on the two groups to reduce spatial feature redundancy and enhance the spatial feature representation of remote sensing image targets. Subsequently, to address the redundancy of channel information in feature maps, a Partial Convolution-based Channel Reconstruction Unit (PCRU) is proposed. It divides feature maps into two parts along the channel dimension, one part uses partial convolution for efficient feature extraction, and the other part uses pointwise convolution to obtain hidden detailed information. The two parts are weighted and reconstructed, and then concatenated to extract features at a lower computational cost and enhance the representation of important channels. The lightweight method based on Spatial-Channel Reconstruction is applied to ACRFNet, an adaptive circular receptive field network for remote sensing image target detection. Experimental results on the NWPU VHR-10, DIOR and HRRSD datasets verify its performance. Specifically, the method can maintain or even improve detection accuracy, while significantly reducing the model’s computational load and parameter volume. Compared with other mainstream methods for remote sensing image target detection, the proposed method demonstrates obvious advantages in balancing detection accuracy and model complexity, especially for circular and rectangular-like targets that are difficult to detect. The lightweight remote sensing target detection method based on Spatial-Channel Reconstruction effectively solves the core problem of balancing detection accuracy and model complexity. By reducing feature redundancy, it overcomes the deployment limitations of complex models on resource-constrained devices, retains good adaptability to typical targets, and provides a reliable technical support for the practical application of remote sensing target detection technology.关键词：remote sensing imagery;object detection;circular target;model lightweighting;reconstruction unit115|129|0更新时间：2026-03-09 -
摘要：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.关键词：urban renewal;street view imagery;semantic change detection;scene change detection;weak supervision;dynamic index275|424|0更新时间：2026-03-06 -
摘要：(Objective)River discharge is a pivotal variable within the hydrological cycle, holding significant importance for flood warning, water resource allocation, and eco-environmental management. Traditional ground-based methods are limited by sparse station distribution and high costs of data acquisition, particularly in areas with complex terrains or remote regions, making it difficult to meet the demands of precise water resource management. Satellite remote sensing technology offers extensive coverage and high spatiotemporal resolution, providing new data sources and methodologies for river discharge monitoring. Machine learning (ML) approaches can accurately simulate complex relationships between river discharge and multiple driving factors, offering novel avenues for processing intricate hydrological data and optimizing models. By integrating machine learning algorithms with remote sensing and in-situ river discharge, it can provide the innovative measure for the efficient and reliable of river discharge monitoring.(Method)This study selected the Tangnaihai Hydrometry Station as the study area, and proposed a river discharge monitoring method by integrating satellite remote sensing and ML methods. Firslty, the Sentinel-2 imagery was utilized to extract river water surface width based on the Google Earth Engine cloud platform. The GLDAS v2.2 model-simulated five variables were served as predictor variables, namely evapotranspiration, soil moisture, temperature, terrestrial water storage and runoff. Discharge monitoring models were subsequently developed based on four statistical methods (linear function, power function, exponential function, and polynomial function) and four ML algorithms (XGBoost, Random Forest, LightGBM, and CatBoost). The discrepancies among different models were assessed, and the Shapley Additive Explanation (SHAP) method was employed to quantify the importance of different input variables.(Result)The results demonstrate that the polynomial function model demonstrates superior performance over other three statistical models during the testing period, with an R²of 0.67, and its error metrics (MAE: 319.01 m³/s, RMSE: 393.14 m³/s) were lower than those of the other three statistical models. Compare to traditional statistical approaches, ML models exhibit significant improvements overall in both simulation accuracy and stability, and the coefficient of determination (R2) increased by 46.15%, while the root mean square error (RMSE) and mean absolute error (MAE) decreased by 54.61% and 55.65, respectively. Notably, the Random Forest model achieved the optimal performance in the testing phase, with the R2 of 0.96, Nash-Sutcliffe efficiency coefficient (NSE) of 0.89, RMSE of 172.81 m3/s, and MAE of 147.33 m3/s, reflecting robust generalization capability and stability. SHAP analysis revealed that water surface width contributed most significantly to the discharge monitoring model (189.02), followed by soil moisture (145.11) and temperature (97.41). The runoff variable exhibited the minimal degree of influence on the river discharge monitoring model with the value of 14.14%.(Conclusion)This study confirms the feasibility and superiority of integrating satellite remote sensing and ML approaches for high-accuracy discharge estimation in regions characterized by complex topography and data scarcity. Future work could be optimized by integration of higher-resolution satellite imagery and mechanistic models with physical processes.关键词：satellite remote sensing;machine learning;statistical models;SHAP method;discharge monitoring;Tangnaihai Hydrometry Station197|445|0更新时间：2026-02-27 -
摘要：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.关键词：Gaofen-1;Dynamic Water Body;water body extraction;dataset;feature extraction343|497|0更新时间：2025-12-11 -
摘要：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.关键词：poverty-alleviated counties;ecological environment;socioeconomic development;Remote sensing dataset;coupling coordination;boosted regression tree model;contribution rate;spatial distribution pattern;industrial poverty alleviation229|226|0更新时间：2025-11-04 -
摘要：(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.关键词：Satellite pixel scale ground truth;ground observation;scale difference;spatial heterogeneity;representativeness errors;uncertainty analysis;validation;accuracy assessment;training sample394|795|0更新时间：2025-07-21 -
摘要：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.关键词：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 activities525|791|0更新时间：2025-07-21 -
摘要：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.关键词：Remote sensing observation;mesoscale eddy;coupling analysis;sea surface temperature;sea surface height;Chlorophyll concentration;wind speed162|725|0更新时间：2024-06-17 -
摘要：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;关键词：land surface temperature;AMSR-E LST;reconstruction;deep learning;MTFC-CNN2692|921|0更新时间：2022-03-09 -
摘要：With the global warming, land ecosystem productivity presents a substantial enhanced trend, which is primarily attributed to the prolonged growing season of terrestrial vegetation. However, the impact of summer greatest growing magnitude on vegetation productivity remains unclear. This study aims to explore the different impacts of growing season length (LOS) and magnitude (MAG) on the long-term trends and interannual variability of vegetation productivity, based on long time series of satellite images. Firstly, the GIMMS NDVI3g data was applied to derive the key phenology parameters, including start and end of growing season (SOS, EOS), summer growing peak. Summer vegetation productivity (Summary of Vegetation Index, VIsum) was obtained by integrating the area under growth curve. The long-term trends and interannual variability of SOS, EOS and VIsum was explored at pixel and land cover ecosystem level. Special attention was paid to the impacts of LOS and MAG on VIsum changes. The relative important method was used to quantify contribution of LOS and MAG to VIsum. The results indicate the overall vegetation productivity for Northeastern China do not behave a clear trend, but LOS has a decrease trend and MAG show an increase trend. VIsum presents a consistent fluctuation pattern with LOS, but changes asynchronously with MAG. There is a cycle of 10-years length for the trends and variability of LOS, MAG and VIsum. For the spatial distribution at pixel level, VIsum tends to increase in needle forest of northern and southern part, and the western grassland. The spatial distribution of LOS trend shows an opposite pattern with that of MAG. LOS is becoming shortened in the middle cropland and western grassland (81.5% of vegetated area). While MAG is enhanced in this ecosystem (16.5% of vegetated area). This reflects shortened LOS induces the increase of MAG. Across various land types, LOS contribute mostly to the long-term trends and interannual variability of VIsum (75%). LOS plays a key role in northern needle and eastern broad-leaved forest, MAG accounts for 27% of relative importance. The carbon flux data show a similar change pattern with satellite NDVI in the calculation of phenology parameters and vegetation productivity. Although LOS mainly control the trend and interannual variability, MAG may play a control role in the future as it keeps the increase trend.关键词：植被物候;植被生产力;生长季长度;生长幅度;相对重要性;GIMMS NDVI3g1717|2225|0更新时间：2021-06-11
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