ISPRS Journal of Photogrammetry and Remote Sensing最新文献_第7页

MARSNet: A Mamba-driven adaptive framework for robust multisource remote sensing image matching in noisy environments MARSNet：一个mamba驱动的自适应框架，用于噪声环境下的鲁棒多源遥感图像匹配

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2026-01-05 DOI: 10.1016/j.isprsjprs.2025.12.021

Weipeng Jing , Peilun Kang , Donglin Di , Jian Wang , Yang Song , Chao Li , Lei Fan

Semi-dense matching of multi-source remote sensing images under noise interference remains a challenging task. Existing detector-free methods often exhibit low efficiency and reduced performance when faced with large viewpoint variations and significant noise disturbances. Due to the inherent noise and modality differences in multi-source remote sensing images, the accuracy and robustness of feature matching are substantially compromised. To address this issue, we propose a hybrid network for multi-source remote sensing image matching based on an efficient and robust Mamba framework, named MARSNet. The network achieves efficient and robust matching through the following innovative designs: First, it leverages the efficient Mamba network to capture long-range dependencies within image sequences, enhancing the modeling capability for complex scenes. Second, a frozen pre-trained DINOv2 foundation model is introduced as a robust feature extractor, effectively improving the model’s noise resistance. Finally, an adaptive fusion strategy is employed to integrate features, and the Mamba-like linear attention mechanism is adopted to refine the Transformer-based linear attention, further enhancing the efficiency and expressive power for long-sequence processing. To validate the effectiveness of the proposed method, extensive experiments were conducted on multi-source remote sensing image datasets, covering various scenarios such as noise-free, additive random noise, and periodic stripe noise. The experimental results demonstrate that the proposed method achieves significant improvements in matching accuracy and robustness compared to state-of-the-art methods. Additionally, by performing pose error evaluation on a large-scale general dataset, the superior performance of the proposed method in 3D reconstruction is validated, complementing the test results from the multi-source remote sensing dataset, thereby providing a more comprehensive assessment of the method’s generalization ability and robustness.

噪声干扰下多源遥感图像的半密集匹配一直是一项具有挑战性的任务。现有的无探测器方法在视点变化大、噪声干扰大的情况下，效率低、性能下降。由于多源遥感图像中固有的噪声和模态差异，大大降低了特征匹配的准确性和鲁棒性。为了解决这一问题，我们提出了一种基于高效鲁棒曼巴框架的多源遥感图像匹配混合网络，称为MARSNet。该网络通过以下创新设计实现了高效鲁棒的匹配：首先，利用高效的曼巴网络捕获图像序列内的远程依赖关系，增强了对复杂场景的建模能力。其次，引入冻结预训练的DINOv2基础模型作为鲁棒特征提取器，有效提高了模型的抗噪性；最后，采用自适应融合策略对特征进行融合，并采用类似mamba的线性注意机制对基于transformer的线性注意进行细化，进一步提高了长序列处理的效率和表达能力。为了验证该方法的有效性，在多源遥感图像数据集上进行了大量实验，包括无噪声、加性随机噪声和周期性条纹噪声等多种场景。实验结果表明，与现有方法相比，该方法在匹配精度和鲁棒性方面都有显著提高。此外，通过在大型通用数据集上进行位姿误差评估，验证了该方法在三维重建中的优越性能，与多源遥感数据集的测试结果相补充，从而更全面地评估了该方法的泛化能力和鲁棒性。

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引用次数: 0

DVGBench: Implicit-to-explicit visual grounding benchmark in UAV imagery with large vision–language models DVGBench：基于大型视觉语言模型的无人机图像中隐式到显式视觉接地基准

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2026-01-14 DOI: 10.1016/j.isprsjprs.2026.01.005

Yue Zhou , Jue Chen , Zilun Zhang , Penghui Huang , Ran Ding , Zhentao Zou , PengFei Gao , Yuchen Wei , Ke Li , Xue Yang , Xue Jiang , Hongxin Yang , Jonathan Li

Remote sensing (RS) large vision–language models (LVLMs) have shown strong promise across visual grounding (VG) tasks. However, existing RS VG datasets predominantly rely on explicit referring expressions – such as relative position, relative size, and color cues – thereby constraining performance on implicit VG tasks that require scenario-specific domain knowledge. This article introduces DVGBench, a high-quality implicit VG benchmark for drones, covering six major application scenarios: traffic, disaster, security, sport, social activity, and productive activity. Each object provides both explicit and implicit queries. Based on the dataset, we design DroneVG-R1, an LVLM that integrates the novel Implicit-to-Explicit Chain-of-Thought (I2E-CoT) within a reinforcement learning paradigm. This enables the model to take advantage of scene-specific expertise, converting implicit references into explicit ones and thus reducing grounding difficulty. Finally, an evaluation of mainstream models on both explicit and implicit VG tasks reveals substantial limitations in their reasoning capabilities. These findings provide actionable insights for advancing the reasoning capacity of LVLMs for drone-based agents. The code and datasets will be released at https://github.com/zytx121/DVGBench.

遥感（RS）大视觉语言模型（LVLMs）在视觉基础（VG）任务中显示出强大的应用前景。然而，现有的RS VG数据集主要依赖于显式引用表达式——例如相对位置、相对大小和颜色线索——从而限制了需要特定于场景的领域知识的隐式VG任务的性能。本文介绍了DVGBench，这是一个用于无人机的高质量隐式VG基准测试，涵盖了六个主要应用场景：交通、灾难、安全、体育、社交活动和生产活动。每个对象都提供显式和隐式查询。基于该数据集，我们设计了DroneVG-R1，这是一种将新型的隐式到显式思维链（I2E-CoT）集成到强化学习范式中的LVLM。这使模型能够利用特定场景的专业知识，将隐式引用转换为显式引用，从而降低接地难度。最后，对主流模型在显式和隐式VG任务上的评估揭示了它们在推理能力上的实质性限制。这些发现为提高基于无人机的智能体的LVLMs推理能力提供了可操作的见解。代码和数据集将在https://github.com/zytx121/DVGBench上发布。

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引用次数: 0

Attributing GHG emissions to individual facilities using multi-temporal hyperspectral images: Methodology and applications 利用多时相高光谱图像将温室气体排放归因于单个设施：方法和应用

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2026-01-17 DOI: 10.1016/j.isprsjprs.2026.01.014

Yichi Zhang , Ge Han , Yiyang Huang , Huayi Wang , Hongyuan Zhang , Zhipeng Pei , Yuanxue Pu , Haotian Luo , Jinchun Yi , Tianqi Shi , Siwei Li , Wei Gong

Industrial parks are major sources of greenhouse gas (GHG) emissions and the ultimate entities responsible for implementing mitigation policies. Current satellite remote sensing technologies perform well in reporting localized strong point-source emissions, but face significant challenges in monitoring emissions from multiple densely clustered sources. To address the limitation, we propose an emission allocation framework, EA-MILES, which integrates multi-source hyperspectral data with plume modeling to quantify process-level emissions. Simulation experiments show that with existing hyperspectral satellites, EA-MILES can estimate emissions for sources with intensities above 80 t CO₂/h and 100 kg CH₄/h with bias not exceed 13.60 % and 17.08 %. A steel and power production park is selected as a case study, where EA-MILES estimates process-level emissions with uncertainties ranging from 26.33 % to 37.78 %. Estimation results are consistent with inventory values derived from emission factor methods. Top-down Integrated Mass Enhancement method is utilized to compare with EA-MILES results, the estimation bias did not exceed 16.84 %. According to the Climate TRACE, about 32 % of CO₂ and 44 % of CH₄ point-sources worldwide fall within EA-MILES detection coverage, accounting for over 80 % and 55 % of anthropogenic CO₂ and CH₄ emissions. Therefore, this study provides a novel satellite-based approach for reporting facility-scale GHG emissions in industrial parks, offering transparent and accurate monitoring data to support the mitigation and energy transition decision-making.

工业园区是温室气体（GHG）排放的主要来源，也是负责实施减缓政策的最终实体。目前的卫星遥感技术在报告局部强点源排放方面表现良好，但在监测多个密集聚集源的排放方面面临重大挑战。为了解决这一限制，我们提出了一个排放分配框架EA-MILES，该框架将多源高光谱数据与羽流建模相结合，以量化过程级排放。模拟实验表明，利用现有的高光谱卫星，EA-MILES可以估算强度在80 t CO2/h和100 kg CH4/h以上的源的排放量，偏差不超过13.60%和17.08%。以某钢铁和电力生产园区为例，EA-MILES估算的过程级排放不确定性在26.33% ~ 37.78%之间。估算结果与排放因子法得出的库存值一致。采用自顶向下集成质量增强方法与EA-MILES结果进行比较，估计偏差不超过16.84%。根据Climate TRACE，全球约32%的CO2和44%的CH4点源在EA-MILES检测范围内，占人为CO2和CH4排放量的80%和55%以上。因此，本研究提供了一种新的基于卫星的方法来报告工业园区设施规模的温室气体排放，提供透明和准确的监测数据，以支持减缓和能源转型决策。

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引用次数: 0

SAR-NanoShipNet: A scale-adaptive network for robust small ship detection in SAR imagery SAR- nanoshipnet：一种用于SAR图像鲁棒小型船舶检测的尺度自适应网络

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2025-12-19 DOI: 10.1016/j.isprsjprs.2025.12.006

Yuhao Zhang , Jieru Chi , Guowei Yang , Chenglizhao Chen , Teng Yu

Currently, notable progress has been attained in small ship target detection for synthetic aperture radar (SAR) imagery, with such advancements being driven by three key methodological innovations within the deep learning framework: self-supervision combined with knowledge distillation, rotated bounding box detection, and multi-scale feature fusion. However, it still faces challenges such as high speckle noise in SAR images, difficulty in extracting small target features, geometric distortion of ship shapes and heading dependence. Therefore, this article proposes a new SAR-NanoShipNet model. To enhance the targeting of ship objects, the proposed method employs a specialized convolution (DABConv) that exhibits greater suitability for ship targets, replacing the conventional standard convolution. As opposed to traditional approaches for SAR target detection, which typically lack the capability to adaptively capture the irregular boundaries and low-contrast features of small ship targets in SAR images, this method pioneers the adaptive capture of these features through deformable convolutions and boundary attention mechanisms, leading to enhanced target localization accuracy. In addition, we introduce the VerticalCompSPPF module (VC-SPPF), which incorporates longitudinal multi-scale convolution alongside a channel attention mechanism. Finally, the design of D-CLEM is linked with DABConv to enhance directional feature extraction while also fusing, improving the accuracy of small object detection. We have validated the superiority of our method on five datasets, particularly for high precision detection of small targets (AP

_{s}

↑

2.66%). Our code can be found at https://github.com/Z-Yuhao/1.git.

目前，合成孔径雷达（SAR）图像的小型船舶目标检测取得了显著进展，这些进展主要得益于深度学习框架内的三个关键方法创新：结合知识蒸馏的自我监督、旋转边界盒检测和多尺度特征融合。然而，该方法仍然面临着SAR图像中散斑噪声高、小目标特征提取困难、舰船形状几何畸变和航向依赖性等问题。为此，本文提出了一种新的SAR-NanoShipNet模型。为了提高船舶目标的瞄准能力，该方法采用了一种更适合船舶目标的专用卷积（DABConv），取代了传统的标准卷积。传统的SAR目标检测方法通常缺乏自适应捕获SAR图像中小船目标不规则边界和低对比度特征的能力，而该方法率先通过可变形卷积和边界注意机制自适应捕获这些特征，从而提高了目标定位精度。此外，我们还介绍了垂直compsppf模块（VC-SPPF），该模块结合了纵向多尺度卷积和通道注意机制。最后，将D-CLEM的设计与DABConv相结合，在增强方向特征提取的同时进行融合，提高小目标检测的精度。我们已经在5个数据集上验证了我们的方法的优越性，特别是对于小目标的高精度检测（APs ^ 2.66%）。我们的代码可以在https://github.com/Z-Yuhao/1.git上找到。

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引用次数: 0

Improvement of the consistency among long-term global land surface phenology products derived from AVHRR, MODIS, and VIIRS observations AVHRR、MODIS和VIIRS长期全球地表物候产品一致性的改进

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2025-12-28 DOI: 10.1016/j.isprsjprs.2025.12.010

Yongchang Ye , Xiaoyang Zhang , Yu Shen , Khuong H. Tran , Shuai Gao , Yuxia Liu , Shuai An

Land surface phenology (LSP) has been widely derived from observations of different satellite sensors, including the Advanced Very High-Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). However, the consistency of long-term LSP products is a major concern because the time series data quality may vary greatly, particularly due to temporal gaps caused by cloud contamination, instrumental degradations (e.g., orbital drift), and other factors. Therefore, this study investigated the reconstruction of the high-quality time series of vegetation indices using a Spatiotemporal Shape-Matching Model (SSMM) and the reduction of the temporal gap impacts on LSP detections globally at the climate modeling grid (0.05°). Specifically, we first generated the climatology of a 3-day two-band Enhanced Vegetation Index (EVI2) using the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset from 2003 to 2022. The temporal climatology EVI2 was used in the SSMM algorithm to fuse the 3-day time series of EVI2 data derived separately from five different surface reflectance products: AVHRR reflectance data (1981–2019), MODIS standard surface reflectance (SSR) and NBAR data (2000–2023), and VIIRS SSR and NBAR data (2012–2023). These five sets of EVI2 time series were further applied to detect LSP metrics. The result indicates that the coefficient of determination (R²) increased by up to 0.2 among the fused EVI2 time series from AVHRR, MODIS SSR, VIIRS SSR, MODIS NBAR, and VIIRS NBAR compared to that among the raw EVI2 time series. Although the AVHRR EVI2 dataset was more consistent with MODIS SSR or VIIRS SSR observations than with MODIS NBAR or VIIRS NBAR datasets, the highest R² was found between MODIS and VIIRS NBAR EVI2, especially between their fused EVI2 time series. Consequently, the mean absolute difference (MAD) of LSP metrics was reduced by one to three days in comparing fused EVI2 with raw EVI2 time series between two different sensors. Overall, the highest LSP consistency was found between fused MODIS NBAR and fused VIIRS NBAR, which was followed by LSP detections between raw MODIS NBAR and raw VIIRS NBAR, fused MODIS SSR and fused AVHRR, raw MODIS SSR and raw AVHRR, fused MODIS NBAR and fused AVHRR, and raw MODIS NBAR and raw AVHRR. The result suggests that long-term LSP products from 1980 forward should be generated using the fused EVI2 time series from AVHRR, MODIS SSR, and VIIRS SSR, while the product from 2000 forward should be produced using the fused time series from MODIS NBAR and VIIRS NBAR observations.

陆地表面物候学（LSP）广泛来源于不同卫星传感器的观测，包括先进高分辨率辐射计（AVHRR）、中分辨率成像光谱仪（MODIS）和可见红外成像辐射计套件（VIIRS）。然而，长期LSP产品的一致性是一个主要问题，因为时间序列数据质量可能会有很大变化，特别是由于云污染、仪器退化（例如轨道漂移）和其他因素造成的时间间隙。因此，本研究利用时空形状匹配模型（spatial - temporal Shape-Matching Model， SSMM）重建植被指数的高质量时间序列，并在气候模拟网格（0.05°）下降低时间间隙对LSP检测的影响。具体而言，我们首先使用MODIS Nadir双向反射率分布函数(BRDF)-调整反射率（NBAR）数据集生成了2003 - 2022年3天两波段增强型植被指数（EVI2）的气候。利用时间气候学数据EVI2在SSMM算法中融合了5种不同地表反射率产品分别获得的3 d时间序列数据：AVHRR反射率数据（1981-2019）、MODIS标准地表反射率（SSR）和NBAR数据（2000-2023）、VIIRS SSR和NBAR数据（2012-2023）。这五组EVI2时间序列进一步用于检测LSP度量。结果表明，与原始EVI2时间序列相比，AVHRR、MODIS SSR、VIIRS SSR、MODIS NBAR和VIIRS NBAR融合的EVI2时间序列的决定系数（R2）提高了0.2。虽然AVHRR EVI2数据集与MODIS SSR或VIIRS SSR观测值的一致性高于MODIS NBAR或VIIRS NBAR数据集，但MODIS和VIIRS NBAR EVI2之间的R2最高，尤其是两者融合的EVI2时间序列之间的R2最高。因此，在比较两个不同传感器之间融合的EVI2和原始EVI2时间序列时，LSP度量的平均绝对差（MAD）减少了一到三天。总体而言，融合MODIS NBAR与融合virs NBAR之间的LSP一致性最高，其次是原始MODIS NBAR与原始virs NBAR、融合MODIS SSR与融合AVHRR、原始MODIS SSR与原始AVHRR、融合MODIS NBAR与融合AVHRR、原始MODIS NBAR与融合AVHRR以及原始MODIS NBAR与原始AVHRR之间的LSP一致性。结果表明，1980年以后的长期LSP产品应采用AVHRR、MODIS SSR和VIIRS SSR数据融合的EVI2时间序列生成，2000年以后的产品应采用MODIS NBAR和VIIRS NBAR数据融合的时间序列生成。

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引用次数: 0

City-Facade: A city-level large-scale point cloud building facade dataset for semantic & instance segmentation 城市立面：用于语义和实例分割的城市级大规模点云建筑立面数据集

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2026-01-09 DOI: 10.1016/j.isprsjprs.2026.01.003

Yiping Chen , Jonathan Li , Ting Han , Huifang Feng , Jun Chen , Cheng Wang

With the growing demand for high-quality 3D urban scene understanding in applications such as building information modeling (BIM) and digital twins, large-scale and well-annotated 3D datasets have become essential for advancing scientific research and algorithm development. However, existing building facade datasets are predominantly image-based, suffering from drawbacks such as a lack of spatial information and sensitivity to lighting and weather conditions. Moreover, publicly available large-scale labeled datasets of building point clouds still remain scarce and have a relatively small coverage area. To this end, we introduce a city-level building facade point cloud dataset named City-Facade for semantic-level and instance-level segmentation. Firstly, the paper conducts a comprehensive review and analysis of existing urban & building point cloud datasets and point cloud segmentation algorithms. Secondly, we present a large-scale building facade dataset with approximately 200 millions of labeled 3D point clouds (over 60 km roads) belonging to urban scenarios, realized to facilitate the development and evaluation of semantic and instance level algorithms in the urban understanding. Finally, baseline experiments for semantic and instance segmentation are conducted to encourage further research. The proposed dataset is accessible at https://github.com/gorgeouseping/City-Facade, comprising the dataset and segmentation baselines for better comparison and presentation of strengths and weaknesses of different methods. Additionally, the data will undergo continuous improvement and updates based on feedback from the community.

随着建筑信息模型（BIM）和数字孪生等应用对高质量3D城市场景理解的需求不断增长，大规模和精心注释的3D数据集已成为推进科学研究和算法开发的必要条件。然而，现有的建筑立面数据集主要是基于图像的，存在诸如缺乏空间信息和对照明和天气条件的敏感性等缺点。此外，公开可用的大规模建筑点云标记数据集仍然很少，覆盖面积相对较小。为此，我们引入了城市级建筑立面点云数据集City-Facade，用于语义级和实例级的分割。本文首先对现有的城市建筑点云数据集和点云分割算法进行了全面的综述和分析。其次，我们提供了一个大型建筑立面数据集，其中包含大约2亿个属于城市场景的标记3D点云（超过60公里的道路），以促进城市理解中语义和实例级算法的开发和评估。最后，进行了语义和实例分割的基线实验，以鼓励进一步的研究。建议的数据集可访问https://github.com/gorgeouseping/City-Facade，包括数据集和分割基线，以便更好地比较和展示不同方法的优缺点。此外，数据将根据社区的反馈进行持续改进和更新。

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引用次数: 0

An enhanced spatiotemporal prediction method on landslide displacement with LDP-ConvFormer and MT-InSAR observations 基于LDP-ConvFormer和MT-InSAR观测的滑坡位移增强时空预测方法

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.isprsjprs.2025.12.027

Jianao Cai , Dongping Ming , Feng Liu , Wenyi Zhao , Mingzhi Zhang , Xiao Ling , Mengyuan Zhu , Lu Xu , Tingting Lu , Ningjie Liu , Yanfei Wei , Ming Huang

Landslide Displacement Prediction (LDP) implementation for Landslide Early Warning Systems (LEWS) using the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique poses significant challenges in the Three Gorges Reservoir Area (TGRA). On the one hand, the limited revisit frequency of satellites fails to satisfy the high-frequency monitoring requirements of LEWS. On the other hand, traditional LDP methods concentrate on single-point modeling. It neglects the spatial correlation between displacement points and the landslide surface. To enhance the low-frequency MT-InSAR observations, this paper proposes a new hybrid algorithm that integrates the Kalman Filter (KF) and LDP-ConvFormer to achieve enhanced spatiotemporal LDP. First, multi-orbit MT-InSAR measurements are transformed to downslope displacement. Subsequently, the multi-orbit downslope displacements are integrated by KF to generate time series data with enhanced temporal resolution (5/7-day intervals). The KF estimations indicate that the integrated higher-resolution time series achieves high accuracy, with an RMSE of 0.431 cm and an R² of 0.974 compared to GNSS. Finally, to overcome the limitation of single-point modeling, a novel LDP-ConvFormer is constructed for enhanced spatiotemporal LDP. The Spatiotemporal Displacement Prediction Transformer (STDP-Former) employs Local Spatial Multi-Head Self-Attention (LSMHSA) and Temporal Multi-Head Self-Attention (TMHSA) to capture the displacement dependencies between different spatial locations at the same time steps and temporal relationships across different time steps, respectively. Additionally, the spatiotemporal feature map is decomposed into trend and periodic components, which are modeled separately and then summed for final predictions. Experimental results demonstrate that the constructed model can accurately establish the nonlinear relationship between the landslide displacement and its triggering factors. The LDP-ConvFormer outperforms benchmark methods, achieving RMSE: 46.29 mm, MAE: 26.7 mm, SSIM: 0.8187, PSNR: 35.62, R²: 0.9574, and EVar: 0.9603. Moreover, LDP-ConvFormer shows notable superiority in LDP over medium to long periods (60-90d) in the TGRA. The enhanced spatiotemporal LDP method provides extremely valuable reference for LEWS of translational landslides in the TGRA.

利用多时相干涉合成孔径雷达（MT-InSAR）技术在三峡库区滑坡预警系统（LEWS）中实现滑坡位移预测（LDP）是一项重大挑战。一方面，卫星的重访频率有限，无法满足LEWS的高频监测需求。另一方面，传统的LDP方法侧重于单点建模。它忽略了位移点与滑坡面之间的空间相关性。为了增强低频MT-InSAR观测，本文提出了一种将Kalman Filter （KF）和LDP- conformer相结合的混合算法，实现增强的时空LDP。首先，将多轨道MT-InSAR测量结果转化为下坡位移。随后，利用KF对多轨道下坡位移进行积分，生成时间分辨率更高（5/7天间隔）的时间序列数据。KF估计表明，与GNSS相比，整合后的高分辨率时间序列具有较高的精度，RMSE为0.431 cm， R2为0.974。最后，为了克服单点建模的局限性，构造了一种新的LDP- convformer，用于增强时空LDP。时空位移预测转换器（STDP-Former）利用局部空间多头自注意（LSMHSA）和时间多头自注意（TMHSA）分别捕捉同一时间步长不同空间位置之间的位移依赖关系和不同时间步长之间的时间关系。此外，将时空特征图分解为趋势分量和周期分量，分别建模，然后进行汇总预测。实验结果表明，所建立的模型能较准确地建立滑坡位移与其触发因素之间的非线性关系。LDP-ConvFormer优于基准方法，实现RMSE: 46.29 mm, MAE: 26.7 mm, SSIM: 0.8187, PSNR: 35.62, R2: 0.9574, EVar: 0.9603。此外，LDP- convformer在TGRA中长期（60-90d）表现出显著的LDP优势。改进的时空LDP方法为该区平动滑坡的LEWS提供了极有价值的参考。

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引用次数: 0

National mapping of wetland vegetation leaf area index in China using hybrid model with Sentinel-2 and Landsat-8 data 基于Sentinel-2和Landsat-8数据混合模型的中国湿地植被叶面积指数全国制图

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2025-12-01 DOI: 10.1016/j.isprsjprs.2025.11.031

Jianing Zhen , Dehua Mao , Yeqiao Wang , Junjie Wang , Chenwei Nie , Shiqi Huo , Hengxing Xiang , Yongxing Ren , Ling Luo , Zongming Wang

Leaf area index (LAI) of wetland vegetation provides vital information for its growth condition, structure and functioning. Accurately mapping LAI at a broad scale is essential for conservation and rehabilitation of wetland ecosystem. However, owing to the spatial complexity and periodic inundation characteristics of wetland vegetation, retrieving LAI of wetlands remains a challenging task with significant uncertainty. Here, with 865 in-situ measurements across different wetland biomes in China during 2013–2023, we proposed a hybrid strategy that incorporated active learning (AL) technique, physically-based PROSAIL-5B model, and Random Forest machine learning algorithm to map wetland biomes LAI across China from Sentinel-2 and Landsat-8 imagery. The validation results showed that the hybrid approach outperformed physically-based and empirically-based methods and achieved higher accuracy (R² increased by 0.15–0.40, RMSE decreased by 0.02–0.27, and RRMSE reduced by 3.37–12.78 %). Additionally, three indices that we newly-developed (TBVI5, TBVI3 and TBVI1) exhibited superior potential for LAI inversion across different types of wetland vegetation. Our mapping results exhibited spatial details and consistency, and matched with in-situ observations from Sentinel-2 compared to Landsat-8 and the other MODIS-based products. In this study, we developed the first national-scale mapping of wetland vegetation LAI in China. The findings offer insights into accurate retrieval of LAI in wetland vegetation, providing valuable support for the scientific restoration of wetlands and assessing their responses to climate change.

湿地植被叶面积指数（LAI）是湿地植被生长状况、结构和功能的重要信息。在大尺度上准确绘制LAI对湿地生态系统的保护和恢复至关重要。然而，由于湿地植被的空间复杂性和周期性淹没特征，湿地LAI的反演仍然是一项具有挑战性的任务，具有很大的不确定性。本文基于2013-2023年中国不同湿地生物群落的865个原位测量数据，提出了一种结合主动学习（AL）技术、基于物理的PROSAIL-5B模型和随机森林机器学习算法的混合策略，利用Sentinel-2和Landsat-8图像绘制中国湿地生物群落LAI。验证结果表明，混合方法优于物理方法和经验方法，获得了更高的准确率（R2提高0.15 ~ 0.40，RMSE降低0.02 ~ 0.27，RRMSE降低3.37 ~ 12.78%）。此外，我们新开发的3个指数TBVI5、TBVI3和TBVI1在不同类型湿地植被的LAI反演中表现出较好的潜力。与Landsat-8和其他基于modis的产品相比，我们的制图结果显示了空间细节和一致性，并且与Sentinel-2的原位观测结果相匹配。在这项研究中，我们开发了中国第一个国家尺度的湿地植被LAI制图。研究结果为湿地植被LAI的精确反演提供了新的思路，为湿地的科学恢复和评估其对气候变化的响应提供了有价值的支持。

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引用次数: 0

Explainable spatiotemporal deep learning for subseasonal super-resolution forecasting of Arctic sea ice concentration during the melting season 基于可解释时空深度学习的北极海冰融化季亚季节超分辨率预报

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2025-12-01 DOI: 10.1016/j.isprsjprs.2025.11.027

Jianxin He , Yuxin Zhao , Shuo Yang , Woping Wu , Jian Wang , Xiong Deng

Accurate, high-resolution forecasting of Arctic sea-ice concentration (SIC) during the melting season is crucial for climate monitoring and polar navigation, yet remains hindered by the system’s complex, multi-scale, and cross-sphere dynamics. We present MSS-STFormer, an explainable multi-scale spatiotemporal Transformer designed for subseasonal SIC super-resolution forecasting. The model integrates 14 environmental factors spanning the ice, ocean, and atmosphere, and incorporates four specialized modules to enhance spatiotemporal representation and physical consistency. Trained with OSTIA satellite observations and ERA5 reanalysis data, MSS-STFormer achieves high forecasting skill over a 60-day horizon, yielding an RMSE of 0.049, a correlation of 0.9951, an SSIM of 0.9603, and a BACC of 0.9656. Post-hoc explainability methods, Gradient SHAP and LIME—reveal that the model captures a temporally evolving prediction mechanism: early forecasts are dominated by persistence of initial conditions, mid-term phases are governed by atmospheric dynamics such as wind and pressure, and later stages transition to a coupled influence of radiative and dynamic processes. This progression aligns closely with established thermodynamic and dynamic theories of sea-ice evolution, underscoring the model’s ability to identify physically meaningful drivers. The framework demonstrates strong potential for advancing explainable GeoAI in Earth observation, combining predictive accuracy with physical explainability for operational Arctic SIC monitoring and climate applications.

融冰季北极海冰浓度（SIC）的准确、高分辨率预报对气候监测和极地导航至关重要，但仍受到系统复杂、多尺度和跨球体动力学的阻碍。我们提出了MSS-STFormer，一个可解释的多尺度时空转换器，设计用于亚季节SIC超分辨率预测。该模型集成了14个环境因子，包括冰、海洋和大气，并结合了四个专门的模块，以增强时空表征和物理一致性。在OSTIA卫星观测数据和ERA5再分析数据的训练下，MSS-STFormer在60天范围内具有较高的预测能力，RMSE为0.049，相关系数为0.9951，SSIM为0.9603，BACC为0.9656。事后可解释性方法、Gradient SHAP和lime表明，该模式捕获了一种时间演化的预测机制：早期预报受初始条件的持续影响，中期阶段受风和气压等大气动力学的影响，后期阶段过渡到辐射和动力过程的耦合影响。这一进展与已建立的海冰演化热力学和动力学理论密切相关，强调了该模型识别物理上有意义的驱动因素的能力。该框架展示了在地球观测中推进可解释GeoAI的强大潜力，将北极SIC监测和气候应用的预测精度与物理可解释性相结合。

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引用次数: 0

Mapping melliferous tree species in Kenya via one-class classification with hyperspectral unsupervised domain adaptation 通过高光谱无监督域适应的一类分类绘制肯尼亚蜜科树种

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 Epub Date: 2026-01-08 DOI: 10.1016/j.isprsjprs.2025.12.028

Zhaozhi Luo , Janne Heiskanen , Ilja Vuorinne , Ian Ocholla , Shiqi Zhang , Saana Järvinen , Xinyu Wang , Yanfei Zhong , Petri Pellikka

The beekeeping sector holds significant potential for livelihood diversification among the agropastoral communities in Kenya. Melliferous tree species play a critical role by providing essential nectar sources for bees. However, limited knowledge of their precise spatial distributions constrains the full development of beekeeping. One-class classification (OCC) offers a practical solution for detecting single target species without requiring extensive labeled data from other classes. Although existing OCC methods perform well in trained domains, the generalization capability to unseen domains remains limited due to domain shift. To address these challenges, this study proposes a hyperspectral unsupervised domain adaptation OCC framework (HyUDA-One) for tree species mapping using airborne hyperspectral imagery and laser scanning data. The spatial–spectral regularized pseudo-positive learning was designed to mitigate domain shift and improve model generalizability. The effectiveness of HyUDA-One was demonstrated by mapping three key melliferous tree species in two savanna landscapes in southern Kenya. The results show that HyUDA-One significantly improves performance in unlabeled domains. The F1-scores of 0.788, 0.845, and 0.768 were achieved for Senegalia mellifera, Vachellia tortilis, and Commiphora africana in the trained domain, respectively. In the untrained domain, the F1-scores of Senegalia mellifera and Vachellia tortilis were 0.756 and 0.884, respectively. The distribution maps revealed the spatial patterns of these melliferous tree species and the nectar source availability, offering an important reference for sustainable beekeeping development in savanna landscapes. Furthermore, the proposed framework can potentially be extended to other mapping applications, such as invasive species detection.

养蜂业对肯尼亚农牧社区的生计多样化具有巨大潜力。蜜科树种通过为蜜蜂提供必需的花蜜来源而发挥关键作用。然而，对其精确空间分布的有限了解限制了养蜂业的充分发展。单类分类（OCC）为检测单个目标物种提供了实用的解决方案，而不需要从其他类中获得大量标记数据。虽然现有的OCC方法在训练域中表现良好，但由于域移位，对未知域的泛化能力受到限制。为了解决这些挑战，本研究提出了一个高光谱无监督域自适应OCC框架（HyUDA-One），用于利用航空高光谱图像和激光扫描数据进行树种制图。设计了空间-频谱正则化伪正学习以减轻域漂移，提高模型的可泛化性。HyUDA-One的有效性通过在肯尼亚南部的两个稀树草原上绘制三种关键的蜜树物种来证明。结果表明，HyUDA-One显著提高了未标记域的性能。Senegalia mellifera、Vachellia tortilis和Commiphora africana在训练域的f1得分分别为0.788、0.845和0.768。在非训练域，塞内加尔和玉米饼的f1得分分别为0.756和0.884。该分布图揭示了这些蜜科树种的空间分布格局和花蜜来源的有效性，为热带稀树草原景观的可持续养蜂发展提供了重要参考。此外，所提出的框架可以扩展到其他测绘应用，如入侵物种检测。

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引用次数: 0