International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"GFS-Net: Geometry–frequency synergistic fusion for accurate optical–SAR land-cover classification","authors":"Hongkang Tao,&nbsp;Qin Huang,&nbsp;Teng Zhong,&nbsp;Zan Yang,&nbsp;Haobo Qiu,&nbsp;Chen Jiang","doi":"10.1016/j.jag.2026.105549","DOIUrl":"10.1016/j.jag.2026.105549","url":null,"abstract":"<div><div>High-precision land-cover classification (LCC) from remote sensing imagery provides valuable semantic information. This information is critical for intelligent navigation and environmental understanding in autonomous unmanned systems. Optical imagery provides rich spectral and texture information, while synthetic aperture radar (SAR) offers all-weather sensing and strong structural characterization. The effective fusion of optical and SAR data plays a vital role in LCC, but it is hindered by heterogeneous imaging mechanisms that introduce geometric misalignment and frequency-domain discrepancies in the fusion process. To address these challenges, this paper proposes a geometry–frequency synergistic fusion network (GFS-Net) for accurate optical–SAR land-cover classification. By jointly integrating a Swin Transformer-based hierarchical dual-stream encoder with a carefully designed decoder that incorporates semantic flow alignment and local-context embedding, GFS-Net enables accurate multi-scale feature alignment and fine-grained detail recovery. At the core of the network, a geometry–frequency synergistic fusion module explicitly combines deformable geometric alignment with frequency-domain amplitude-phase fusion. This module corrects cross-modal spatial offsets, suppresses SAR speckle noise, and preserves discriminative optical structures. Furthermore, a dual-domain gated recalibration module is introduced to adaptively suppress modality-specific redundancy and enhance complementary semantic responses through global gating and joint spatial–channel rectification. Extensive experiments on the WHU-OPT-SAR and DFC2025 datasets show that GFS-Net achieves 84.96% OA and 58.64% mIoU on WHU-OPT-SAR and 87.02% OA and 74.66% mIoU on DFC2025, outperforming the strongest compared method CFFormer by +1.14% and +0.44% mIoU, respectively.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105549"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying high-value landing ellipses on Mars using a continuous optimization framework with suitability map","authors":"Rong Wang,&nbsp;Yongjiu Feng,&nbsp;Yuhao Wang,&nbsp;Yi Zhou,&nbsp;Panli Tang,&nbsp;Jingbo Sun,&nbsp;Yusheng Xu,&nbsp;Chao Wang,&nbsp;Sicong Liu,&nbsp;Zhen Ye,&nbsp;Xiaohua Tong","doi":"10.1016/j.jag.2026.105564","DOIUrl":"10.1016/j.jag.2026.105564","url":null,"abstract":"<div><div>For Mars landing site selection, it is of great engineering significance to identify landing ellipses that not only satisfy the geometric constraints of entry, descent, and landing (EDL) but also offer high scientific value. In this study, we develop an optimization framework that transforms landing ellipse identification from predefined discrete candidate evaluation into a suitability-map-driven spatial optimization problem. Within this framework, a Particle Swarm Optimization-based Search (PSOS) strategy is introduced as a representative optimization strategy to identify high-value landing ellipses in a continuous suitability map. The performance of PSOS is evaluated against four discrete search strategies: Threshold-based Search (TS), Moving Ellipse Search (MES), Global Random Search (GRS), and High-value Local Search (HLS). The results show that the PSOS can effectively identify high-suitability landing ellipses (suitability &gt; 0.77) with solution consistency comparable to MES (Jaccard index ≈ 0.6), while exhibiting lower variability across independent runs than stochastic search methods such as GRS and HLS. Furthermore, the optimization effort of PSOS can be adjusted through algorithm parameters, enabling a flexible trade-off between solution quality and computational cost when exact global optimality is not required. The proposed framework provides a complementary approach to exhaustive search for automated landing ellipse identification and supports iterative mission analysis for future Mars landing site selection.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105564"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assimilating GRACE observations into a hydrological model in the Yangtze River Basin: The role of the Three Gorges Reservoir","authors":"Jun Huang,&nbsp;Yuhao Xiong,&nbsp;Wei Feng,&nbsp;Fan Yang,&nbsp;Hongbing Bai,&nbsp;Min Zhong","doi":"10.1016/j.jag.2026.105577","DOIUrl":"10.1016/j.jag.2026.105577","url":null,"abstract":"<div><div>Assimilating terrestrial water storage anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) mission into hydrological models can improve the representation of water storage variations. However, when anthropogenic water storage changes are not explicitly represented in the model, GRACE data assimilation (GRACE-DA) may redistribute unmodeled signals into individual storage components, introducing compensatory biases. Here, we implement a regional GRACE-DA framework over the Yangtze River Basin using the Local Ensemble Kalman Filter and conduct twin experiments by assimilating original GRACE-derived TWSA and TWSA corrected for the Three Gorges Reservoir (TGR) storage signal. This design allows us to assess how unmodeled reservoir storage variations propagate into hydrological components and fluxes during assimilation. Results show that GRACE-DA improves TWSA simulations and corrects the erroneous TWSA trend around the TGR region, but this improvement in total water storage does not necessarily translate into better component-level simulations. Moreover, assimilating the original TWSA incorrectly assigns TGR-related mass changes to model storage components, especially groundwater and soil storage, thereby further exacerbating their degradation. Instead, correcting the TGR storage signal before assimilation reduces this physical inconsistency and generally improves agreement with independent groundwater, soil moisture, runoff, and evapotranspiration data. These findings highlight that maintaining consistency between GRACE-observed mass changes and model-represented storage components is essential for obtaining reliable hydrological estimates in basins strongly affected by human regulation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105577"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148936100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameter-efficient multimodal adaptation of vision foundation models for remote sensing semantic segmentation","authors":"Haocheng Li,&nbsp;Juepeng Zheng,&nbsp;Shuangxi Miao,&nbsp;Ruibo Lu,&nbsp;Guosheng Cai,&nbsp;Haohuan Fu,&nbsp;Jianxi Huang","doi":"10.1016/j.jag.2026.105520","DOIUrl":"10.1016/j.jag.2026.105520","url":null,"abstract":"<div><div>Multimodal remote sensing semantic segmentation improves scene understanding by integrating complementary information from heterogeneous Earth observation data. Although pretrained Vision Foundation Models (VFMs) provide strong general-purpose representations, adapting them to multimodal tasks typically requires updating large modality-specific encoders, and optimization tends to be dominated by the optical modality, leaving auxiliary cues under-utilized. To address these challenges, we propose MoBaNet, a parameter-efficient multimodal adaptation framework that keeps the VFM backbone frozen and concentrates trainable capacity on three complementary components. First, the Cross-modal Prompt-Injected Adapter (CPIA) generates semantic prompts from paired modalities and injects them into lightweight bottleneck adapters to enable cross-modal interaction under the frozen backbone. Second, the Difference-Guided Gated Fusion Module (DGFM) exploits cross-modal discrepancy to guide channel-wise and spatially adaptive feature fusion. Third, the training-only Modality-Conditional Random Masking (MCRM) strategy locally corrupts one modality at a time and applies hard-pixel auxiliary supervision to modality-specific branches, encouraging more robust use of complementary cues. Extensive experiments on the ISPRS Vaihingen and Potsdam benchmarks show that MoBaNet achieves the best overall performance among the compared methods, while its default DINOv2-ViT-B configuration uses only 5.43% trainable parameters and remains robust under partial modality degradation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105520"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148855158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual geographical scene modeling towards geospatial digital twins: A case study of urban highway tunnel scenarios","authors":"Jinbin Zhang,&nbsp;Jun Zhu,&nbsp;Bowen Yang,&nbsp;Zhihao Guo,&nbsp;Ranran Tang,&nbsp;Tao Jiang,&nbsp;Jigang You","doi":"10.1016/j.jag.2026.105561","DOIUrl":"10.1016/j.jag.2026.105561","url":null,"abstract":"<div><div>As a virtual component of the geospatial digital twin (GDT), the virtual geographic environment (VGE) has significant advantages in enhancing human geographic cognition and analyzing and solving practical engineering problems. However, existing virtual geographic scene modeling methods still have significant gaps compared to GDT, making it difficult to balance the ability to represent dynamic objects as required by GDT while preserving the geographic characteristics of VGE, such as spatiotemporal reference and multiscale representation. Against this backdrop, this paper takes highway tunnel scenes as an example. It proposes a modeling framework that leverages the synergy between perception data and scene knowledge to construct a virtual geographic scene for GDT. First, we constructed a multi-domain integrated knowledge graph of highway tunnel scenes to enhance the standardization and completeness of the modeling process. Second, we established a dynamic object detection network based on an improved YOLOv8 model, endowing the virtual geographic scene with the capability to represent dynamic objects. Third, we proposed a tunnel scene twin modeling method that integrates a knowledge graph with a perception network, enabling effective fusion of the static base and dynamic objects. Finally, we selected a tunnel in China as the study area, developed a prototype system, and conducted experimental analyses. The results demonstrate that the proposed method successfully constructs a GDT-oriented virtual geographic scene for highway tunnels. For the city-level model, over 93.6 % of points have an error of less than 0.1 m; for the component-level model, over 95.4 % of points have an error of less than 0.01 m; the model’s semantic accuracy reaches 95 %; the average modeling time for dynamic targets is 11.8 ms; and the average rendering efficiency of the virtual scene is 133.2 FPS. Furthermore, compared to existing scene modeling methods, the constructed scene integrates advantages such as geospatial and temporal references, multiscale representation, and dynamic object representation. Research results provide an example reference for intelligent management of urban traffic, and provide new ideas for virtual geographical scene modeling.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105561"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Filtering GEDI footprints based on combined spatial-spectral characteristics for individual building height mapping in 12 major global cities","authors":"Xiao Ma,&nbsp;Yajie Qu,&nbsp;Hexiang Wang,&nbsp;Meiyuan Chen,&nbsp;Chuyi Yang,&nbsp;Guang Zheng,&nbsp;Chi Xu,&nbsp;Yixi Bi","doi":"10.1016/j.jag.2026.105566","DOIUrl":"10.1016/j.jag.2026.105566","url":null,"abstract":"<div><div>Building height is an essential variable for describing the urban vertical landscape and evaluating urban resilience to natural disasters and climate change adaptation. Building-scale height maps are key to quantifying urban structures across multiple relevant scales (e.g., building, block, and landscape scales). Although some studies have filled in missing building height data using various methods, the scarcity of temporally consistent and spatially balanced building height samples continues to limit mapping accuracy. This is particularly true in the Global South, where urban vertical data remain scarce. In this study, we developed an easy-to-use mapping framework to produce building-scale heights for global major cities in 2020 by combining active and passive remotely sensed data. Specifically, we proposed a novel approach for generating building-scale height samples through integrating Global Ecosystem Dynamics Investigation (GEDI) data and building footprints. Combining spatial-spectral characteristics effectively mitigated the impact of positional offsets in GEDI footprints during sample construction. Twelve cities at different stages of development were selected as the study area. Validation results indicated good agreement between derived samples and reference data (<em>r</em> = 0.87; RMSE = 5.78 m). Random forest models calibrated with local samples, neighboring high-value samples, and variables derived from building footprints as well as Sentinel-1 and Sentinel-2 data produced accurate building height maps (<em>r</em> = 0.81; RMSE = 11.48 m). Our framework outperforms existing products in accuracy stability, alleviating the global scarcity of building-scale height samples. This contributes to building-scale height data availability and reliability.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105566"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A factorial spatial–temporal null-model framework for satellite–ground lightning fusion across Chinese marginal seas","authors":"Nengxing Guo,&nbsp;Li Cai,&nbsp;Wenzhao Lu,&nbsp;Yingpu Xie,&nbsp;Yuheng Niu,&nbsp;Tao Li,&nbsp;Chun Zhao,&nbsp;Shanqiang Gu","doi":"10.1016/j.jag.2026.105552","DOIUrl":"10.1016/j.jag.2026.105552","url":null,"abstract":"<div><div>Earth observation increasingly combines satellite and ground systems that record different manifestations of the same phenomenon. Spatial clustering and temporal persistence inflate apparent agreement, creating uncertainty over where joint products are statistically supported. Marine lightning is a stringent case because offshore energy, shipping, and coastal infrastructure depend on geostationary optical imagers and coastal low-frequency networks. We develop a four-state factorial null framework that evaluates FY-4A Lightning Mapping Imager–ground-network correspondence under observed, spatially randomized, temporally displaced, and jointly randomized states across four Chinese marginal seas and 215 coastal VLF/LF stations. Spatial and temporal storm backgrounds interact non-additively, contributing 11.5–32.8 percentage points of correspondence after block-bootstrap propagation of spatial autocorrelation. Combining this interaction — rather than any single bias-corrected match rate — with distance support and multi-year stability yields joint summaries in the Bohai and Yellow seas, local calibration in the East China Sea, and parallel optical and radio evidence in the northern South China Sea. These classes remain stable across 20 matching criteria and six summers. Independent datasets over six non-Chinese coasts reproduce the factorial structure, while calendar-date randomization reduces the interaction towards zero. Controlled event loss and location error contract fusion support, and timing displacement adds 1.6–2.7 percentage points of apparent matches. The framework defines a transferable decision sequence for satellite–ground observing pairs with different observation entities, common support, countable correspondence, and clustered persistence. This study demonstrates the sequence for marine lightning; matching windows, support limits, coefficients, and application classes remain locally estimated.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105552"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping selective logging with Sentinel-1 image time series and a novel transformer-based model","authors":"Xinyao Huang,&nbsp;Raian Vargas Maretto,&nbsp;Leila Maria Garcia Fonseca,&nbsp;Alfred Stein,&nbsp;Claudio Persello","doi":"10.1016/j.jag.2026.105567","DOIUrl":"10.1016/j.jag.2026.105567","url":null,"abstract":"<div><div>Selective logging (SL) is a major driver of forest degradation, yet its small and short-lived canopy openings remain difficult to detect in persistently cloudy tropical regions. Optical approaches miss many events, while synthetic aperture radar (SAR) time series are challenging to model with architectures that either fuse multivariate features too early or smooth over transient signals. We present InterFormer, a Transformer-based architecture that jointly models feature interactions and temporal dynamics via a feature block with Multi-head Intercross Self Attention (MISA) and a temporal block with Multi-head Self Dissimilar Attention (MSDA). Texture features were further utilized as complementary input. Since precise SL labels are unavailable, the model was trained and internally tested using a weak-label benchmark, in which pixel labels were extracted within the official published Real-Time Deforestation Detection System (DETER) polygons. To complement this internal weak-label benchmark, we designed two independent practical experiments to evaluate the model performance in low-intensity Annual Production Units (UPAs) based on field-inventory data and at the municipality scale (<span><math><mo>&gt;</mo></math></span> 30,000 km²) in the Brazilian Amazon. In the independent UPA validation, InterFormer localized 92.65% of tree-falling gaps in UPA areas and outperformed standard Transformer variants and Planet-based alerts from the Brazilian Forest Service by approximately 20% at the tree level. At the municipality level, our predicted alert polygons achieved the highest Detection Rate against DETER across multiple reference-overlap thresholds in both Paragominas and Feliz Natal, while also producing fewer unmatched alerts than RAdar for Detecting Deforestation (RADD) and Tropical Moist Forest dataset (TMF). However, substantial over-expansion of predicted alert regions remained. These results suggest that InterFormer provides a useful framework for representing weak and short-lived SL signals in Sentinel-1 time series, and that openly available SAR data have the potential to support large-scale SL monitoring under weak supervision. Nevertheless, the current municipality-scale workflow still relies on optical-derived masking as a supporting step, and the development of a fully SAR-only large-area monitoring framework remains an important direction for future work.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105567"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CDBNet: Detail-preserving and structure-aware pipeline mapping in chemical industrial parks","authors":"Tianhao Wang,&nbsp;Shixin Wang,&nbsp;Futao Wang,&nbsp;Suju Li,&nbsp;Litao Wang,&nbsp;Zhenqing Wang,&nbsp;Xingguang Gu,&nbsp;Ziqi Nie,&nbsp;Zhaowei Wang,&nbsp;Chengyue Xiong,&nbsp;Jinfeng Zhu,&nbsp;Wenliang Liu","doi":"10.1016/j.jag.2026.105529","DOIUrl":"10.1016/j.jag.2026.105529","url":null,"abstract":"<div><div>Accurate maps of chemical industrial park pipelines are important for infrastructure inventory, safety supervision, emergency response, and maintenance planning, yet pipeline networks are difficult to delineate from high-resolution optical images because they are narrow, low-texture, partially occluded, and embedded in visually complex industrial backgrounds. This study develops CDBNet, a detail-preserving and structure-aware deep network for pipeline mapping from Earth observation images. A frozen DINOv3 ViT-L backbone pretrained on satellite imagery is used as a transferable feature extractor. A cross-layer detail-preserving foundation pyramid adapter converts multi-layer transformer tokens into high-resolution pyramid features, a direction-aware aggregation module enhances elongated structures in horizontal, vertical, oblique, and curved layouts, and a boundary-skeleton-guided refinement module improves contour localization and centerline continuity. We also construct PipelineRS, a pixel-level annotated dataset from GF-2 and GF-7 images covering four chemical industrial parks in China. On PipelineRS, CDBNet achieves 0.9499 precision, 0.9365 recall, 0.9432 F1-score, 0.8925 foreground IoU, 0.9460 mean IoU, and 0.9029 average boundary localization accuracy. Cross-dataset tests on Massachusetts and DeepGlobe road datasets, robustness experiments, and independent cross-regional evaluations further show that the proposed method can produce stable geospatial information for slender industrial infrastructure mapping.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105529"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Guided Machine learning approach for estimating spatiotemporally seamless FAPAR from Landsat surface reflectance","authors":"Yimin Ni,&nbsp;Gaofei Yin,&nbsp;Guodong Zhang,&nbsp;Gang Sun,&nbsp;Xiaodan Wu,&nbsp;Xiaojun Li,&nbsp;Xinyan Liu,&nbsp;Anxin Ding,&nbsp;Rui Chen,&nbsp;Meilian Wang,&nbsp;Yi Zhang,&nbsp;Jiangchuan Hu,&nbsp;Zongyan Li,&nbsp;Aleixandre Verger","doi":"10.1016/j.jag.2026.105544","DOIUrl":"10.1016/j.jag.2026.105544","url":null,"abstract":"<div><div>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a key biophysical parameter for characterizing vegetation photosynthetic efficiency, playing a crucial role in understanding terrestrial ecosystems, global carbon cycles, and climate change impacts. While satellite observations enable global-scale FAPAR estimation, existing medium to low-resolution products (250 m-1 km) cannot adequately capture surface heterogeneity, particularly in regions with complex terrain and diverse vegetation types. This study presents a novel physics-guided machine learning approach for high-resolution FAPAR retrieval. First, we established a coupled soil–vegetation–atmosphere radiative transfer model to simulate Landsat surface reflectance and FAPAR at global representative pixels. Second, we integrated simulated reflectance-simulated FAPAR and actual reflectance-actual FAPAR to construct a Physics-Guided Light Gradient Boosting Machine (PGLGBM) for retrieving clear-sky FAPAR from Landsat surface reflectance. Third, we incorporated spatio-temporal constraints and environmental variables to build a second LGBM model for reconstructing missing Landsat FAPAR under cloudy conditions. The method was validated against ground measurements, including Ground Based Observations for Validation (GBOV) and ImagineS, achieving high spatial–temporal consistency (GBOV: R² = 0.793, RMSE = 0.113 and ImagineS: R² = 0.856, RMSE = 0.110) and demonstrating robust performance across diverse landscapes. This approach effectively addresses the limitations of traditional methods regarding insufficient physical constraints and cloud contamination, while providing continuous 30 m-resolution FAPAR products at 16-day intervals. The resulting high-resolution FAPAR dataset offers valuable support for studying ecosystem light use efficiency, simulating vegetation productivity, and assessing carbon cycle dynamics.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105544"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148855160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}