ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Time-Series models for ground subsidence and heave over permafrost in InSAR Processing: A comprehensive assessment and new improvement","authors":"Chengyan Fan ,&nbsp;Cuicui Mu ,&nbsp;Lin Liu ,&nbsp;Tingjun Zhang ,&nbsp;Shichao Jia ,&nbsp;Shengdi Wang ,&nbsp;Wen Sun ,&nbsp;Zhuoyi Zhao","doi":"10.1016/j.isprsjprs.2025.02.019","DOIUrl":"10.1016/j.isprsjprs.2025.02.019","url":null,"abstract":"<div><div>InSAR is an effective tool for indirectly monitoring large-scale hydrological-thermal dynamics of the active layer and permafrost by detecting the surface deformation. However, the conventional time-series models of InSAR technology do not consider the distinctive and pronounced seasonal characteristics of deformation over permafrost. Although permafrost-tailored models have been developed, their performance relative to the conventional models has not been assessed. In this study, we modify sinusoidal function and Stefan-equation-based models (permafrost-tailored) to better characterize surface deformation over permafrost, and assess advantages and limitations of these models for three application scenarios: filling time-series gaps for Small Baseline Subset (SBAS) inversion, deriving velocity and amplitude of deformation and selecting reference points automatically. The HyP3 interferograms generated from Sentinel-1 are utilized to analyze the surface deformation of the permafrost region over the upper reaches of the Heihe River Basin from 2017 to 2023. The result shows that adding a semi-annual component to the sinusoidal function can better capture the characteristics of ground surface deformation in permafrost regions. The modified Stefan-equation-based model performs well in those application scenarios, but it is only recommended for complex scenarios that conventional mathematical models cannot handle or for detailed simulations at individual points due to sophisticated data preparation and high computational cost. Furthermore, we find reference points can introduce substantial uncertainties into the deformation velocity and amplitude measurements, in comparison to the uncertainties derived from interferograms alone. The analysis of deformation amplitude and inter-annual velocity reveals that an ice-rich permafrost region, exhibiting a seasonal amplitude of 50–130 mm, is experiencing rapid degradation characterized by a subsidence velocity ranging from −10 to −20 mm/yr. Our study gives a permafrost-tailored modification and quantitative assessment on the InSAR time-series models. It can also serve as a reference and promotion for the application of InSAR technology in future permafrost research. The dataset and code are available at <span><span>https://github.com/Fanchengyan/FanInSAR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 167-185"},"PeriodicalIF":10.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bounding box versus point annotation: The impact on deep learning performance for animal detection in aerial images","authors":"Zeyu Xu ,&nbsp;Tiejun Wang ,&nbsp;Andrew K. Skidmore ,&nbsp;Richard Lamprey ,&nbsp;Shadrack Ngene","doi":"10.1016/j.isprsjprs.2025.02.017","DOIUrl":"10.1016/j.isprsjprs.2025.02.017","url":null,"abstract":"<div><div>Bounding box and point annotations are widely used in deep learning-based animal detection from remote sensing imagery, yet their impact on model performance and training efficiency remains insufficiently explored. This study systematically evaluates the influence of these two annotation methods using aerial survey datasets of African elephants and antelopes across three commonly employed deep learning networks: YOLO, CenterNet, and U-Net. In addition, we assess the effect of image spatial resolution and the training efficiency associated with each annotation method. Our findings indicate that when using YOLO, there is no statistically significant difference in model accuracy between bounding box and point annotations. However, for CenterNet and U-Net, bounding box annotations consistently yield significantly higher accuracy compared to point-based annotations, with these trends remaining consistent across different spatial resolution ranges. Furthermore, training efficiency varies depending on the network and annotation method. While YOLO exhibits similar convergence speeds for both annotation types, U-Net models trained with bounding box annotations converge significantly faster, followed by CenterNet, where bounding box-based models also show improved convergence. These findings demonstrate that the choice of annotation method should be guided by the specific deep learning architecture employed. While point-based annotations are more cost-effective, their lower training efficiency in U-Net and CenterNet suggests that bounding box annotations are preferable when maximizing both accuracy and computational efficiency. Therefore, when selecting annotation strategies for animal detection in remote sensing applications, researchers should carefully balance detection accuracy, annotation cost, and training efficiency to optimize performance for specific task requirements.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 99-111"},"PeriodicalIF":10.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing LiDAR point cloud generation with BRDF-based appearance modelling","authors":"Alfonso López,&nbsp;Carlos J. Ogayar,&nbsp;Rafael J. Segura,&nbsp;Juan C. Casas-Rosa","doi":"10.1016/j.isprsjprs.2025.02.010","DOIUrl":"10.1016/j.isprsjprs.2025.02.010","url":null,"abstract":"<div><div>This work presents an approach to generating LiDAR point clouds with empirical intensity data on a massively parallel scale. Our primary aim is to complement existing real-world LiDAR datasets by simulating a wide spectrum of attributes, ensuring our generated data can be directly compared to real point clouds. However, our emphasis lies in intensity data, which conventionally has been generated using non-photorealistic shading functions. In contrast, we represent surfaces with Bidirectional Reflectance Distribution Functions (BRDF) obtained through goniophotometer measurements. We also incorporate refractivity indices derived from prior research. Beyond this, we simulate other attributes commonly found in LiDAR datasets, including RGB values, normal vectors, GPS timestamps, semantic labels, instance IDs, and return data. Our simulations extend beyond terrestrial scenarios; we encompass mobile and aerial scans as well. Our results demonstrate the efficiency of our solution compared to other state-of-the-art simulators, achieving an average decrease in simulation time of 85.62%. Notably, our approach introduces greater variability in the generated intensity data, accounting for material properties and variations caused by the incident and viewing vectors. The source code is available on GitHub (<span><span>https://github.com/AlfonsoLRz/LiDAR_BRDF</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 79-98"},"PeriodicalIF":10.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LuoJiaHOG: A hierarchy oriented geo-aware image caption dataset for remote sensing image–text retrieval","authors":"Yuanxin Zhao ,&nbsp;Mi Zhang ,&nbsp;Bingnan Yang ,&nbsp;Zhan Zhang ,&nbsp;Jujia Kang ,&nbsp;Jianya Gong","doi":"10.1016/j.isprsjprs.2025.02.009","DOIUrl":"10.1016/j.isprsjprs.2025.02.009","url":null,"abstract":"<div><div>Image–text retrieval (ITR) is crucial for making informed decisions in various remote sensing (RS) applications, including urban development and disaster prevention. However, creating ITR datasets that combine vision and language modalities requires extensive geo-spatial sampling, diverse categories, and detailed descriptions. To address these needs, we introduce the LuojiaHOG dataset, which is geospatially aware, label-extension-friendly, and features comprehensive captions. LuojiaHOG incorporates hierarchical spatial sampling, an extensible classification system aligned with Open Geospatial Consortium (OGC) standards, and detailed caption generation. Additionally, we propose a CLIP-based Image Semantic Enhancement Network (CISEN) to enhance sophisticated ITR capabilities. CISEN comprises dual-path knowledge transfer and progressive cross-modal feature fusion. The former transfers multimodal knowledge from a large, pretrained CLIP-like model, while the latter enhances visual-to-text alignment and fine-grained cross-modal feature integration. Comprehensive statistics on LuojiaHOG demonstrate its richness in sampling diversity, label quantity, and description granularity. Evaluations of LuojiaHOG using various state-of-the-art ITR models–including ALBEF, ALIGN, CLIP, FILIP, Wukong, GeoRSCLIP, and CISEN-employ second- and third-level labels. Adapter-tuning shows that CISEN outperforms others, achieving the highest scores with WMAP@5 rates of 88.47% and 87.28% on third-level ITR tasks, respectively. Moreover, CISEN shows improvements of approximately 1.3% and 0.9% in WMAP@5 over its baseline. When tested on previous RS ITR benchmarks, CISEN achieves performance close to the state-of-the-art methods. Pretraining on LuojiaHOG can further enhance retrieval results. These findings underscore the advancements of CISEN in accurately retrieving relevant information across images and texts. LuojiaHOG and CISEN can serve as foundational resources for future research on RS image–text alignment, supporting a broad spectrum of vision-language applications. The retrieval demo and dataset are available at:<span><span>https://huggingface.co/spaces/aleo1/LuojiaHOG-demo</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 130-151"},"PeriodicalIF":10.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photogrammetric system of non-central refractive camera based on two-view 3D reconstruction","authors":"Zhen Wu ,&nbsp;Mingshu Nan ,&nbsp;Haidong Zhang ,&nbsp;Junzhou Huo ,&nbsp;Shangqi Chen ,&nbsp;Guanyu Chen ,&nbsp;Zhang Cheng","doi":"10.1016/j.isprsjprs.2025.02.016","DOIUrl":"10.1016/j.isprsjprs.2025.02.016","url":null,"abstract":"<div><div>Due to the harsh construction environment of tunnels, the visual system must be fitted with a sphere cover of a certain thickness. The visual system with an optical sphere cover invalidates conventional measurement methods. Therefore, this paper provides a comprehensive visual measurement method using spherical glass refraction. First, the spherical glass refraction imaging is modeled using a geometry-driven camera model. Second, a three-parameter calibration method for the optical center offset unit vector, incident optical path offset distance, and optical center offset distance was proposed to accurately characterize refractive distortion. Then, a dynamic interval (DI) based on angle and depth constraints is introduced, and a DI-SGM algorithm utilizing the semi-global stereo matching method is developed to solve the polar constraint failure problem under refraction. Finally, an improved binocular parallax method that uses refraction image pairs is proposed and demonstrated to be effective and stable under spherical refraction using effectiveness and comprehensive data experiments. The constructed DI has narrow characteristics. The imaging model presented in this paper has an average space reconstruction error of only 0.087 mm. The maximum measurement error for sphere center distance is only 0.157 mm, which is comparable in accuracy to the case with no refraction. The proposed method provides an effective approach for applying visual measurement methods under refractive effects, thereby improving the visual system’s reliability in tunnel environments.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 112-129"},"PeriodicalIF":10.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Discrepancy Masked Distillation for remote sensing object detection","authors":"Cong Li,&nbsp;Gong Cheng,&nbsp;Junwei Han","doi":"10.1016/j.isprsjprs.2025.02.006","DOIUrl":"10.1016/j.isprsjprs.2025.02.006","url":null,"abstract":"<div><div>Knowledge distillation (KD) has become a promising technique for obtaining a performant student detector in remote sensing images by inheriting the knowledge from a heavy teacher detector. Unfortunately, not every pixel contributes (even detrimental) equally to the final KD performance. To dispel this problem, the existing methods usually derived a distillation mask to stress the valuable regions during KD. In this paper, we put forth Adaptive Discrepancy Masked Distillation (ADMD), a novel KD framework to explicitly localize the beneficial pixels. Our approach stems from the observation that the feature discrepancy between the teacher and student is the essential reason for their performance gap. With this regard, we make use of the feature discrepancy to determine which location causes the student to lag behind the teacher and then regulate the student to assign higher learning priority to them. Furthermore, we empirically observe that the discrepancy masked distillation leads to loss vanishing in later KD stages. To combat this issue, we introduce a simple yet practical weight-increasing module, in which the magnitude of KD loss is adaptively adjusted to ensure KD steadily contributes to student optimization. Comprehensive experiments on DIOR and DOTA across various dense detectors show that our ADMD consistently harvests remarkable performance gains, particularly under a prolonged distillation schedule, and exhibits superiority over state-of-the-art counterparts. Code and trained checkpoints will be made available at <span><span>https://github.com/swift1988</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 54-63"},"PeriodicalIF":10.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the satellite instrument visibility range for detecting underwater targets","authors":"Jun Chen ,&nbsp;Wenting Quan ,&nbsp;Xianqiang He ,&nbsp;Ming Xu ,&nbsp;Caipin Li ,&nbsp;Delu Pan","doi":"10.1016/j.isprsjprs.2025.02.013","DOIUrl":"10.1016/j.isprsjprs.2025.02.013","url":null,"abstract":"<div><div>To assess the ability of a satellite instrument to detect submerged targets, we constructed a semi-analytical relationship to link target reflectance and the contrast threshold of the satellite instrument to visibility ranges. Using numerical simulation, we found that the contrast threshold of the satellite instrument was equal to 50 % of the residual error contained in satellite <em>R</em>_rs data. We evaluated our model using known sea depths of optically shallow water and found that the model produced ∼ 16 % uncertainty in retrieving the visibility range around the edge of the optically shallow water. By comparison, the contrast threshold of the human eye was more than 20 times larger than the satellite instrument contrast threshold. In addition, using a Secchi disk submerged in the shallow water, we found that the Secchi disk was invisible to the human eye when the disk was still visible to a high-quality camera handheld or mounted on an unmanned aerial vehicle. Moreover, when the image data quality was as well as MODIS instrument, we found that the maximum instrument visibility range reached 130 m in theory, which was approximately four times larger than the maximum reached by the human eye. Our findings suggest that high-quality cameras such as satellite instruments are more effective than the human eye for detecting underwater targets.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 64-78"},"PeriodicalIF":10.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ensemble learning framework for generating high-resolution regional DEMs considering geographical zoning","authors":"Xiaoyi Han,&nbsp;Chen Zhou,&nbsp;Saisai Sun,&nbsp;Chiying Lyu,&nbsp;Mingzhu Gao,&nbsp;Xiangyuan He","doi":"10.1016/j.isprsjprs.2025.02.007","DOIUrl":"10.1016/j.isprsjprs.2025.02.007","url":null,"abstract":"<div><div>The current digital elevation model super-resolution (DEM SR) methods are unstable in regions with significant spatial heterogeneity. To address this issue, this study proposes a regional DEM SR method based on an ensemble learning strategy (ELSR). Specifically, we first classified geographical regions into 10 zones based on their terrestrial geomorphologic conditions to reduce spatial heterogeneity; we then integrated the global terrain features with local geographical zoning for terrain modeling; finally, based on ensemble learning theory, we integrated the advantages of different networks to improve the stability of the generated results. The approach was tested for 46,242 km² in Sichuan, China. The total accuracy of the regional DEM (stage 3) improved by 2.791 % compared with that of the super-resolution convolutional neural network (SRCNN); the accuracy of the geographical zoning strategy results (stage 2) increased by 1.966 %, and that of the baseline network results (stage 1) increased by 0.950 %. Specifically, the improvement in each stage compared with the previous stage was 110.105 % (in stage 2) and 41.963 % (in stage 3). Additionally, the accuracy of the 10 terrestrial geomorphologic classes improved by at least 2.000 %. In summary, the strategy proposed herein is effective for improving regional DEM resolution, with an improvement in relative accuracy related to terrain relief. This study creatively integrated geographical zoning and ensemble learning ideas to generate a stable, high-resolution regional DEM.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 363-383"},"PeriodicalIF":10.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal ensemble of UAV-borne hyperspectral, thermal, and RGB imagery to identify combined nitrogen and water deficiencies in field-grown sesame","authors":"Maitreya Mohan Sahoo ,&nbsp;Rom Tarshish ,&nbsp;Yaniv Tubul ,&nbsp;Idan Sabag ,&nbsp;Yaron Gadri ,&nbsp;Gota Morota ,&nbsp;Zvi Peleg ,&nbsp;Victor Alchanatis ,&nbsp;Ittai Herrmann","doi":"10.1016/j.isprsjprs.2025.02.011","DOIUrl":"10.1016/j.isprsjprs.2025.02.011","url":null,"abstract":"<div><div>Hyperspectral reflectance as well as thermal infrared emittance unmanned aerial vehicle (UAV)-borne imagery are widely used for determining plant status. However, they have certain limitations to distinguish crops subjected to combined environmental stresses such as nitrogen and water deficiencies. Studies on combined stresses would require a multimodal analysis integrating remotely sensed information from a multitude of sensors. This research identified field-grown sesame plants’ combined nitrogen and water status when subjected to these treatment combinations by exploiting the potential of multimodal remotely sensed dataset. Sesame (<em>Sesamum indicum</em> L.; indeterminate crop) was grown under three nitrogen regimes: low, medium, and high, combined with two irrigation treatments: well-watered and water limited. With the removal of high nitrogen treated sesame plots due to adverse effects on crop development, the effects of combined treatments were analyzed using remotely acquired dataset- UAV-borne sesame canopy hyperspectral at 400 – 1020 nm, red–green–blue, thermal infrared imagery, and contact full range hyperspectral reflectance (400 – 2350 nm) of youngest fully developed leaves in the growing season. Selected leaf traits- leaf nitrogen content, chlorophyll <em>a</em> and b, leaf mass per area, leaf water content, and leaf area index were measured on ground and estimated from UAV-borne hyperspectral dataset using genetic algorithm inspired partial least squares regression models (R² ranging from 0.5 to 0.9). These estimated trait maps were used to classify the sesame plots for combined treatments with a 40 – 55 % accuracy, indicating its limitation. The reduced separability among the combined treatments was resolved by implementing a multimodal convolutional neural network classification approach integrating UAV-borne hyperspectral, RGB, and normalized thermal infrared imagery that enhanced the accuracy to 65 – 90 %. The ability to remotely distinguish between combined nitrogen and irrigation treatments was demonstrated for field-grown sesame based on the availability of ground truth data, combined treatments, and the developed ensembled multimodal timeline modeling approach.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"222 ","pages":"Pages 33-53"},"PeriodicalIF":10.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking remotely sensed growth-related canopy attributes to interannual tree-ring width variations: A species-specific study using Sentinel optical and SAR time series","authors":"Vahid Nasiri ,&nbsp;Paweł Hawryło ,&nbsp;Piotr Tompalski ,&nbsp;Bogdan Wertz ,&nbsp;Jarosław Socha","doi":"10.1016/j.isprsjprs.2025.02.002","DOIUrl":"10.1016/j.isprsjprs.2025.02.002","url":null,"abstract":"<div><div>Tree ring width (TRW) is crucial for assessing biomass increments, carbon uptake, forest productivity, and forest health. Due to the limitations involved in measuring TRW, utilizing canopy attributes based on vegetation indices (VIs) offers a promising alternative. This study investigated the species-specific relationship between the VIs derived from the Sentinel optical (Sentinel-2) and SAR (Sentinel-1) time series and TRW. For each of the seven dominant Central European tree species, we aimed to identify the most suitable VI that shows the strongest relationship with the interannual variation in TRW. We also developed species-specific models using the random forest (RF) approach and a variety of VIs to predict TRW. Additionally, the impact of detrending TRW on its correlation with VIs and on the accuracy of TRW modeling was assessed. The results showed that the VIs that had the strongest correlation with TRW differed among the analyzed tree species. The results confirmed our hypothesis that the use of novel VIs, such as the green normalized difference vegetation index (GNDVI), or red-edge-based VIs can increase our ability to detect growth-related canopy attributes. Among all the models constructed based on raw and detrended TRWs, 12–39 % of the annual variance in TRW was explained by the integrated optical and SAR-based features. Comparing the raw and detrended TRWs indicated that detrending is necessary for certain species, even in short-term studies (i.e., less than 6 years). We concluded that Sentinel-based VIs can be used to improve the understanding of species-specific variation in forest growth over large areas. These results are useful for modeling and upscaling forest growth, as well as for assessing the effect of extreme climate events, such as droughts, on forest productivity.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 347-362"},"PeriodicalIF":10.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}