ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"The U. V. Helava Award – Best Paper Volumes 207–218 (2024)","authors":"Qihao Weng Ph.D., MAE, AAAS/IEEE/AAG/ASPRS/AAIA Fellow (ChairProfessor &Global STEM Scholar,Editor-in-Chief, ISPRS Journal of Photogrammetry and Remote Sensing)","doi":"10.1016/j.isprsjprs.2025.05.018","DOIUrl":"10.1016/j.isprsjprs.2025.05.018","url":null,"abstract":"","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 315-316"},"PeriodicalIF":10.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139482","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":"CPVF: vectorization of agricultural cultivation field parcels via a boundary–parcel multi-task learning network in ultra-high-resolution remote sensing images","authors":"Xiuyu Liu,&nbsp;Jinshui Zhang,&nbsp;Yaming Duan,&nbsp;Jiale Zhou","doi":"10.1016/j.isprsjprs.2025.05.019","DOIUrl":"10.1016/j.isprsjprs.2025.05.019","url":null,"abstract":"<div><div>Accurate recognition and vectorization of agricultural cultivation field parcels (CFP) are crucial for agricultural monitoring. However, the diverse sizes and shapes of parcels, inherent blurriness of boundaries, and adhesion of densely distributed parcels pose considerable challenges in extracting complete and separable parcels from high-resolution imagery. To address these issues, we propose an end-to-end cultivated parcel vectorization framework (CPVF) based on a boundary-parcel multi-task learning model. The CPVF comprises two components: the model introduced in this paper for CFP extraction, termed the drone-based cultivation parcel extraction multitask learning model (DCP-MTL), and the universal vectorization module (UVM) for post-processing. The model combines region, boundary, and distance tasks with a discrete cosine transform module for frequency domain feature extraction and an ensemble decoding block. The ensemble decoding block with deep-supervision, enhancing parcel region separability and boundary connectivity in complex and densely packed parcel scenarios. The UVM incorporates region–boundary interaction and topological relation-based hanging line extension to repair broken boundaries. Experiments on a newly developed the first large-scale ultra-high-resolution (UHR) dataset show that our method achieves a region IoU of 92.88 %, boundary IoU of 60.94 %, and over-segmentation and under-segmentation rates of 17.5 % and 20.7 %, respectively. The proposed method outperforms BsiNet by improving region and boundary IoU by 9.08 % and 20.6 %, respectively, and reducing over- and under-segmentation by 5.7 % and 7 %. We assessed the model’s transferability across ten regions and various farmland landscapes, demonstrating stable generalization. Ablation studies and comparisons confirmed that CPVF provides precise and effective CFP vectorization in diverse and complex farmland scenarios.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 267-299"},"PeriodicalIF":10.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134652","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":"NeSF-Net: Building roof and facade segmentation based on neighborhood relationship awareness and scale-frequency modulation network for high-resolution remote sensing images","authors":"Yuan Zhou,&nbsp;Wanshou Jiang,&nbsp;Bin Wang","doi":"10.1016/j.isprsjprs.2025.05.025","DOIUrl":"10.1016/j.isprsjprs.2025.05.025","url":null,"abstract":"<div><div>Building information extraction holds significant application value in smart city development, urban planning, and management. With the accelerating process of urbanization, mid- and high-rise buildings are increasingly prevalent. In orthophotos, the roofs of tall buildings often do not fully overlap with their footprints. In satellite images from oblique angles, buildings may also be obstructed or affected by shadows. Therefore, building information extraction should evolve from a roof-only extraction task to a comprehensive task that includes both roofs and facades. Current methods predominantly employ convolutional neural networks (CNNs) and Transformer models, focusing on describing building boundary and global features. However, these methods have the following limitations: insufficient utilization of information between pixels and limited spatial information recovery capabilities in decoders. This makes it difficult to distinguish between roofs and facades, and the morphological structure of buildings is challenging to maintain. To address these issues, this paper proposes a new network architecture—NeSF-Net, designed to focus on the accurate extraction of roofs and facades. NeSF-Net consists of two core modules: the neighborhood relationship awareness module (NRAM) and the scale-frequency modulation decoder (SFMD). NRAM enhances the connectivity between pixels by constructing sub-neighborhood relationship awareness in the latent space of deep features, effectively improving the integrity of the segmentation results. SFMD significantly reduces the loss of spatial information during the upsampling process by thoroughly extracting and integrating the scale and frequency features of buildings in the decoder. Experiments were conducted on the BANDON dataset, which contains images captured from oblique angles. The proposed method achieved a mIoU of 72.71 % and an F1 score of 83.04 %, outperforming state-of-the-art segmentation methods. The performance in facade extraction was particularly notable, with a mIoU score exceeding the second-best method by 4.92 %. Additionally, generalization experiments were conducted using GaoFen-7 satellite images, taking Shenzhen as a case study. The results demonstrate that the proposed method exhibits good generalization and robustness.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 247-266"},"PeriodicalIF":10.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134651","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":"Robust cutting plane pure integer programming phase unwrapping algorithm considering fringe frequency for dual-baseline InSAR","authors":"Hui Liu ,&nbsp;Shiji Yang ,&nbsp;Changwei Miao ,&nbsp;Junguo Liu ,&nbsp;Xuemei Liu ,&nbsp;Xianlin Liu ,&nbsp;Jiawei Yue ,&nbsp;Geshuang Li ,&nbsp;Mengyuan Zhu","doi":"10.1016/j.isprsjprs.2025.05.017","DOIUrl":"10.1016/j.isprsjprs.2025.05.017","url":null,"abstract":"<div><div>Multi-baseline (MB) phase unwrapping (PU) represents the core processing step of MB InSAR, which overcomes discontinuous terrain height estimation. However, MBPU faces the challenge of low noise robustness. A new robust cutting plane–pure integer programming (CP-PIP) PU algorithm that considers the fringe frequency for dual-baseline InSAR is proposed to address this problem. First, we establish a closed-region PIP model with one objective function and two constraints using prior information about the fringe frequency and the relationship between the interferometric phase difference and the same relative elevation. Then, after determining secant equations, we begin bidirectional traversal with the integer solution of one ambiguity number variable to verify whether another ambiguity number variable satisfies the integer condition using the CP-PIP method. Finally, two-neighborhood phase gradients in both the azimuth and range directions are introduced to extract abrupt topographic change points, and the ambiguity number of the mis-unwrapping point at the center of the window function is replaced by that with the highest frequency to complete the PU. Comprehensive comparisons using both simulated data and real data confirm the effectiveness, universality, and reliability of our approach. Compared with the most effective minimum-cost flow (MCF) algorithm in single-baseline (SB) PU and the two-stage programming approach (TSPA) in MBPU, the proposed algorithm not only achieves better and more stable unwrapping in the region of abrupt phase change and dense interferometric fringes but also improves the unwrapping accuracy. The root mean square error of the proposed algorithm is less than those of MCF and TSPA algorithms by more than 60% and 10%, respectively. The full implementation of CP-PIP is publicly available <span><span>https://github.com/Shiji-Yang/MBPU.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 300-314"},"PeriodicalIF":10.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139481","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":"SpecDETR: A transformer-based hyperspectral point object detection network","authors":"Zhaoxu Li ,&nbsp;Wei An ,&nbsp;Gaowei Guo ,&nbsp;Longguang Wang ,&nbsp;Yingqian Wang ,&nbsp;Zaiping Lin","doi":"10.1016/j.isprsjprs.2025.05.005","DOIUrl":"10.1016/j.isprsjprs.2025.05.005","url":null,"abstract":"<div><div>Hyperspectral target detection (HTD) aims to identify specific materials based on spectral information in hyperspectral imagery and can detect extremely small-sized objects, some of which occupy a smaller than one-pixel area. However, existing HTD methods are developed based on per-pixel binary classification, neglecting the three-dimensional cube structure of hyperspectral images (HSIs) that integrates both spatial and spectral dimensions. The synergistic existence of spatial and spectral features in HSIs enable objects to simultaneously exhibit both, yet the per-pixel HTD framework limits the joint expression of these features. In this paper, we rethink HTD from the perspective of spatial–spectral synergistic representation and propose hyperspectral point object detection as an innovative task framework. We introduce SpecDETR, the first specialized network for hyperspectral multi-class point object detection, which eliminates dependence on pre-trained backbone networks commonly required by vision-based object detectors. SpecDETR uses a multi-layer Transformer encoder with self-excited subpixel-scale attention modules to directly extract deep spatial–spectral joint features from hyperspectral cubes. During feature extraction, we introduce a self-excited mechanism to enhance object features through self-excited amplification, thereby accelerating network convergence. Additionally, SpecDETR regards point object detection as a one-to-many set prediction problem, thereby achieving a concise and efficient DETR decoder that surpasses the state-of-the-art (SOTA) DETR decoder. We develop a simulated hyper<em>S</em>pectral <em>P</em>oint <em>O</em>bject <em>D</em>etection benchmark termed SPOD, and for the first time, evaluate and compare the performance of visual object detection networks and HTD methods on hyperspectral point object detection. Extensive experiments demonstrate that our proposed SpecDETR outperforms SOTA visual object detection networks and HTD methods. Our code and dataset are available at <span><span>https://github.com/ZhaoxuLi123/SpecDETR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 221-246"},"PeriodicalIF":10.6,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131067","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":"Aboveground biomass mapping of Canada with SAR and optical satellite observations aided by active learning","authors":"Shuhong Qin ,&nbsp;Hong Wang ,&nbsp;Cheryl Rogers ,&nbsp;José Bermúdez ,&nbsp;Ricardo Barros Lourenço ,&nbsp;Jingru Zhang ,&nbsp;Xiuneng Li ,&nbsp;Jenny Chau ,&nbsp;Piotr Tompalski ,&nbsp;Alemu Gonsamo","doi":"10.1016/j.isprsjprs.2025.05.022","DOIUrl":"10.1016/j.isprsjprs.2025.05.022","url":null,"abstract":"<div><div>National forest inventory (NFI) data has become an indispensable reference for model training and validation when estimating forest aboveground biomass (AGB) using satellite observations. However, obtaining statistically sufficient NFI data for model training is challenging for countries with vast land areas and extensive forest coverage like Canada. This study aims to directly upscale all available NFI data into high-resolution (30-m) spatially continuous AGB and explicit uncertainties maps across Canada’s treed land, using seasonal Sentinel 1&amp;2 and yearly mosaic of L-band Synthetic Aperture Radar (SAR) observations. To address the poor performance with limited training dataset, failure to extrapolate prediction beyond the bound of the training dataset and cannot provide spatially explicit uncertainties that are inherent to the commonly used Random Forest (RF) model, the Gaussian Process Regression (GPR) model and active learning optimization was introduced. The models were trained using stratified 10-fold cross-validation (ST10CV) and optimized by Euclidean distance-based diversity with bidirectional active learning (EBD-BDAL) before extrapolated on the Google Earth Engine (GEE) platform. The GPR model optimized with EBD-BDAL estimated Canada’s 2020 treed land AGB at 40.68 ± 6.8 Pg, with managed and unmanaged forests accounting for 82 % and 18 %, respectively. Trees outside forest ecosystems account for 2 % (0.8 Pg AGB) of total AGB in Canada’s treed land, and there are 0.1134 Pg AGB within urban treed lands. The uncertainty analysis showed that the GPR model demonstrated superior extrapolation capability for high AGB forests while maintaining lower relative uncertainty. The ST10CV results showed that the GPR model performed better than RF with or without EBD-BDAL optimization. The proposed NFI upscaling framework based on the GPR model and EBD-BDAL optimization shows great potential for national AGB mapping based on limited NFI data and seasonal satellite observations.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 204-220"},"PeriodicalIF":10.6,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123938","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":"Deep learning and machine learning enable broad-scale woodland height, cover, and biomass mapping","authors":"Michael J. Campbell ,&nbsp;Jessie F. Eastburn ,&nbsp;Simon C. Brewer ,&nbsp;Philip E. Dennison","doi":"10.1016/j.isprsjprs.2025.05.016","DOIUrl":"10.1016/j.isprsjprs.2025.05.016","url":null,"abstract":"<div><div>Accurate, spatially explicit quantification of vegetation structure in drylands can improve our understanding of the important role that these critical ecosystems play in the Earth system. In semiarid woodland settings, remote sensing of vegetation structure is challenging due to low tree height, cover, and greenness as well as limited spatial and temporal availability of airborne lidar data. These limitations have hindered the development of remote sensing applications in globally widespread and ecologically important dryland systems. In this study, we implement a U-Net convolutional neural network capable of predicting per-pixel, lidar-derived vegetation height in piñon-juniper woodlands using widely available, high-resolution aerial imagery. We used this imagery and modeled canopy height data to construct random forest models for predicting tree density, canopy cover, and live aboveground biomass. Trained and validated on a field dataset that spanned diverse portions of the vast range of piñon-juniper woodlands in the southwestern US, our models demonstrated high performance according to both variance explained (R²_density = 0.45; R²_cover = 0.80; R²_biomass = 0.61) and predictive error (%RMSE_density = 57; %RMSE_cover = 19; %RMSE_biomass = 42). A comparative analysis revealed that, while performance was somewhat lower than models driven solely by airborne lidar, they vastly exceeded that of models driven by aerial imagery alone or a combination of Landsat, topography, and climate data. Although the structural predictive maps featured some artifacts from illumination and perspective differences inherent to aerial imagery, this workflow represents a viable pathway for spatially exhaustive and temporally consistent vegetation structure mapping in piñon-juniper and other dry woodland ecosystems.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 187-203"},"PeriodicalIF":10.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115469","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":"ProFiT: A prompt-guided frequency-aware filtering and template-enhanced interaction framework for hyperspectral video tracking","authors":"Yuzeng Chen ,&nbsp;Qiangqiang Yuan ,&nbsp;Yuqi Tang ,&nbsp;Xin Wang ,&nbsp;Yi Xiao ,&nbsp;Jiang He ,&nbsp;Ziyang Lihe ,&nbsp;Xianyu Jin","doi":"10.1016/j.isprsjprs.2025.05.008","DOIUrl":"10.1016/j.isprsjprs.2025.05.008","url":null,"abstract":"<div><div>Hyperspectral (HSP) video data can offer rich spectral-spatial–temporal information crucial for capturing object dynamics, attenuating the drawbacks of classical unimodal and multi-modal tracking. Current HSP tracking arts often suffer from feature refinements and information interactions, sealing the ceiling of capabilities. This study presents ProFiT, an innovative prompt-guided frequency-aware filtering and template-enhanced interaction framework for HSP video tracking, mitigating the above issues. First, ProFiT introduces a frequency-aware filtering module with adaptive filter generators to refine spectral-spatial consistency within HSP and false-color features. Then, a template-enhanced interaction module is introduced to extract complementary information for efficient cross-modal interactions. Furthermore, a token fusion module is devised to capture contextual dependencies with minimal parameters. While a temporal decoder embeds historical states, guiding to ensure temporal coherence. Comprehensive experiments across nine HSP benchmarks demonstrate that ProFiT achieves competitive accuracy, with overall precision and success rate scores of 0.870 and 0.678, respectively, along with a frame per second of 39.5. These results outperform 59 state-of-the-art trackers, establishing ProFiT as a robust solution for HSP video tracking. The code and result will be accessible at: <span><span>https://github.com/YZCU/ProFiT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 164-186"},"PeriodicalIF":10.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106730","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":"RSTeller: Scaling up visual language modeling in remote sensing with rich linguistic semantics from openly available data and large language models","authors":"Junyao Ge,&nbsp;Xu Zhang,&nbsp;Yang Zheng,&nbsp;Kaitai Guo,&nbsp;Jimin Liang","doi":"10.1016/j.isprsjprs.2025.05.002","DOIUrl":"10.1016/j.isprsjprs.2025.05.002","url":null,"abstract":"<div><div>Abundant, well-annotated multimodal data in remote sensing are pivotal for aligning complex visual remote sensing (RS) scenes with human language, enabling the development of specialized vision language models across diverse RS interpretation tasks. However, annotating RS images with rich linguistic semantics at scale demands expertise in RS and substantial human labor, making it costly and often impractical. In this study, we propose a workflow that leverages large language models (LLMs) to generate multimodal datasets with semantically rich captions at scale from plain OpenStreetMap (OSM) data for images sourced from the Google Earth Engine (GEE) platform. This approach facilitates the generation of paired remote sensing data and can be readily scaled up using openly available data. Within this framework, we present RSTeller, a multimodal dataset comprising over 1.3 million RS images, each accompanied by two descriptive captions. Extensive experiments demonstrate that RSTeller enhances the performance of multiple existing vision language models for RS scene understanding through continual pre-training. Our methodology significantly reduces the manual effort and expertise needed for annotating remote sensing imagery while democratizing access to high-quality annotated data. This advancement fosters progress in visual language modeling and encourages broader participation in remote sensing research and applications. The RSTeller dataset is available at <span><span>https://github.com/SlytherinGe/RSTeller</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 146-163"},"PeriodicalIF":10.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099702","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":"Identify and track white flower and leaf phenology of deciduous broadleaf trees in spring with time series PlanetScope images","authors":"Baihong Pan ,&nbsp;Xiangming Xiao ,&nbsp;Shanshan Luo ,&nbsp;Li Pan ,&nbsp;Yuan Yao ,&nbsp;Chenchen Zhang ,&nbsp;Cheng Meng ,&nbsp;Yuanwei Qin","doi":"10.1016/j.isprsjprs.2025.05.013","DOIUrl":"10.1016/j.isprsjprs.2025.05.013","url":null,"abstract":"<div><div>In spring, many deciduous broadleaf trees start with flower emergence and then leaf emergence, which are two key phenological events, as they signal the onset of reproduction and vegetative growth in a year. These trees provide essential resources for early pollinators searching for flowers, contribute to biodiversity, and create socio-economic benefits through tourism. Accurate detection and monitoring of the flower and leaf phenology of these trees are important. In this study we combine <em>in-situ</em> photo observations with time series satellite data in spring 2024 to develop new methods for identifying and tracking white flower and leaf phenology of Callery Pear trees, which are deciduous broadleaf trees distributed worldwide. We analyzed <em>in-situ</em> photos and surface reflectance, flower-related, and leaf-related vegetation indices from three optical satellite datasets—PlanetScope (3-m, daily), Sentinel-2 A/B (10-m, 5-day), and Harmonized Landsat and Sentinel-2 (HLS, 30-m, 2–3-day; HLSL30 and HLSS30). Time series of White Flower Index (WFI), a combination of blue, green, and red bands, delineated the flowering period (start, peak, and end dates) of white (light-colored) flowers. Time series of chlorophyll and green leaf indicator (CGLI; Blue &lt; Green &gt; Red) delineated the green leaf emergence dates of the trees (start of season, SOS). In comparison, flower and leaf phenology of these trees cannot be accurately identified and tracked by Sentinel-2 data due to insufficient number of good-quality observations and HLS data due to mixed land cover types in 30-m pixels. This study enhances our understanding of surface reflectance dynamics of flowers and green leaves of these trees in spring and demonstrates the critical role of satellite data with high spatio-temporal resolutions and WFI and CGLI algorithms in tracking floral and leaf phenology.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"226 ","pages":"Pages 127-145"},"PeriodicalIF":10.6,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083985","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}