Remote Sensing of Environment最新文献

{"title":"Rapid thinning of lake ice for Himalayan glacial lakes since 2010","authors":"Meimei Zhang ,&nbsp;Fang Chen ,&nbsp;Weigui Guan ,&nbsp;Hang Zhao","doi":"10.1016/j.rse.2025.115062","DOIUrl":"10.1016/j.rse.2025.115062","url":null,"abstract":"<div><div>Lake ice, a significant indicator of global warming, plays a crucial role in regulating regional hydroclimate and maintaining lake ecosystem balance, in particular for the fragile high-mountain environment in Asia. However, the spatiotemporal variability of ice thickness in glacial lakes remains elusive due to limited and inconsistent observations, as well as the lack of a comprehensive glacial lake ice model that effectively couples ice layer dynamics with multiple physical fields, such as atmosphere and lake water. Although efforts have been made in applying lake ice model based on climate model outputs, the estimation of lake ice thickness largely overlooks the actual frozen lake conditions and remains constrained to highly glaciated regions. Therefore, a systematical method for automatically extracting ice-covered area and estimating the ice thickness in glacial lakes is critically needed. Here we employed SDGSAT-1 MII images to meticulously delineate ice coverage regions, and harnessed CryoSat-2 waveforms to derive ice thickness in glacial lakes across Himalaya. Then we proposed a novel lake ice model that was cross-validated by altimetric measurements, with a Pearson correlation coefficient (CC) of 0.85 and RMSE of 0.25 m for the whole Himalaya, to give a reliable estimation of ice thickness for 64 Himalayan glacial lakes large than 1 km². The maximum mean ice thickness during 2010–2024 is 2.5 m, observed in Western Himalaya. Approximately 80 % of lakes are experiencing statistically significant reductions in ice thickness. The fastest decrease in lake ice thickness occurs in the Eastern Himalaya (up to 0.08 m/yr), and the thinning rates in the Western and Central Himalayas are comparatively lower, with the maximum values of 0.04 m/yr and 0.07 m/yr, respectively. Further investigations show that the associated lower ice is primarily driven by the notably rising temperature and accelerated glacier ablation. This research enhances the interpretations of SDGSAT-1 imagery signal from frozen glacial lakes, offering new possibilities for broader applications of SDGSAT-1 in cryospheric studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115062"},"PeriodicalIF":11.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229802","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":"The first gap-free 20 m 5-day LAI/FAPAR products over China (2018–2023) from integrated Landsat-8/9 and Sentinel-2 Analysis Ready Data","authors":"Han Ma ,&nbsp;Qian Wang ,&nbsp;WenYuan Li ,&nbsp;Yongzhe Chen ,&nbsp;Jianglei Xu ,&nbsp;Yichuan Ma ,&nbsp;Jianxi Huang ,&nbsp;Shunlin Liang","doi":"10.1016/j.rse.2025.115048","DOIUrl":"10.1016/j.rse.2025.115048","url":null,"abstract":"<div><div>Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are essential land variables for environmental monitoring and climate modeling. High resolution (≤30 m) gap-free LAI/FAPAR products are in high demand, but frequent cloud contaminations in optical data cause substantial data gaps. To address the ill-posed nature of land surface variable inversion by leveraging time-series information instead of traditional pixel-based inversions, this study presents a temporal deep learning model that jointly estimates gap-free, 20 m/5-day LAI/FAPAR from integrated Landsat-8/9 and Sentinel-2 sequential observations, denoted as High-resolution Global LAnd Surface Satellite (Hi-GLASS) LS20 LAI/FAPAR products, part of the Hi-GLASS level 3 product suite. A hybrid Bidirectional LSTM with an attention mechanism that synergizes multiple satellite observations effectively under different cloud cover conditions was trained on representative samples derived from GLASS LAI/FAPAR and 30 m land cover data, accounting for site heterogeneity. The algorithm was directly validated against 4046 in-situ measurements from 29 validation sites, achieving an R² of 0.79 for LAI and 0.86 for FAPAR, Root Mean Square Error (RMSE) of 1.0 for LAI and 0.155 for FAPAR. Intercomparisons with existing high and coarse resolution products showed superior continuity and accuracy. To implement the model, we constructed Landsat and Sentinel-2 Analysis Ready Data (LSARD) and generated the first 20 m gap-free LAI/FAPAR product over China from 2018 to 2023 (<span><span>www.glasss.hku.hk</span><svg><path></path></svg></span>). We also provide a web tool on Google Colab that can calculate LAI/FAPAR for any region of interest. Unlike methods that rely solely on clear-sky pixels from a single sensor, our approach enables spatiotemporally continuous and physically consistent LAI/FAPAR estimates from multiple sensors.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115048"},"PeriodicalIF":11.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262958","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":"Towards a global assessment of sandy shorelines: Systematic extraction and validation of optical satellite-derived coastal indicators at various sites","authors":"Marcan Graffin ,&nbsp;Thibault Touzé ,&nbsp;Erwin W.J. Bergsma ,&nbsp;Rafael Almar","doi":"10.1016/j.rse.2025.115033","DOIUrl":"10.1016/j.rse.2025.115033","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Satellite-based remote sensing offers unprecedented opportunities for monitoring coastal dynamics at large spatiotemporal scales through proxies such as shoreline positions. Recent advances in computational tools and the accessibility of publicly available satellite imagery have made it feasible to derive these metrics efficiently using user-defined parameters (e.g., band combinations, segmentation algorithms, and preprocessing steps). This abundance of data and tools, coupled with increasing computational power, suggests that global-scale monitoring of shoreline change is within reach. However, efforts to develop continental-to-global shoreline change datasets remain limited, partly due to challenges associated with varying environmental conditions and the lack of standardized algorithms. Yet, quantitative relationships between errors in the satellite-derived observations and environmental conditions remain unexplored. Also, the large body of available methods for satellite-derived waterline (SDW) extraction has merely been inter-compared. In this study, we built on previous works to develop a customizable open-access SDW extraction Python toolkit. We assess the influence of spectral band combination and segmentation method on the accuracy of waterline and shoreline positions derived from Sentinel-2 and Landsat optical imagery, comparing 20 SDW extraction methods in total. By evaluating the performances of these extraction methods across eight sandy coast sites using in-situ beach elevation profiles and FES2022 tide model outputs, we highlight the systematic gap between SDW performances obtained at microtidal and meso- to macrotidal environments. We found that SDW/SDS accuracy at microtidal sites is slightly influenced by the choice of band combination, while the accuracy at meso- to macrotidal sites is rather impacted by the choice of thresholding method. We also validated related satellite-derived metrics, such as long-term trends, interannual variability, and seasonal cycles of shoreline change, as well as beach slopes, and found that there are generally well captured by the top-performing SDW methods, with errors in long-term trend, interannual variability and seasonal cycle amplitude estimations around 0.8 m/yr, 2 m, and 2.5 m, respectively. Finally, we address the issue related with low signal-to-noise ratio in SDS time series, emphasizing the necessity to quantify and mitigate noise by aggregating data over time, and propose empirical laws quantifying errors in shoreline position time series as a function of the inverse beach slope and the tidal excursion, meaning tidal range normalized by the beach slope. These results provide a critical framework for first order estimation of the accuracy of satellite-derived shoreline positions derived at sites lacking validation data, and bring insightful materials to discuss methodological challenges related to large-scale, automated shoreline monitoring, highlighting strengths and limitati","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115033"},"PeriodicalIF":11.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263024","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":"Improved mapping of perennial crop types based on intra-annual biophysical changing patterns of spectral endmembers","authors":"Xiang Gao ,&nbsp;Qiyuan Hu ,&nbsp;Danfeng Sun ,&nbsp;Mariana Belgiu ,&nbsp;Fei Lun ,&nbsp;Qiangqiang Sun ,&nbsp;Zhengxin Ji ,&nbsp;Xin Jiao","doi":"10.1016/j.rse.2025.115059","DOIUrl":"10.1016/j.rse.2025.115059","url":null,"abstract":"<div><div>Perennial crops are vital to economic growth, environmental sustainability, and human well-being. However, due to the diversity and complexity of natural environments and agricultural management practices, there is currently no widely transferable mapping strategy for these crop types, particularly in regions with diverse perennial species. To address this gap, we propose a novel perennial crop mapping strategy based on intra-annual changing patterns of spectral endmembers (CPSEM). This strategy integrates a unified spectral endmember (EM) space with a harmonic model to characterize and quantify the biophysical processes and morphology of vegetation. Using Linear Spectral Mixture Analysis (LSMA), Sentinel-2 time-series data (2020−2022) were unmixed into a unified spectral EM space comprising green vegetation (GV), non-photosynthetic vegetation (NPV), soil (SL), and dark surfaces (DA), enabling the reconstruction of land surface component (LSC) trajectories at the pixel level. We developed two EM-based morphology indices to capture structural and compositional relationships among EMs. A harmonic model was applied to extract key parameters from the EM fractions and EM-based morphology indices, representing vegetation biophysical processes. Finally, a Random Forest model was used to classify perennial crop types. The results show that perennial crops of the same type exhibited similar biophysical processes and morphology, while distinct types exhibited substantial differences. Our method effectively maps perennial crop types across diverse environments and planting conditions, achieving classification accuracies of 87.27 %–90.91 %. Compared to traditional spectral-based methods, the proposed strategy improves perennial crop classification by 1.7 %–3.9 % and overall vegetation classification by 5.3 %–8.4 %. Additionally, this strategy effectively addressed the limitations inherent in traditional phenological indices for accurately classifying perennial crops, demonstrating robust performance even in complex classification scenarios. Incorporating synthetic aperture radar (SAR) features did not further improve classification accuracy. This strategy enhances interpretability and transferability through the use of a unified spectral EM space and detailed biophysical characterization. Thus, the CPSEM-based perennial crop mapping strategy provides a robust and scalable approach for accurately identifying perennial crops and land cover at large scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115059"},"PeriodicalIF":11.4,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216221","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":"A deep learning framework for mapping evergreen conifer fractional cover at 30 m resolution using fused bi-temporal WorldView and time-series Landsat imagery in mixed mountain forests","authors":"Xiao Zhu ,&nbsp;Tiejun Wang ,&nbsp;Andrew K. Skidmore ,&nbsp;Isla Duporge","doi":"10.1016/j.rse.2025.115055","DOIUrl":"10.1016/j.rse.2025.115055","url":null,"abstract":"<div><div>Evergreen conifers are key components of temperate broadleaf and mixed forests, playing a significant role in shaping ecosystem structure, function, and resilience to climate change. While very high-resolution (VHR) satellite imagery enables accurate classification of evergreen conifers and creation of reference fractional cover maps, scaling this capability to regional levels using coarser-resolution time-series satellite data remains challenging. Traditional machine learning approaches are limited by their inability to fully exploit the spatial detail of VHR imagery and capture sequential patterns in satellite time series. To address these limitations, we developed a deep learning-based framework for mapping evergreen conifer fractional cover at 30 m resolution in mountainous forests. The framework integrates a 3D U-Net model to extract spatial and spectral features from bi-temporal WorldView imagery—while mitigating terrain shadows—and a long short-term memory (LSTM) network to learn sequential dependencies from Landsat time series for regression. We compared our framework against a random forest baseline. Independent spatial and temporal transferability assessments showed that our approach achieved an R² of 0.71 and an RMSE of 0.14, outperforming the benchmark method. To further interpret the spatial predictions, we quantified the spatial configuration of evergreen conifers using landscape metrics across areas with varying conifer cover. Our findings demonstrate the value of combining multi-source, multi-resolution imagery with deep learning models tailored for spatial and temporal complexity. This framework improves the accuracy and transferability of fractional cover mapping and offers a scalable solution for ecosystem monitoring in topographically complex forested landscapes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115055"},"PeriodicalIF":11.4,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226714","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":"Volcanic sulfur dioxide monitored from a constellation of FengYun hyperspectral infrared sounders in dawn-dusk, mid-morning, and afternoon sun-synchronous orbits","authors":"Zhao-Cheng Zeng ,&nbsp;Lieven Clarisse ,&nbsp;Bruno Franco ,&nbsp;Cathy Clerbaux ,&nbsp;Nicolas Theys ,&nbsp;Chengli Qi ,&nbsp;Lu Lee ,&nbsp;Lin Zhu ,&nbsp;Xiuqing Hu ,&nbsp;Mingjian Gu ,&nbsp;Peng Zhang","doi":"10.1016/j.rse.2025.115057","DOIUrl":"10.1016/j.rse.2025.115057","url":null,"abstract":"<div><div>Satellite observations offer a unique way of monitoring the spatial distribution, vertical structure and temporal variation of volcanic sulfur dioxide (SO₂) plumes. In this study, we use observations from the Hyperspectral Infrared Atmospheric Sounder (HIRAS) constellation on board China's FengYun-3 (FY-3) meteorological satellites flying in three different sun-synchronous orbits, including dawn-dusk, mid-morning, and afternoon orbits. The constellation provides six global coverages (roughly every 4-h) each day, with equatorial overpass times at 5:30 am/pm for FY-3E, 10:00 am/pm for FY-3F, and 2:00 am/pm for FY-3D. We retrieve SO₂ total column and layer height from the Ruang volcanic eruptions in April 2024. The retrievals show consistency among the different HIRAS and are highly correlated with IASI and TROPOMI observations. The e-folding time of the volcanic SO₂ mass is estimated to be 9.0 ± 2.8 days, which is representative of a plume in the Upper Troposphere-Lower Stratosphere (UTLS). Lastly, we apply the methods to the eruptions of the Russia's Sheveluch volcano in November 2024 at high latitudes and show the effectiveness and high consistency among the HIRAS sensors in detecting the SO₂ signal. This study demonstrates the capability of a global constellation of FengYun hyperspectral infrared sounders to monitor SO₂ emissions from volcanic eruptions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115057"},"PeriodicalIF":11.4,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216232","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":"Open access battle damage detection via Pixel-Wise T-Test on Sentinel-1 imagery","authors":"Ollie Ballinger","doi":"10.1016/j.rse.2025.115025","DOIUrl":"10.1016/j.rse.2025.115025","url":null,"abstract":"<div><div>In the context of recent, highly destructive conflicts in Gaza and Ukraine, reliable estimates of building damage are essential for an informed public discourse, human rights monitoring, and humanitarian aid provision. Given the contentious nature of conflict damage assessment, these estimates must be fully <span><span>reproducible</span><svg><path></path></svg></span>, explainable, and derived from open access data. This paper introduces a new method for building damage detection– the Pixel-Wise T-Test (PWTT)– that satisfies these conditions. Using a combination of freely-available synthetic aperture radar imagery and statistical change detection, the PWTT generates accurate conflict damage estimates across a wide area at regular time intervals. Accuracy is assessed using an original dataset of over 2 million labeled building footprints spanning 30 cities across Palestine, Ukraine, Sudan, Syria, and Iraq. Despite being simple and lightweight, the algorithm achieves building-level accuracy statistics (AUC=0.87 in the full sample) rivaling state of the art methods that use deep learning and high resolution imagery. The workflow is <span><span>open source</span><svg><path></path></svg></span> and deployed entirely within the Google Earth Engine environment, allowing for the generation of interactive Battle Damage Dashboards for <span><span>Ukraine</span><svg><path></path></svg></span> and <span><span>Gaza</span><svg><path></path></svg></span> that update in near-real time, enabling the public and humanitarian practitioners to immediately get estimates of damaged buildings in a given area.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115025"},"PeriodicalIF":11.4,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216205","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":"Is satellite land surface temperature an appropriate proxy for intra-urban variability of daytime heat stress?","authors":"Ferdinand Briegel ,&nbsp;Joaquim G. Pinto ,&nbsp;Andreas Christen","doi":"10.1016/j.rse.2025.115045","DOIUrl":"10.1016/j.rse.2025.115045","url":null,"abstract":"<div><div>Adaptation of urban areas to heat extremes requires adequate information on intra-urban variability patterns of outdoor thermal comfort (OTC). Remotely sensed Land Surface Temperatures (LST) are often used to map heat hotspots in urban areas. However, this approach has limitations as LST and OTC are influenced by different physical processes. This study investigates the relationship between satellite-derived Landsat Level-2 LST data and pedestrian-level Universal Thermal Climate Index (UTCI) predictions from a microscale thermal comfort model across Freiburg, Germany. A cluster analysis of the differences is performed, and multiple random forest models are trained using different combinations of LST, ERA5-Land reanalysis, and local-specific urban morphology and land cover data as predictors.</div><div>While a linear relationship between LST and UTCI exists under non-heat stress conditions (UTCI &lt;26 °C) and in vegetated or open areas, this becomes non-linear and spatially inconsistent under heat stress, particularly in compact urban environments. The growing divergence between LST and UTCI along an urbanization gradient ranging from −1 K to +9 K highlights the significant impact of urban morphology on the LST-UTCI relationship, leading to substantial intra-urban variability. This variability appears to persist even within similar urban typologies (e.g. LCZs/clusters), with only limited reduction in spatial variability. Random forest models confirm these findings: those based solely on LST or global-scale predictors struggle to capture intra-urban UTC variability, while models incorporating local urban morphology and land cover data outperform them (even without LST input). This suggests that the contribution of LST to neighborhood-scale UTC modeling is limited under certain conditions and environments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115045"},"PeriodicalIF":11.4,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216220","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":"A novel index for directly indicating fractional vegetation cover based on spectral differences between vegetation and soil","authors":"Bangke He ,&nbsp;Wenquan Zhu ,&nbsp;Cenliang Zhao ,&nbsp;Zhiying Xie ,&nbsp;Huimin Zhuang","doi":"10.1016/j.rse.2025.115056","DOIUrl":"10.1016/j.rse.2025.115056","url":null,"abstract":"<div><div>The green fractional vegetation cover (FVC) is an essential parameter used to characterize the spatial pattern of vegetation coverage. Remote sensing provides the most efficient way to estimate FVC at regional and global scales. However, existing FVC-estimation approaches based on remote sensing fail to achieve high accuracy, broad applicability, and ease of use simultaneously, thus limiting their practical implementation. Based on the unique spectral shapes of green vegetation and soil within the visible to near-infrared spectrum (400–1000 nm), we proposed the vegetation coverage index (VCI), which is a novel index for directly indicating the FVC. VCI utilizes the spectral reflectance from the blue, green, red, and near-infrared bands to quantify the vegetation signal and soil signal as 1 and 0, respectively, and then establishes a quantitative relationship with FVC through the linear spectral mixing model. The performance of VCI in FVC estimation was first tested using simulated datasets generated by the radiative transfer model LESS under varying factors, including the vegetation structure, leaf area index, soil background, and solar zenith angle. It was then validated at 15 in-situ test sites in China, using UAV-derived reference FVC and Sentinel-2 surface reflectance data. These sites covered 10 vegetation and land cover types, 4 phenological phases, and 10 soil types. Additionally, VCI was compared against existing FVC products across another 40 in-situ comparative sites in China, using Landsat-8/9, Sentinel-3, and MODOCGA data at spatial resolutions of 30 m, 300 m, and 1000 m, respectively. Simulation results demonstrated that VCI performed comparably or slightly better than the dimidiate pixel model (DPM), reducing the root mean square error (RMSE) by 0.21 % to 14.42 %. Validation at 15 test sites showed that during the green-up to peak phase, when pixels are primarily composed of green vegetation and soil, VCI and DPM exhibited similar average accuracy (VCI: RMSE = 0.13; DPM: RMSE = 0.12). In contrast, during the peak to dormancy phase, with the presence of non-photosynthetic vegetation, VCI (RMSE = 0.11) clearly outperformed DPM (RMSE = 0.21), achieving a 46.8 % reduction in RMSE. At the 40 comparative sites, VCI yielded RMSE comparable to the MultiVI FVC product and outperformed the GEOV3 FVC and GLASS FVC products, with RMSE reductions of 20.00 % and 30.77 %, respectively. VCI provides a simple and efficient approach for FVC estimation through basic spectral band calculations. Moreover, VCI demonstrates broad applicability across widely used remote sensing sensors, including the Sentinel-2 Multispectral Instrument, Sentinel-3 Ocean and Land Colour Instrument, Landsat-8 Operational Land Imager, and Moderate-Resolution Imaging Spectroradiometer, showing strong potential for FVC monitoring across various spatial and temporal scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115056"},"PeriodicalIF":11.4,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216122","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":"Global reconstruction of three decades of fine-grained nighttime light data with analysis of large-scale infrastructure and landmarks","authors":"Jinyu Guo ,&nbsp;Feng Zhang ,&nbsp;Hang Zhao ,&nbsp;Baoxiang Pan ,&nbsp;Linlu Mei","doi":"10.1016/j.rse.2025.115036","DOIUrl":"10.1016/j.rse.2025.115036","url":null,"abstract":"<div><div>Satellite-collected nighttime light provides a unique perspective on human activities, including urbanization, population growth, and epidemics. However, global, long-term, and fine-grained nighttime light observations are lacking, hindering the analysis and application of continuous light changes in specific facilities over extended periods. To address this gap, we reformulated the super-resolution task – reconstructing low-resolution nighttime light data into high-resolution images – by explicitly incorporating temporal difference information. Rigorous validation across three evaluation years demonstrates that our dataset consistently outperforms existing products at global, national, and city scales. Case studies of high-value infrastructure sites, such as artificial islands and international airports, highlight our dataset’s unprecedented ability to capture both long-term gradual illumination changes associated with development and abrupt intensity oscillations caused by wars. The dataset is available at <span><span>https://doi.org/10.5281/zenodo.15845676</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115036"},"PeriodicalIF":11.4,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216161","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}