Science of Remote Sensing最新文献

{"title":"A globally applicable deep learning model for Sentinel-2 cloud and shadow detection","authors":"Dong Luo ,&nbsp;Hankui K. Zhang ,&nbsp;Hugo De Lemos ,&nbsp;Junjie Li ,&nbsp;David P. Roy","doi":"10.1016/j.srs.2025.100278","DOIUrl":"10.1016/j.srs.2025.100278","url":null,"abstract":"<div><div>This study presents and evaluates a globally applicable cloud and shadow masking model for Sentinel-2 top-of-atmosphere (TOA) reflectance using a state-of-the-art transformer-based U-Net model (Swin-Unet) trained with nearly 20 thousand globally distributed 512 × 512 20 m pixel patches to classify each pixel as cloud, cloud shadow, or clear. The training data were compiled from publicly available annotation data, that were refined for obvious annotation errors and supplemented with additional annotations to enhance representation of underrepresented cloud and surface conditions. The trained Swin-Unet model was validated using the KappaSet and CloudSEN12+ testing datasets and compared with the Fmask, Sen2Cor scene classification layer (SCL), and a deep learning model CloudS2Mask. The Swin-Unet achieved the highest overall accuracy (91.32 %) and the highest F1-scores for cloud (0.909) and clear classes (0.935), despite a lower cloud shadow F1-score (0.691) than CloudS2Mask (0.743). The four models were also applied to 11,458 Sentinel-2 images acquired over a calendar year for 78 globally distributed 109 × 109 km tiles and the temporal smoothness index (TSI) of the time series ‘clear’ surface reflectance was derived. The Swin-Unet model yielded the smallest TSI value (most temporally consistent reflectance) for each selected Sentinel-2 band. Visual assessment confirmed the superior performance of the Swin-Unet model. The results highlight the potential of the Swin-Unet model for Sentinel-2 cloud and cloud shadow detection for images acquired anywhere and anytime. The training data and model are publicly available to enable users to apply them efficiently.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100278"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic mapping of high-resolution impervious surfaces driven by hierarchical adaptive features","authors":"Linyilin Xu,&nbsp;Genyun Sun,&nbsp;Aizhu Zhang,&nbsp;Zheng Han,&nbsp;Zheng Li,&nbsp;Yuanhao Zhao","doi":"10.1016/j.srs.2025.100300","DOIUrl":"10.1016/j.srs.2025.100300","url":null,"abstract":"<div><div>The uncontrolled expansion of impervious surfaces across the Indochina Peninsula poses serious environmental and societal challenges. Accurate monitoring of their spatiotemporal dynamics is crucial for regional sustainable development. However, existing medium-to low-resolution datasets often suffer from systematic and regionally diverse rounding errors, which significantly undermine the reliability of monitoring efforts. Here, we propose a robust and computationally efficient framework built on Google Earth Engine to automate the production of large-scale, high-resolution impervious surface datasets. First, the Phenology Enhanced Vegetation Index (PEVI) is introduced to suppress noise in automated samples, enabling a de-manualized training. Then, hierarchical adaptive features are derived from multi-scale convolution and a hierarchical strategy to advance regional heterogeneous target representation, especially in complex scenarios. Accordingly, we develop ICPIS, the first-ever impervious surface dataset with a 5-m resolution for the Indochina Peninsula, spanning the period from 2016 to 2023. Accuracy assessments show its overall accuracies of 90.69 % and 91.32 % for 2016 and 2023, with Kappa coefficients of 0.813 and 0.826. In practical applications of spatial representation and temporal monitoring, ICPIS outperforms existing high-resolution and multi-temporal datasets. This study successfully reduces rounding errors in current impervious surface mapping for the Indochina Peninsula, and enables a provision of clearer data support for stakeholders.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100300"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid unsupervised methods and inject-multiply morphological features for mapping wildfire burned areas with multi-spectral satellite data","authors":"Mohammad Esmaeili ,&nbsp;Dariush Abbasi-Moghadam ,&nbsp;Alireza Sharifi ,&nbsp;Nizom Farmonov","doi":"10.1016/j.srs.2025.100305","DOIUrl":"10.1016/j.srs.2025.100305","url":null,"abstract":"<div><div>Mapping wildfire burned areas using satellite imagery is essential for immediate response measures as well as for long-term recovery planning. These maps provide critical information to response teams, allowing them to effectively allocate resources and prioritize affected areas. This study focuses on the aim of providing accurate maps of areas affected by wildfires in the Guzli region near Bukhara province in Uzbekistan. The benchmark dataset from the study area, named UZB-WF2022, which indicates the country name and year of occurrence, and includes Sentinel-2 and Plant-Scope multispectral multi-resolution images. This study uses a mixture of unsupervised deep learning with the k-means algorithm to accurately identify and map burned areas. The core of the proposed method is an autoencoder model designed with 3-dimensional convolutional layers. This autoencoder is mixed with the k-means algorithm in the latent space of the model and uses the k-means cost function to improve the training process. In addition, the proposed method has an attention mechanism based on morphological operations called Inject-Multiply. This mechanism integrates morphological features obtained from post-wildfire vegetation index data and moisture changes captured in Sentinel-2 images, focusing on enriching features related to the shape and boundaries of burned areas. This study evaluates the effectiveness of the proposed method in labeling, identifying, and mapping burned areas using benchmark datasets, using different evaluation criteria. The model achieves an accuracy of over 93 % on the UZB-WF2022 dataset. This approach increases the accuracy of burned area detection in similar datasets and facilitates more informed decision-making for post-wildfire recovery and land management.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100305"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-enabled evaluation of forestry plantations: A comprehensive assessment of laser scanning and photogrammetric approaches","authors":"Robin J.L. Hartley ,&nbsp;Sadeepa Jayathunga ,&nbsp;Joane S. Elleouet ,&nbsp;Benjamin S.C. Steer ,&nbsp;Michael S. Watt","doi":"10.1016/j.srs.2025.100245","DOIUrl":"10.1016/j.srs.2025.100245","url":null,"abstract":"<div><div>The use of unmanned aerial vehicles (UAVs), particularly with high-density point clouds obtained through UAV laser scanning (ULS) and UAV structure from motion (UAV-SfM) techniques, offer cost-effective alternatives for forest inventory. However, the literature lacks comprehensive assessments of their limitations across diverse ranges of age classes and site conditions. This study addressed this gap by evaluating the estimation accuracy of crucial tree attributes, diameter at breast height (DBH) and tree height, in a range of age classes within forest plantations. In addition, this study thoroughly evaluated the performance of ULS and UAV-SfM in diverse site conditions using point clouds obtained from <em>Pinus radiata</em> D. Don plantations, a widely planted commercial timber species worldwide. To achieve this, UAV and field data were gathered from twelve sites, including multitemporal data for four sites. By employing an automated data processing pipeline, individual trees were segmented and structural metrics extracted from tree segments to estimate DBH and tree height at an individual tree level. Results indicated that UAV-SfM and ULS performed comparably in estimating DBH over the entire dataset, with R² values of 0.67 and 0.74 and RMSE values of 2.05 cm (11 %) and 2.13 cm (11 %) respectively. However, ULS generally outperformed UAV-SfM at the site level, achieving higher R² values (0.46–0.90 vs 0.21–0.85) and RMSE values (0.33–7.24 cm at 7–24 % vs. 0.35–6.15 cm at 8–17 %). ULS also consistently outperformed UAV-SfM in tree height measurements across sites, with an average per site RMSE of 0.68 m (5.4 %) compared with 1.21 m (11.59 %), demonstrating its robustness in diverse conditions. Site-specific factors such as stand maturity and logging debris affected measurement reliability in both datasets, with accuracy improving for younger sites, sites with a more open canopy and more favourable site conditions (less logging debris and weed cover). The study also indicated a moderate relationship between ground sampling distance (GSD) of the imagery and UAV-SfM accuracy. The findings highlight the significance of considering site-specific variables when choosing UAV technologies for conducting forest inventory, ensuring informed decisions in UAV-based forest inventory practices. Consequently, the insights gained from this research hold significant importance for practical forestry applications.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100245"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal variation and driving mechanisms of vegetation net primary productivity in Hunan Province from 2001 to 2023","authors":"Xiangyu Yin,&nbsp;Dan Cao","doi":"10.1016/j.srs.2025.100269","DOIUrl":"10.1016/j.srs.2025.100269","url":null,"abstract":"<div><div>Understanding the spatiotemporal variation in vegetation net primary productivity (NPP) and its response to natural and anthropogenic factors is essential for advancing regional ecological conservation and restoration. Therefore, this study employed the Mann–Kendall test, Sen's slope estimator, and partial correlation analysis to identify the trends and relationships between NPP and climatic factors across Hunan Province, China. Additionally, Random Forest and Geodetector models were used to evaluate the explanatory power of natural and anthropogenic factors on NPP. The following results were obtained: (1) During the study period from 2001 to 2023, the average annual NPP showed an increasing trend, with a growth rate of 2.66 gC/m²/a; (2) in high-altitude areas and the Dongting Lake Plain, average NPP was lower during 2019–2023 than that during 2001–2005, but higher in other regions. (3) in Hunan Province, NPP was more sensitive to temperature than to precipitation. Considering the lag effect of climatic factors, the temperature from the previous year showed a significant positive impact on NPP; (4) elevation had the strongest explanatory power for NPP and exhibited notable bivariate enhancement when interacting with other factors. Thus, our study provides a systematic analysis of the temporal and spatial variations in NPP, offering a scientific basis for the sustainable management of regional ecosystems.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100269"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring cropland cultivation, abandonment, fallowing and recultivation dynamics with regard to conflict intensity in war-affected Ukraine","authors":"Josef Wagner ,&nbsp;Shabarinath S. Nair ,&nbsp;Sergii Skakun ,&nbsp;Erik C. Duncan ,&nbsp;Fangjie Li ,&nbsp;Oleksandra Oliinyk ,&nbsp;Françoise Nerry ,&nbsp;Jean Rehbinder ,&nbsp;Inbal Becker-Reshef","doi":"10.1016/j.srs.2025.100326","DOIUrl":"10.1016/j.srs.2025.100326","url":null,"abstract":"<div><div>Three years of sustained shelling, mining, and active combat have caused major cropland abandonment in Ukraine, particularly along frontlines. Existing estimates of abandoned areas vary up to fourfold, due to inconsistent definitions, baselines and biased estimators of area. Many studies classify fallow land – temporarily unused but managed – as abandoned. In contrast, abandoned lands (neither cultivated nor managed) are often contaminated by unexploded ordnance, mines, or chemicals, requiring clearance before recultivation. Disentangling fallow from abandoned cropland is therefore crucial for post-war recovery planning and for determining tax relief for farmers unable to access their fields. We applied a two-level stratified random sampling design and unbiased estimators of area to quantify the extent of cultivated, fallow, and abandoned cropland. Four regions with distinct conflict dynamics were delineated using the Armed Conflict Location and Event Dataset, and within each region stratified random samples were drawn from a Planet-based cultivation status map. Between 2021 and 2024, full Ukraine's cultivated area declined by 2.5 Mha (−8.5 %). By 2024, 7 % of cropland (2.213 ± 0.256 Mha) was abandoned, of which 1.121 ± 0.148 Mha to 1.671 ± 0.169 Mha may be permanently lost to cultivation, primarily along frontlines. Fallow areas increased nationally, especially in territories reclaimed from occupation. In 2024 alone, up to 0.472 ± 0.143 Mha were recultivated, by far exceeding official land clearance figures and suggesting widespread reliance on informal or self-organized demining. These results establish a replicable framework for monitoring land-use dynamics in conflict zones, supporting evidence-based recovery, landmine clearance prioritization, and agricultural policy planning in post-war Ukraine.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100326"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid domain adaptation for disaster impact assessment: Remote sensing of building damage after the 2021 Germany floods","authors":"Victor Hertel ,&nbsp;Christian Geiß ,&nbsp;Marc Wieland ,&nbsp;Hannes Taubenböck","doi":"10.1016/j.srs.2025.100287","DOIUrl":"10.1016/j.srs.2025.100287","url":null,"abstract":"<div><div>The extent of building damage is a crucial indicator for guiding post-disaster relief strategies and rescue operations. However, diverse built environments and variations in imaging setups pose significant challenges for rapid, automated damage assessment from remote sensing data, leading to strong domain shifts and significantly reduced performance of pre-trained models. To align advanced domain adaptation techniques with the practical constraints of rapid mapping, we evaluate and propose techniques that effectively balance accuracy, resource efficiency, and operational applicability. By employing a Siamese multitask fusion network for semantic segmentation and change detection, we introduce a novel experimental approach that quantifies the influence of a priori information on domain adaptation performance. All strategies are benchmarked on a fully labeled dataset from the 2021 Germany floods. Our evaluation includes class-specific accuracy improvements, model tendencies toward over- or underestimation of damage, and resource requirements in terms of processing time, human capacity, and computational demands. Scenario-based recommendations are provided to assist in selecting the most suitable method for given conditions. All adopted techniques significantly improved model performance in a short time, achieving up to 86 % of the potential performance gain compared to supervised learning. Supervised domain adaptation with minimal annotations per class emerged as the most effective method for immediate action. Semi-supervised domain adaptation, coupled with an automatic labeling strategy based on hazard intensity, provided the highest performance improvements while maintaining low demands on time and human resources. Purely semi-supervised domain adaptation turned out time-consuming and computationally expensive, therefore advisable only under specific conditions with sufficient time or in the absence of human capacity.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100287"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drone-borne ground-penetrating radar reveals spatiotemporal moisture dynamics in peatland root zones","authors":"Maud Henrion ,&nbsp;Yanfei Li ,&nbsp;Kaijun Wu ,&nbsp;François Jonard ,&nbsp;Sophie Opfergelt ,&nbsp;Veerle Vanacker ,&nbsp;Kristof Van Oost ,&nbsp;Sébastien Lambot","doi":"10.1016/j.srs.2025.100311","DOIUrl":"10.1016/j.srs.2025.100311","url":null,"abstract":"<div><div>Peatlands are important ecosystems, providing essential ecological services, such as carbon storage and biodiversity support. However, they are endangered by degradation due to land use and climate change. Their moisture status is a key factor, as it substantially impacts carbon storage and decomposition. Therefore, it is essential to accurately characterize, map, and monitor peatland moisture. This study assessed the potential of drone-borne Ground-penetrating radar (GPR), combined with full-wave inversion, to study peatland moisture. We applied this technique to a peatland in the Belgian Hautes Fagnes previously degraded by reforestation. We conducted GPR measurements over 4.5 ha for one and a half years, producing 19 different peatland root-zone moisture maps at a 5 m resolution. Our results demonstrate that this method can track moisture changes over the study site, with an overall temporal correlation of 0.71 with ground-based moisture sensors, but is less reliable in nearly saturated areas. The spatial correlation with ground-based probes is lower (0.23), due to the high micro-variability of moisture and the use of kriging interpolation to generate maps, resulting in a spatial mismatch as GPR measurements were not collected directly above the probes. We applied statistical clustering techniques on the moisture maps to delineate homogeneous moisture classes that align well with other specific site characteristics (peat depth, vegetation types, Normalized Difference Water Index and surface temperature). This technique shows potential for planning and monitoring peatland restoration efforts and provides a new and valuable approach for peatland moisture studies to complement existing satellite- and other drone-based methods.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100311"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Debris covered glacier mapping using newly annotated multisource remote sensing data and geo-foundational model","authors":"Saurabh Kaushik ,&nbsp;Lalit Maurya ,&nbsp;Elizabeth Tellman ,&nbsp;Guoqing Zhang ,&nbsp;Jaydeo K. Dharpure","doi":"10.1016/j.srs.2025.100319","DOIUrl":"10.1016/j.srs.2025.100319","url":null,"abstract":"<div><div>The automated mapping of debris covered glaciers remains challenging due to spectral similarity between supraglacial debris (on-glaciers) and periglacial debris (off-glaciers). Deep learning offers promising capabilities, yet the lack of high-quality publicly available datasets and limited exploration of optimal model architecture constrain progress in this domain. To address this, we introduce the Global Supraglacial Debris Cover Dataset (GSDD), consisting of 1876 images (∼49,000.00 km²) collected globally from diverse glacierized regions, including High Mountain Asia, Andes, Western Canada, Alaska, and Swiss Alps, to incorporate the heterogeneity of glacial features and environments. This multisource remote sensing dataset includes 10 spectral bands—Blue, Green, Red, Near-Infrared, Shortwave Infrared (SWIR1 &amp; SWIR2), Normalized Difference Rock Index (NDRI), Slope, Elevation, and Velocity—providing critical information to distinguish glacier debris. To evaluate the efficacy of deep learning models for mapping glacier debris, we compare Prithvi Geo-Foundational Model (GFM) combined with multiple decoders, CNN-based models (UNet, Attention U-Net, and DeepLabv3+), a Vision Transformer-based model (TransNorm), and variant of the Prithvi GFM (i.e., UViT). Our results show Prithvi GFM with UperNet decoder outperformed all, achieving mIoU = 0.80 and F1-score = 0.91, surpassing DeepLabv3+ (0.71 mIoU), Attention U-Net (0.73), U-Net (0.72), TransNorm (0.71), and UViT (0.70). Our results demonstrate significant methodological advances in accurately mapping glacier termini, along with the identification of glacier snouts. Feature analysis identified the optimal band combination (B-G-NIR-SWIR-Slope-Elevation) for debris mapping. The GSDD dataset enables direct comparison, development, and evaluation of deep learning models, supporting advancement in fast and reliable automated glacier mapping.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100319"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landsat Next current design for geological remote sensing: VNIR-SWIR-TIR data continuity and new opportunities","authors":"Bruno Portela,&nbsp;Harald van der Werff,&nbsp;Christoph Hecker,&nbsp;Mark van der Meijde","doi":"10.1016/j.srs.2025.100258","DOIUrl":"10.1016/j.srs.2025.100258","url":null,"abstract":"<div><div>Landsat Next, the proposed mission in NASA's Landsat program planned for 2031, is designed to extend the legacy of Landsat 8–9 and Sentinel-2 in the visible-near and shortwave infrared and to introduce operational thermal infrared capabilities comparable to ASTER. As the first multispectral spaceborne sensor to combine visible-near, shortwave, and thermal infrared coverage since ASTER, it presents a unique opportunity to reestablish long-term geological remote sensing continuity.</div><div>In this study, we assess whether Landsat Next, in its currently published design, can replicate or even improve geological information derived from Sentinel-2 and ASTER. We simulate Landsat Next imagery using airborne hyperspectral datasets acquired over two well-characterised mineral systems in the Yerington district, Nevada (USA), generating equivalent datasets for Sentinel-2 and ASTER to enable sensor-level comparison without environmental influences. By adapting established band ratios and applying spectral-only and spectral-spatial resampling when simulating Landsat Next data, we isolate the influence of Landsat Next's band configuration and resolution.</div><div>Our results confirm that Landsat Next replicates key mineralogical patterns observed in Sentinel-2 and ASTER products. Moreover, it enables enhanced discrimination in zones of spectrally overlapping alteration, especially where its higher spectral or spatial resolution improves mineral identification.</div><div>By replicating established band ratio products while enhancing the detection of key mineralogical features, Landsat Next represents the first spaceborne sensor since ASTER that can potentially deliver continuous multispectral information across the visible-near, shortwave, and thermal infrared ranges, supporting future geological remote sensing studies.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100258"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}