Remote Sensing of Environment最新文献

{"title":"A large-scale framework for deriving tidal flat topography from SWOT data","authors":"Hao Xu ,&nbsp;Nan Xu ,&nbsp;Wenyu Li ,&nbsp;Kai Tan ,&nbsp;Chunpeng Chen ,&nbsp;Huan Li ,&nbsp;Lucheng Zhan ,&nbsp;Pei Xin ,&nbsp;Jiaqi Yao ,&nbsp;Peng Li ,&nbsp;Zhen Zhang ,&nbsp;Haipeng Zhao ,&nbsp;Bolin Fu ,&nbsp;Yifei Zhao ,&nbsp;Yufeng Li ,&nbsp;Qi Wang ,&nbsp;Fan Zhao ,&nbsp;Xiaojuan Liu ,&nbsp;Zhongwen Hu ,&nbsp;Guofeng Wu ,&nbsp;Qingquan Li","doi":"10.1016/j.rse.2026.115237","DOIUrl":"10.1016/j.rse.2026.115237","url":null,"abstract":"<div><div>Tidal flat topography is a fundamental attribute affecting inundation dynamics, sediment transport, and ecosystem functioning, yet accurate and spatially consistent large-scale monitoring remains challenging. Here, we leveraged satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission to develop a novel, large-scale framework for deriving tidal flat topography from SWOT data, and demonstrated its capability by generating a high-accuracy, national-scale elevation dataset for China. By combining a percentile-based aggregation of multi-temporal water-surface elevation observations with a tide-constrained, adaptive best-quantile (best-q) reconstruction strategy, followed by linear interpolation for gap filling, we improved both vertical accuracy and spatial completeness. Validation against airborne LiDAR, GNSS-RTK surveys, and ICESat-2 photon data demonstrates robust performance across diverse coastal settings, achieving RMSE = 0.34–0.47 m and R² = 0.81–0.88 at a horizontal resolution of 100 m. Compared with existing large-scale digital elevation models (DEMs), the SWOT-derived topography not only improves vertical accuracy by over 80% but also providing substantially more complete spatial coverage of tidal flat elevations. Spatial analyses reveal pronounced latitudinal gradients, with higher tidal flats concentrated in low-latitude regions and extensive low-lying flats dominating northern estuarine and deltaic systems. This study establishes a scalable framework for tidal-flat elevation retrieval and provides a foundational dataset to support coastal monitoring and sustainable management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115237"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947722","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":"Annotation-free cloud masking for PlanetScope images in the Arctic via cross-platform ability transfer using deep learning and foundation models","authors":"Zhili Li ,&nbsp;Yiqun Xie ,&nbsp;Sergii Skakun ,&nbsp;Xiaowei Jia ,&nbsp;Gengchen Mai ,&nbsp;William Lu ,&nbsp;Matthew Tong ,&nbsp;Zhihao Wang","doi":"10.1016/j.rse.2025.115138","DOIUrl":"10.1016/j.rse.2025.115138","url":null,"abstract":"<div><div>Cloud masking is an essential task for satellite-based Earth monitoring, and the quality of cloud masks can directly impact the solutions of the downstream Earth monitoring tasks. While significant progress has been made especially for data with desired bands (e.g., thermal bands in Landsat-8), the masking quality on small satellites with higher resolution but fewer spectral bands is still unreliable at high latitudes, where confusion with snow and ice makes the task significantly more challenging. We propose a novel learning-enabled cross-platform ability transfer paradigm that offers a scalable and effective solution to tackle this challenge through a case study using PlanetScope images in the Arctic. A unique characteristic of the new paradigm is that it does not require manual annotations to be collected for PlanetScope images, which is often the bottleneck and the most time-consuming part of machine learning-based cloud masking, especially given the similarity between clouds and snow/ice. To realize this, our approach first designs and creates a new training dataset, Co-Clouds, which contains around 45,000 coincident pairs of PlanetScope and Landsat-8 image patches collected within a nearly simultaneous temporal window. This coincident dataset offers a way to generate large volumes of training data and builds a bridge to transfer Landsat-8’s stronger cloud masking skills in the Arctic to PlanetScope images via data-driven learning. We also show the feasibility of the ability transfer from spectral signatures (e.g., thermal bands) to spatial signatures (e.g., textures). Using our Co-Clouds dataset, we train several deep learning models including both regular-size deep learning models and large foundation models. To validate the quality of the masks, we further create a manually labeled cloud mask dataset for PlanetScope images in the Arctic. Both the quantitative and qualitative results show significant improvements over the current operational cloud masks by PlanetScope. For example, the large foundation models such as SegFormer achieve approximately 20 % higher overall accuracy and 28 % higher producer’s accuracy than the operational cloud masks, while maintaining comparable or better user’s accuracy exceeding 90 %. The new approach is also very easy to implement and extend to other platforms, opening new opportunities for broadcasting advanced skills from one platform to others.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115138"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689116","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":"Estimation of sea surface foam coverage and effective foam layer thickness from satellite microwave measurements","authors":"Xuchen Jin ,&nbsp;Xianqiang He ,&nbsp;Palanisamy Shanmugam ,&nbsp;Yan Bai ,&nbsp;Jianyun Ying ,&nbsp;Qiankun Zhu ,&nbsp;Yaqi Zhao ,&nbsp;Delu Pan","doi":"10.1016/j.rse.2025.115176","DOIUrl":"10.1016/j.rse.2025.115176","url":null,"abstract":"<div><div>Sea surface foam commonly represents the accumulation of bubbles on the sea surface caused by breaking waves and plays a critical role in the air-sea interaction process and climate research. Both foam coverage and foam layer thickness have a profound effect on sea surface emissivity for moderate to high wind speeds due to the high emissivity of foam at microwave bands. However, there is a lack of models to estimate sea foam layer thickness due to the limited experimental data and constrained empirical formulations. To address this limitation, we propose a dual-channel (L- and Ka-bands) method that utilizes passive microwave satellite measurements to estimate sea surface foam coverage and effective foam layer thickness over the global ocean. This method uses microwave measurements from the Aquarius/SAC-D and Soil Moisture Active Passive (SMAP) missions, together with observations from the WindSat instrument, to convert the top-of-atmosphere (TOA) brightness temperature (TB) to sea surface TBs and then isolate the foam-induced emission component. Because sea surface emissivity is sensitive to foam distributions, a foam emissivity model was developed and implemented to derive the foam layer parameters (coverage and thickness) as functions of wind speed. The retrieved foam coverage shows strong agreement with independent observations, with root mean square differences (RMSDs) of 0.87 % against field measurements and 0.38 % against satellite data. In addition, the retrieved effective foam thickness also shows close consistency with hydrodynamic model expectations. The global distributions of foam coverage and effective layer thickness were further retrieved and analyzed in this study, which demonstrated high potential of the proposed models compared to the previous methods and suggested that the combined use of L- band and Ka-band measurements from a single platform could improve the accuracy of foam coverage and effective layer thickness over the global ocean.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115176"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732130","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 SIF framework for decoupling hierarchical water stress impacts on winter wheat photosynthesis","authors":"Kaiqi Du ,&nbsp;Guilong Xiao ,&nbsp;Xia Jing ,&nbsp;Jie Zhang ,&nbsp;Tingting Zhao ,&nbsp;Xuecao Li ,&nbsp;Yelu Zeng ,&nbsp;Jianxi Huang","doi":"10.1016/j.rse.2025.115216","DOIUrl":"10.1016/j.rse.2025.115216","url":null,"abstract":"<div><div>Drought has emerged as a critical constraint on sustainable agricultural development, particularly in water-scarce agroecosystems where multiscale hydrological stresses—such as rainfall deficits, soil moisture depletion, and groundwater exhaustion—interact to undermine crop productivity stability. However, current remote sensing frameworks lack the capacity to isolate and quantify the independent impacts of groundwater drought on crop photosynthesis and yield, leading to long-standing underestimation of deep-layer hydrological stress, especially in groundwater-dependent regions. To address this gap, we have proposed a dynamic monitoring framework based on solar-induced chlorophyll fluorescence (SIF) to assess the photosynthetic response characteristics of winter wheat across the Huang-Huai-Hai Plain under precipitation, surface moisture, and groundwater anomalies. This framework integrates dynamic time warping (DTW) for interannual phenological alignment and constructs a quantile-based dynamic baseline library that overcomes the limitations of traditional normality-based anomaly metrics. On this basis, we have developed a novel photosynthetic response anomaly index (PRAI) to characterize the spatiotemporal evolution of drought-induced photosynthetic stress. Results reveal that groundwater anomalies induce a significantly lagged crop response (mean lag ≈ +2.1 months, <em>p</em> &lt; 0.01) and exert stronger influence on photosynthetic dynamics than soil surface moisture or rainfall. PRAI exhibits more concentrated and persistent negative anomalies during groundwater drought years, correlating more strongly with yield loss (R² = 0.53) than during meteorological drought years (R² = 0.30). Cross-validation using evapotranspiration (ET) and vegetation optical depth (VOD) further confirms PRAI reliability in capturing physiological stress. The proposed SIF-based dynamic monitoring framework not only deepens the understanding of crop eco-physiological response mechanisms to multiscale water stress, but also provides critical scientific support and methodological innovations for regional scale precision agriculture, crop model optimization, and sustainable water resource management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115216"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823489","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":"Large-scale tree-level mapping of forest structure including species type with remote sensing data and ground measurements","authors":"J. Kostensalo ,&nbsp;P. Packalen ,&nbsp;M. Kuronen ,&nbsp;L. Mehtätalo ,&nbsp;S. Tuominen ,&nbsp;M. Myllymäki","doi":"10.1016/j.rse.2025.115223","DOIUrl":"10.1016/j.rse.2025.115223","url":null,"abstract":"<div><div>Remote-sensing based tree maps can be used to calculate various diversity indices, but the detection probability of trees depends on size and species. We propose a novel approach combining individual tree detection (ITD) with resampling corrections (+R) which aims to simultaneously correct the size, species, and spatial distribution of trees using scalable algorithms. Using airborne laser scanning, optical data, and ground measurements, we demonstrate the compatibility of ITD+R with two different types of forests and ITD algorithms, as well as its scalability to areas exceeding 3000 km². The tree maps were evaluated using plot-level variables and benchmarked against area-based <span><math><mi>k</mi></math></span> nearest neighbors (<span><math><mi>k</mi></math></span>-NN). The ITD+R improved ITD results for most studied metrics, with the Shannon index being an exception, and even outperformed <span><math><mi>k</mi></math></span>-NN in predicting dominant height in managed stands, though <span><math><mi>k</mi></math></span>-NN still outperformed for stem density and volume. The ITD+R approach was shown to be adaptable to various diversity indices which it has not been specifically trained on, with 254 m² plot-level predictions correlating at <span><math><mi>r</mi></math></span>=0.42–0.91. While ITD trees could be classified with OA=82.0%–86.6% to pine, spruce, and deciduous, further research is needed to account for rare tree species, as low prevalence results in a large number of false detections which cannot be sufficiently addressed with classification alone.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115223"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897654","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":"Space-based assessment of NOx emissions from global oil and gas fields: Bridging the gap in current emission inventories","authors":"Piyushkumar N. Patel ,&nbsp;Ritesh Gautam ,&nbsp;Mark Omara","doi":"10.1016/j.rse.2025.115229","DOIUrl":"10.1016/j.rse.2025.115229","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Identifying air pollutant sources and quantifying related emissions provides crucial information towards improving global air quality and public health. However, emission inventories for oil and gas (OG) activities inadequately represent nitrogen oxide (NO&lt;sub&gt;x&lt;/sub&gt;) emissions—a key precursor to tropospheric ozone and secondary aerosols—with notable discrepancies identified. Satellite remote sensing provides a unique vantage point to map and quantify these emissions consistently on a global scale. Here, we quantify annual NO&lt;sub&gt;x&lt;/sub&gt; emissions from 44 major OG basins distributed globally, utilizing TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;) observations with the divergence flux method. In addition, we use the spaceborne Visible Infrared Imaging Radiometer Suite (VIIRS) natural gas flaring detections to further constrain satellite-derived NO&lt;sub&gt;x&lt;/sub&gt; emissions. The divergence flux method, which addresses 3D topography corrections and chemical loss of NO&lt;sub&gt;x&lt;/sub&gt; while accounting for wind-induced flux smearing, provides a robust approach for estimating NO&lt;sub&gt;x&lt;/sub&gt; emissions. This top-down approach resolves emissions at the facility and basin scale (0.01° x 0.01°), enabling direct quantification of a major gap in current bottom-up inventories. Our findings reveal significant differences between satellite observations and established inventories, which systematically underestimate OG sector emissions. A direct comparison of our TROPOMI-derived NO&lt;sub&gt;x&lt;/sub&gt; emissions against the existing inventories suggests that EDGARv6.1 underestimates onshore emissions by 61 % and offshore emissions by 26 %. The discrepancy is even more pronounced for the CAMS-GLOB-ANT_v5.3 inventory, which underestimates onshore and offshore emissions by 78 % and 92 %, respectively. These findings hold significance for global emission assessments, demonstrating that current inventories are missing a substantial source of NO&lt;sub&gt;x&lt;/sub&gt; pollution, particularly from OG fields. Furthermore, our approach provides detailed spatial emission maps that enhance the granularity in depicting NO&lt;sub&gt;x&lt;/sub&gt; distribution with error analysis (uncertainties: 32 %-54 %) supporting that inventory discrepancies are statistically significant, representing structural deficiencies rather than measurement error. We further explore the variations in the correlation between NO&lt;sub&gt;x&lt;/sub&gt; emissions with OG production volumes and CH&lt;sub&gt;4&lt;/sub&gt; concentrations across different OG basins in North America. The observed co-locations and correlations provide important insights into co-pollution emission characteristics of methane and NO&lt;sub&gt;x&lt;/sub&gt;, that are respectively a potent greenhouse gas and a reactive air quality pollutant, linked to be originating from oil and gas activity. These findings have important implications for regulatory monitoring and verification, particularly for addressing emissions transparency requirements under internationa","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115229"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881024","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 detection and analysis of eddies in the East Greenland marginal ice zone from Sentinel-1 SAR imagery","authors":"Fei Jiang ,&nbsp;Xiaofeng Li ,&nbsp;Yingjie Liu ,&nbsp;Yibin Ren","doi":"10.1016/j.rse.2025.115177","DOIUrl":"10.1016/j.rse.2025.115177","url":null,"abstract":"<div><div>Ocean eddies in the marginal ice zone (MIZ) play a crucial role in sea-ice dynamics and polar ocean-atmosphere interactions; however, their detection remains challenging due to their complex surface signatures. In this study, we developed MIZ-EDYOLO, a deep learning model customized for detecting MIZ eddies from dual-polarized Sentinel-1 SAR imagery. Built upon the YOLOv9-t architecture and enhanced with specialized modifications, the model was trained on a dataset containing over 20,000 slices extracted from 1370 SAR images. The MIZ-EDYOLO model achieves high detection accuracy (∼80 % F1-score) on the test set and performs reliably on 200 full-scene images, enabling efficient and automated eddy identification. Using this model, we constructed the first six-year (2018–2023) SAR-based MIZ eddy dataset for the East Greenland region, comprising over 10,000 eddy instances. Analysis of this dataset reveals that eddy distributions are related to boundary currents, topographic forcing, and seasonal variations of the MIZ. Cyclonic eddies (CEs) outnumber anticyclonic eddies (AEs) by a factor of 8.4, while AEs exhibited an average radius about 1.8 times larger than CEs. The observed asymmetries between CEs and AEs are linked to their rotational dynamics and the associated sea-ice responses. This study presents a scalable and operational framework for efficient eddy monitoring in the MIZ, providing new insights into multi-scale oceanographic processes in climate-sensitive polar regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115177"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689855","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":"High-resolution annual desertification mapping in northern China using a novel comprehensive desertification index and unsupervised algorithm","authors":"Yaqing Dou ,&nbsp;Huaiqing Zhang ,&nbsp;Hua Sun ,&nbsp;Hui Lin ,&nbsp;Yang Liu ,&nbsp;Meng Zhang","doi":"10.1016/j.rse.2025.115230","DOIUrl":"10.1016/j.rse.2025.115230","url":null,"abstract":"<div><div>Desertification is a global ecological and environmental problem, dynamic monitoring and accurate assessment of desertification are essential for restoring regional ecology and achieving sustainable development. Current desertification monitoring methods face dual challenges including unclear remote sensing mechanisms, non-robust extraction methods and the absence of high-resolution large-scale desertification products. This study constructs a comprehensive desertification index (CDI) by integrating multisource remote sensing data (Sentinel-1/2), incorporating three features —phenomenal indices (vegetation cover), cause indices (soil moisture) and essence indices (soil roughness)—on the basis of the multidimensional driving mechanisms of desertification physical processes. The Gaussian mixture model (GMM) was then applied to the CDI to automate desertification mapping for yielding the first 10 m-resolution annual desertification dataset in northern China (NCDMD, 2016–2024). The results demonstrated that the CDI-GMM based method achieves superior performance in mapping desertification scope across northern China in 2019, with an overall accuracy of 93.5 % and an overall accuracy of 86.4 % for desertification degree according to field survey data. In comparison, traditional approaches showed significantly lower accuracy, with the pixel dichotomy model (FVC-based) achieving 82.2 % in scope extraction and 50.3 % in degree classification, while the DDI feature space method reached 86.1 % and 64.2 %, respectively. Comparative experiments with five unsupervised classification methods (GMM, K-Means, MiniBatch K-Means, Jenks natural breaks, and Weka LVQ algorithms) indicated that the CDI combined with the GMM clustering algorithm can optimize the extraction of desertification and maintain stable performance, with an overall classification accuracy of over 93 %. Moreover, the NCDMD achieved consistent desertification mapping accuracies above 83 % throughout the 2016–2024 period, further demonstrating the robust spatiotemporal reliability of the proposed product. In summary, as a nationally significant high-resolution base dataset, the NCDMD not only fills the gap in high-precision desertification monitoring in China but also provides scientific support for ecological restoration assessment and land management policy-making.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115230"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938860","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":"Introduction to a 45-year (1979–2023) global daily snow cover fraction product from multiple AVHRR satellites with accuracy assessment","authors":"Xiongxin Xiao ,&nbsp;Kathrin Naegeli ,&nbsp;Valentina Premier ,&nbsp;Shaopeng Li ,&nbsp;Christoph Neuhaus ,&nbsp;Andreas Wiesmann ,&nbsp;Stefan Wunderle","doi":"10.1016/j.rse.2026.115235","DOIUrl":"10.1016/j.rse.2026.115235","url":null,"abstract":"<div><div>Accurate monitoring of seasonal to decadal snow cover dynamics is essential for climate change attribution and sustainable water resource management. As part of the European Space Agency (ESA) Snow Climate Change Initiative (CCI+) Phase-2 project, this study introduces an Advanced Very High Resolution Radiometer (AVHRR) snow cover fraction (SCF) product portfolio, with a particular emphasis on the development of AVHRR10C1.V4—the first 45-year (1979–2023), global, daily SCF product. This dataset achieves unprecedented temporal consistency by addressing long-standing challenges such as orbital drift, inter-sensor inconsistencies, reduced cloud contamination, and enhanced SCF retrieval accuracy. The AVHRR10C1.V4 product was generated by integrating newly calibrated EUMETSAT AVHRR Fundamental Data Record (FDR) data from 16 AVHRR sensors on a 0.05° grid through a calibration method, modified cloud masking, an updated SCF retrieval method, refined post-processing, and a novel consolidation framework. Comprehensive validation against 66 high-resolution Landsat/Sentinel-2 SCF maps and extensive ground-based snow measurements confirms the robust accuracy of the AVHRR10C1.V4 product: root mean square errors of 16–19% for viewable snow cover fraction (SCFV) and 18–28% for ground snow cover fraction (SCFG), with overall accuracy (OA) ranging from 0.80 to 0.92. The consolidation approach remarkably reduces RMSEs by 7%–48% and lowers missing data rates by ∼30% compared to single-sensor products. The product maintains strong temporal consistency with ERA5-Land SCF product (<em>R</em> &gt; 0.8) and in situ snow measurements. Statistical analysis over the 45-year record and multiple separate periods confirms minimal sensor-drift biases, revealing no statistically significant breakpoints or drift trends (Mann-Kendall <em>p</em> &gt; 0.05). While the SCFV product proves robust to variations in viewing geometry and vegetation conditions, SCFG accuracy is more sensitive to forest cover density—exhibiting substantially increased estimation uncertainties in dense forest canopies (forest cover density &gt; 50%) due to canopy radiative effects and the use of static land cover assumptions. This open-access, climate data record–quality AVHRR10C1.V4 product establishes a critical benchmark for studying snow-climate interactions and long-term cryospheric monitoring. It supports the development of next-generation global SCF products, enabling reliable detection of snow trends, improved hydrological modeling, and informed climate adaptation in a warming climate.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115235"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972828","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":"Hyperspectral OCI/PACE observations of the Atlantic Sargassum","authors":"Lin Qi ,&nbsp;Menghua Wang ,&nbsp;Chuanmin Hu ,&nbsp;Yuyuan Xie ,&nbsp;Brian B. Barnes","doi":"10.1016/j.rse.2025.115185","DOIUrl":"10.1016/j.rse.2025.115185","url":null,"abstract":"<div><div>Since the first appearance of the annually recurrent Great Atlantic <em>Sargassum</em> Belt (GASB) in 2011, satellite remote sensing has been used as a primary technique to monitor and track the pelagic <em>Sargassum fluitans/natans</em> in the Atlantic Ocean. The Ocean Color Instrument (OCI) on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE, 2024–present) mission is a first-ever operational hyperspectral sensor designed to measure the surface ocean's biological and biogeochemical properties at global scale on a near-daily basis, which is expected to provide improved performance over traditional multi-band polar-orbiting ocean color sensors. Here, we evaluate the capacity of OCI in detecting and quantifying the Atlantic <em>Sargassum</em>, referenced against heritage multi-band satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS on Aqua) and Visible Infrared Imaging Radiometer Suite (VIIRS on NOAA-20). Our research involved development of a shared deep-learning <em>Sargassum</em> detection algorithm, fine-tuned for each sensor. As such, we found that OCI not only provided 47% more valid observations (# of valid pixels) than MODIS in the central west Atlantic for the study period of May–August 2024, but from the common valid pixels OCI also detected 51% more <em>Sargassum</em>-containing pixels. While the former is mostly due to the OCI's ability to avoid sun glint, the latter appears to be due to band binning and fewer artifacts around clouds. Using VIIRS as a bridge, it is further found that such a difference between OCI and MODIS is not due to the reported MODIS sensor degradation after 2023, but is inherent to sensor and measurement characteristics. On the other hand, VIIRS also showed more valid observations and detected more <em>Sargassum</em> pixels than MODIS, attributed to the larger swath and a finer spatial resolution, respectively. For these reasons, both OCI and VIIRS detected substantially more <em>Sargassum</em> than MODIS at daily, weekly, and monthly scales, although the spatial distributions and temporal changes of <em>Sargassum</em> revealed by the three sensors are similar. Finally, because of the hyperspectral capability, OCI is the only sensor that can spectrally discriminate <em>Sargassum</em> pixels without ambiguity. Such improved performance will make OCI a unique sensor to map both macroalgae mats and microalgae scums at global scale in both near real-time and retrospective analyses.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115185"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759817","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}