Earth System Science Data最新文献

{"title":"META4.0: a new global mesoscale eddy network atlas derived from altimetry","authors":"Juliette Gamot,Antoine Delepoulle,Francesco Nencioli,Marie-Isabelle Pujol,Gerald Dibarboure","doi":"10.5194/essd-18-7043-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7043-2026","url":null,"abstract":"This study introduces the new global Mesoscale Eddy Trajectory Atlases, META4.0 available in an AVISO repository at https://doi.org/10.24400/527896/a01-2026.001 (CLS and CNES, 2026). META4.0 provides eddy detections, trajectories, and interaction networks derived from satellite altimetry. Eddy detection relies on the pyeddytracker (PET) algorithm (Mason et al., 2014), further optimized by Pegliasco et al. (2022), and represents a substantial improvement over the previous META3.2 product (SSALTO/DUACS, distributed by AVISO+ with CNES support). The main advance of META4.0 is the explicit identification of eddy merging and splitting events. By combining grouping, which links detections across consecutive days, and segmentation, which tracks continuity through interaction events, trajectories are organized into networks of interconnected eddies. This network-based representation complements the single-trajectory view of eddy life cycles by explicitly accounting for eddy interactions. The paper presents both diagnostic tools designed to explore individual eddy networks (e.g., timelines, spatial trajectories, and eddy properties such as effective radius or shape error) and the results of a global statistical analysis over more than three decades. These tools and analyses offer new diagnostics for investigating network properties, eddy lifetimes, and the spatial and temporal distribution of merging and splitting events of the META4.0 atlas. Clustering analyses reveal recurrent interaction patterns and identify regions where eddy networks are particularly active. An independent dataset of surface chlorophyll concentration is used for qualitative validation of selected events. Finally, Lagrangian advection of synthetic particles highlights coherent forward and backward transport signatures associated with interaction events, providing a physical validation of the reconstructed networks. Overall, META4.0 offers a novel and physically consistent framework to characterize mesoscale eddy interactions and to better understand their role in shaping ocean dynamics.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"8 1","pages":"7043-7070"},"PeriodicalIF":11.4,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049530","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 30 m annual cropland extent dynamics (2000–2024): a harmonized baseline of structural evolution and regional disparities","authors":"Yuanhong Liao,Shuang Chen,Yuqi Bai,Jie Wang,Peng Gong","doi":"10.5194/essd-18-7071-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7071-2026","url":null,"abstract":"Accurate annual information on cropland extent is essential for monitoring agricultural change, yet existing global products are often limited to snapshots or multiyear epochs and differ in their cropland definitions. Here we present GACED30, which, to our knowledge, is the first dedicated global 30 m annual cropland-extent dataset covering 2000–2024. The mapping framework combines gap-free SDC30 observations, spectral-semantic alignment of expert-annotated and spatially augmented samples, and rule-based spatial and temporal refinement. Independent classifiers were trained for each year using a common observation, feature, sample-construction, and modeling protocol, and the resulting annual record was used to derive pixel-level Cropping Frequency and 1 km Structural Evolution Indicators. Against independent stable-site FAST-Crop samples, GACED30 achieved an overall accuracy of 0.965 and an F 1 score of 0.844. Multitemporal assessments using GLAD Cropland and LUCAS reference samples showed higher recall and F 1 scores than GLC_FCS30D for both crop gain and crop loss, although the absolute transition scores remained modest. In a matched regional assessment in China, GACED30 achieved OA and F1 scores of 0.946 and 0.840, respectively, showing performance comparable with CACD. At the national scale, GACED30 agreed closely with definition-reconciled FAOSTAT statistics ( R 2 : 0.95; area-weighted direction match rate: 83.1 %). The sample-adjusted global cropland area was estimated at 1488.5 Mha in 2024, approximately 30.0 Mha higher than in 2000. Persistent expansion was concentrated in parts of Africa and South America, whereas stability and reduction were more widespread across much of the Global North. GACED30 provides a harmonized baseline for monitoring global cropland-extent dynamics and is publicly available at https://doi.org/10.5281/zenodo.18199675 (Chen et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"93 1","pages":"7071-7096"},"PeriodicalIF":11.4,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049941","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":"Italian Fluvial Sediment Transport Database: a comprehensive hub for archiving and analyzing hydrological data","authors":"Marco Luppichini,Francesco Comiti,Andrea Andreoli,Barbara Aldighieri,Paolo Billi,Irene Maria Bollati,Andrea Brenna,Diletta Chirici,Paolo Ciavola,Silvia Cilli,Marco Donnini,Simona Francalanci,Lorenzo Innocenti,Laura Longoni,Luca Mao,Ivan Marchesini,Ilenia Murgia,Giacomo Pellegrini,Daniele Penna,Lorenzo Picco,Riccardo Rainato,Leonardo Schippa,Luca Solari,Gianluca Tronti,Bruno Testa,Matteo Verdone,Monica Bini","doi":"10.5194/essd-18-7021-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7021-2026","url":null,"abstract":"Fluvial sediment transport plays a key role in geomorphological and hydrological processes, influencing river morphology, watershed and coastal sediment balances, and the environmental response to climatic and anthropogenic changes. In Italy, the availability of homogeneous and long-term data is strongly limited due to the discontinuity of monitoring activities and the fragmentation of existing sources. This study presents the development of a relational database and web application specifically designed for the collection, storage, and consultation of sediment transport data, aimed at integrating and enhancing heterogeneous datasets from both historical and contemporary monitoring networks: the Italian Fluvial Sediment Transport Database (ITASED). The database architecture, developed in PostgreSQL with the PostGIS extension, enables a direct link between observational data and their associated geographical, instrumental, and methodological metadata, ensuring traceability, interoperability, and the possibility to perform multi-temporal analyses. The web interface, built using open-source technologies, allows controlled data entry and interactive data exploration through maps, tables, and export functions. Analysis of the archived datasets highlights strong instrumental, temporal, and spatial heterogeneity: some physical-chemical parameters (e.g., pH and electrical conductivity) show standardized measurement protocols, whereas sediment transport variables exhibit high methodological variability. Time series range from sub-daily to annual observations, with denser coverage in northern Italian basins (e.g., Po, Adige, Piave, Tagliamento) since 1924. Despite these inconsistencies, integration within a unified relational framework enhances the value of a largely underused data heritage. The experience gained through this project enabled the identification of both the limitations and the potential of Italy's sediment monitoring system, providing operational guidance for methodological standardization, metadata improvement, and data harmonization at the national scale. The ITASED represents a concrete step toward establishing a shared sedimentological archive that supports scientific research, environmental management, and sustainable river basin planning. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.18799025 (Luppichini et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"5 1","pages":"7021-7041"},"PeriodicalIF":11.4,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049942","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":"AGPC: an Annual 500 m Gridded Population (1990–2020) for China incorporating 3D building volume dynamics","authors":"Xiaocong Xu,Shiyu He,Jinpei Ou,Yan Zhou,Xiaoping Liu","doi":"10.5194/essd-18-6995-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6995-2026","url":null,"abstract":"Gridded population datasets with long-term temporal coverage and fine spatial resolution are essential for earth system modelling, urban studies, and disaster risk assessment. However, existing population products often fail to adequately represent population distribution in vertically developed urban environments. This paper presents the AGPC dataset, a temporally consistent gridded population dataset for China at 500 m spatial resolution covering the period 1990–2020. AGPC was generated using a machine-learning-based dasymetric mapping framework, integrating multi-source covariates including three-dimensional (3D) building volume, building function, and other socioeconomic variables. County-level census data were used for model calibration, while annual provincial population totals from official statistical yearbooks were applied as constraints to ensure temporal consistency. The SHapley Additive exPlanations (SHAP) analysis confirms the dominant roles of commercial activity intensity and 3D building volume in shaping fine-scale population distribution and highlights the added value of vertical and functional information beyond conventional two-dimensional (2D) indicators. Population estimates were produced annually and aggregated to multiple administrative scales for validation. Comprehensive evaluations demonstrate the reliability and accuracy of the dataset across spatial and temporal scales. At the county level, AGPC shows strong agreement with census data, with correlation coefficients R greater than 0.89 and relative RMSE values below 1 % on the independent validation sets for the baseline years 2010 and 2020. At finer scales, grid-level population estimates aggregated to the township level exhibit high consistency with independent census data with R greater than 0.90, indicating satisfactory capability in capturing finer-scale spatial heterogeneity in population distribution. Multi-temporal validation at the city level for seven time points between 1990 and 2020 yields correlation coefficients ranging from 0.79 to 0.99, indicating stable temporal performance. Comparisons with existing global and regional population datasets show that AGPC better captures population patterns in high-density and vertically developed urban areas, avoiding the density saturation effects commonly observed in 2D products. The AGPC dataset provides a robust and scalable population data resource for long-term socioeconomic and environmental analyses in China, and it is available at https://doi.org/10.6084/m9.figshare.31338352 (Xu et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"11 1","pages":"6995-7019"},"PeriodicalIF":11.4,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022070","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":"Extending daily river discharge records across China using satellite-derived river widths","authors":"Yong Wang,Yulin Gong,Yinghong Jing,Xiaojun She,Yao Li","doi":"10.5194/essd-18-6973-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6973-2026","url":null,"abstract":"Long-term monitoring of global river discharge has been hindered by the uneven distribution of gauging stations and limited data accessibility, a challenge that is particularly acute in China. Although China contains one of the world's densest river networks, high-frequency in situ discharge observations remain largely unavailable in the international public domain. To address this gap, we compiled daily discharge records from 1196 gauges across China, comprising approximately 2.33 ×10 6 observations – 39 times as many gauges as are currently available for the region in the Global Runoff Data Centre (GRDC). Leveraging this unprecedented collection of in situ discharge records, along with river width time series derived from Landsat and Sentinel-2 imagery and gauge-specific hydraulic geometry relationships, we reconstructed and extended daily river discharge observations for 310 gauges from 1990 to 2024, resulting in the China Daily River Discharge Records (CDR 2 ) dataset. Compared with existing global satellite-derived discharge products, CDR 2 expands the number of available gauges in China by more than fivefold while delivering substantially improved performance, achieving a median Kling–Gupta efficiency of 0.66 during validation. Moreover, uncertainties propagated from the fitted hydraulic geometry parameters remain low, with a median relative uncertainty of only 11.89 % in the reconstructed discharge estimates. Sensitivity analyses further indicate that discharge estimation accuracy increases markedly with greater river width variability and stronger hydraulic sensitivity. Trend analysis reveals that nearly 65 % of gauges exhibit declining discharge over 1990–2024, with a median relative trend of −0.21 % yr −1 , most pronounced in the Haihe, Liaohe, Yellow River, and middle Yangtze River basins. As the most extensive satellite-derived, gauge-constrained river discharge dataset currently available for China at daily resolution, CDR 2 bridges a critical geographic gap in global river monitoring and provides a valuable benchmark for future discharge estimation, hydrological research, water resources management, and the calibration and evaluation of satellite missions. The CDR 2 dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.22231453 (Wang and Li, 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"11 1","pages":"6973-6993"},"PeriodicalIF":11.4,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022069","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":"Improving global and regional ocean heat content by consistently combining GRACE gravity, satellite altimetry and Argo profile observations in a joint inversion framework","authors":"Bernd Uebbing,Kristin Vielberg,Roelof Rietbroek,Bene Aschenneller,Armin Köhl,Jürgen Kusche","doi":"10.5194/essd-18-6945-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6945-2026","url":null,"abstract":"The current energy imbalance at the top of atmosphere and corresponding heating of the Earth system is the main driver of steric sea level change through ocean heat uptake (OHU). A global constant heat capacity factor is commonly applied to retrieve ocean heat content (OHC) from observed ocean-average steric sea level. We propose an extension to this methodology, which focuses on the leading modes of steric variability, which are derived from an ocean model and fitted to GRACE gravity, satellite altimetry and in situ Argo observations within a joint inversion framework. These modes are utilized to obtain data driven OHC estimates by establishing a mapping between modeled OHC and steric sea level, and rescaling each mode individually based on observed steric sea level change. On global scales for the period January 2005 till December 2024, our OHU results (0.62 W m −2 ) agree well with a variety of published datasets from in situ Argo data, model reanalyses and space-geodetic approaches as well as independent estimates from the CERES project. At basin scales, we demonstrate the global OHU to be driven mainly by warming of the Pacific Ocean (0.23 W m −2 ), followed by contributions from the Indian (0.20 W m −2 ) and Atlantic (0.13 W m −2 ) oceans. Minor contributions are found from the Arctic Ocean (0.01 W m −2 ), the Southern Ocean (0.02 W m −2 ) and the residual ocean (0.03 W m −2 ). Our results also indicate a shift from dominant heating in the Indian Ocean driven by heat transport from the Pacific Ocean, e.g. found during 2005–2015, towards a more evenly distributed global ocean heat budget. Monthly gridded OHC data from our Grace, Altimetry and Argo Mode Rescaling (GAAMR) method together with corresponding error estimates is available from https://doi.org/10.1594/PANGAEA.994058 (Uebbing and Kusche, 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"8 1","pages":"6945-6972"},"PeriodicalIF":11.4,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022071","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 random forest isoscape model of bioavailable Sr for South America: a focus on southern Brazil","authors":"Cinzia Scaggion,Tommaso Giovanardi,László Palcsu,Daniel Loponte,Mirian Carbonera,Sara Bernardini,Stefano Benazzi,Giulia Marciani,Marcos Cesar Pereira Santos,Eugenio Bortolini,Anna Cipriani,Federico Lugli","doi":"10.5194/essd-18-6925-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6925-2026","url":null,"abstract":"In recent years, advances in machine learning have greatly improved the generation of maps showing the geographic distribution of isotope ratios (isoscapes), which have become essential tools for environmental, mobility and provenance studies in both modern and archaeological contexts. Among the various isotopic systems employed, strontium (Sr) is particularly useful because its 87 Sr /  86 Sr ratio in the environment is largely controlled by the underlying geology through the composition of local soils and rocks. In this work, we present a new dataset of bioavailable 87 Sr /  86 Sr ratios derived from n =233 plant samples collected across southern Brazil, covering the states of Santa Catarina and Rio Grande do Sul (ca. 370 000 km 2 ). The measured ratios span from 0.70521 to 0.76039 and capture the bioavailable Sr isotope signatures over all major geological units in the region. We combined these new data with an extensive compilation of published bioavailable Sr measurements from across South America (including plants, fauna, ancient human remains, shells, snails, lichens, water and soils) to construct three random forest Sr isoscapes using different subsets of the combined dataset at the regional and continental scales. The first model incorporates the entire dataset (“All” dataset, n = 883 sites), the second is based on plant + fauna + lichen + human ( n = 661 sites) and the third is limited to plant + lichen samples ( n = 531 sites). Among the three models, the full dataset model shows lower predictive power, while the plant + fauna + lichen + human and the plant + lichen models yield better results, with similar RMSE (0.0049 and 0.0054) and R 2 values (ca. 0.76). Compared to existing Sr isoscapes of South America, our models significantly enhance both spatial coverage and resolution of bioavailable Sr predictions, particularly in southern Brazil. The new bioavailable Sr isotope dataset from Santa Catarina and Rio Grande do Sul states is available at https://doi.org/10.5281/zenodo.17988601 (Scaggion et al., 2025a) and the compiled literature dataset is reported as supplementary material.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"5 1","pages":"6925-6943"},"PeriodicalIF":11.4,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022072","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":"SPAMS10: an InSAR-derived soil motion parameter dataset to model relative peat surface elevation changes","authors":"Yustisi Lumban-Gaol,Ramon Hanssen","doi":"10.5194/essd-18-6913-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6913-2026","url":null,"abstract":"Peat subsidence poses a significant challenge to long-term land and environmental stability by increasing flood risk and producing greenhouse gas emissions. Field monitoring with extensometers reveals variability in relative surface elevation changes, which are then modeled using simple parameterization for the motion of soils (SPAMS). To extend the spatial scale of monitoring, Interferometric Synthetic Aperture Radar (InSAR) time series are used. InSAR-derived SPAMS parameters provide displacement models that can describe peat subsidence and dynamics with a set of parameters, namely four parameters for modeling and six variance-covariance terms for model uncertainties. We publish the SPAMS10 dataset, which refers to the descriptive displacement model SPAMS with ten parameters. The dataset also provides additional contextual information, such as soil code, at the parcel level, defined by the administrative boundary of an agricultural field. This dataset promotes data sharing among stakeholders, shifting products from InSAR-based relative elevation estimates to InSAR-derived displacement model parameters. These parameters enable the reconstruction of displacement models for peat subsidence analyses. The dataset is available through the 4TU.ResearchData repository: https://doi.org/10.4121/dfbe9109-d058-4a64-a5b4-1cc9d9a5f836 (Lumban-Gaol et al., 2025) and is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"95 1","pages":"6913-6923"},"PeriodicalIF":11.4,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022073","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 global gridded dataset of significant wave height via fusion of multi-mission altimetry and numerical hindcast","authors":"Haoyu Jiang,Hao Su","doi":"10.5194/essd-18-6841-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6841-2026","url":null,"abstract":"Satellite altimeters provide long-term, high-accuracy observations of significant wave height (SWH) over the global ocean. However, their sparse spatial and temporal sampling introduces undersampling errors in wave climate analyses. Direct gridding of multi-mission altimeter data through spatiotemporal interpolation still results in low-accuracy global SWH fields due to this limitation. To overcome this challenge, we use SWH outputs from a WAVEWATCH III hindcast as a background field and apply an offline fusion with along-track, jointly calibrated altimeter observations from the Climate Change Initiative Sea State dataset. As a retrospective reconstruction, the offline fusion allows observations acquired both before and after a target time to contribute to the estimate, with their influence explicitly constrained by temporal separation. Validation against buoy measurements and withheld satellite data demonstrates that the fused gridded product achieves high accuracy. To address different application needs, we provide two versions of the fused dataset: (1) a “two-sat” version that incorporates data from only two satellites at any given time, designed for wave climate studies. This configuration is designed to reduce sampling-related temporal inhomogeneity by maintaining a relatively stable number of observations and broadly consistent orbital sampling characteristics throughout the record. (2) A “multi-sat” version that integrates data from as many altimeter missions as possible, intended to support applications such as the training of artificial intelligence-based wave models, where higher spatial and temporal accuracy is prioritized. The dataset is freely available at https://doi.org/10.57760/sciencedb.29314 (Su and Jiang, 2025).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"7 1","pages":"6841-6858"},"PeriodicalIF":11.4,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021468","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":"Signal-domain guided deep learning for gap-filling of XCO and XCH\u0000 4\u0000 : a masked spatio-temporal fusion of TROPOMI and GEOS-Chem (2019–2023)","authors":"Chengkun An,Yuan Tian,Zhiwei Li,Qiaoyu Jiang,Peize Lin,Bowen Chang,Jingkai Xue,Youwen Sun","doi":"10.5194/essd-18-6859-2026","DOIUrl":"https://doi.org/10.5194/essd-18-6859-2026","url":null,"abstract":"Long-term, high-resolution monitoring of carbon monoxide (CO) and methane (CH 4 ) is essential for understanding their spatiotemporal variability and supporting climate mitigation strategies. However, satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) are often spatially and temporally incomplete, while existing fusion methods still struggle to achieve both high accuracy and spatiotemporal continuity. Here, we propose a signal-domain fusion approach that combines three-dimensional discrete cosine transform (3D DCT) and singular value decomposition (SVD) to integrate TROPOMI retrievals with GEOS-Chem simulations. A lightweight residual U-Net is further employed to refine the initial reconstruction by learning residual fields from GEOS-Chem simulations and DCT/SVD reconstruction outputs, guided by a masked loss. The method generates global 0.25° and China-specific 0.05° daily gap-free XCO and XCH 4 datasets from 2019 to 2023. In the time-series comparison analysis at representative sites, the fused datasets generally follow the temporal variations observed by the Total Carbon Column Observing Network (TCCON) and TROPOMI. Missing-rate-threshold experiments further show that the fused products perform comparably to original TROPOMI retrievals under low and moderate missing-rate conditions and show improved performance under sparse TROPOMI coverage (MR >0.5), with R 2 values of 0.91 for XCO and 0.83 for XCH 4 , along with reduced biases and standard deviations. The fused datasets also capture regional XCO increases in parts of North America, decreases over eastern China, and widespread XCH 4 growth, wildfire-related enhancements in Chongqing in 2022, and short-term variations over rice-growing regions. These results indicate that the proposed framework can reconstruct missing satellite observations with improved continuity and provide useful fused datasets for studying regional variability, event-related enhancements, and atmospheric composition changes. The generated datasets are publicly available at https://doi.org/10.5281/zenodo.22010891 (An et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"102 1","pages":"6859-6884"},"PeriodicalIF":11.4,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021472","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}