Earth and Space Science最新文献

{"title":"Optimizing PPP Performance by Incorporating ZWD Constraints Derived From Data Assimilation","authors":"Masoud Dehvari,&nbsp;Saeed Farzaneh,&nbsp;Ehsan Forootan","doi":"10.1029/2024EA004173","DOIUrl":"10.1029/2024EA004173","url":null,"abstract":"<p>One of the primary error sources limiting the performance of the Precise Point Positioning (PPP) technique is the atmospheric wet delay, caused by the presence of water vapor in the lower atmosphere. Accurately representing this parameter is crucial for improving the initialization and accuracy of satellite-based positioning techniques. However, existing empirical models have struggled to capture the severe spatial and temporal variations of this parameter, thereby limiting their effectiveness in high-precision applications. To address these challenges, this study introduces a sequential Calibration and Data Assimilation (C/DA) approach to enhance the estimation and prediction of Zenith Wet Delay (ZWD) values. For this aim, an empirical regional atmospheric wet delay model was constructed using Principal Component Analysis (PCA), serving as the background model for the C/DA method. The methodology involves calibrating this empirical ZWD model using the Ensemble Kalman Filter (EnKF) method, wherein observed ZWD values from approximately 309 GNSS stations across the central Europe are assimilated into the model. The calibrated model parameters were then used to estimate ZWD values, which were subsequently applied as constraints in the PPP method (referred to as PPP-DA) at 10 GNSS test stations within the study area. The study compares the positioning accuracy and convergence time achieved using the PPP-DA method with those obtained from traditional PPP approaches and PPP utilizing ZWD constraints from the GFS model (PPP-GFS). The results demonstrate a significant enhancement, with the PPP-DA method achieving an average improvement of 2 mm in positioning accuracy across all considered stations (representing a 21% reduction compared to the conventional PPP method), along with an average decrease in convergence time of approximately 16%. These findings highlight the potential of integrating CDA techniques to refine the accuracy and efficiency of satellite-based positioning.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asymmetries in the Temporal Variation of the Electron Content During a Solar Eclipse","authors":"A. Meza,&nbsp;G. Bosch,&nbsp;M. P. Natali,&nbsp;B. Eylenstein,&nbsp;A. Urutti","doi":"10.1029/2025EA004372","DOIUrl":"10.1029/2025EA004372","url":null,"abstract":"<p>Solar eclipses offer a unique opportunity to study rapid variations in the Earth's atmosphere. By accurately modeling the timing and magnitude of obscuration caused by the Moon's shadow, we can investigate the eclipse's impact on the behavior of the Earth's ionosphere. Our approach involves deriving the vertical total electron content (VTEC) of the ionosphere using navigation satellite signals. Our previous research, conducted during the 2017 North American eclipse, focused on a limited area. We employed a skewed Gaussian profile to model the temporal variation of the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 </mrow>\u0000 <annotation> ${Delta }$</annotation>\u0000 </semantics></math>VTEC curve, introducing a new parameter to better characterize the time delay in the ionosphere's response. This study broadens our research to include the East Coast and integrates the Global Ionosphere-Thermosphere Model (GITM). The skewness parameter reflects the relative durations of ionospheric decay and recovery, where positive values indicate rapid decay coupled with slow recovery and negative values suggest the opposite. Although our <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 </mrow>\u0000 <annotation> ${Delta }$</annotation>\u0000 </semantics></math>VTEC simulation using GITM qualitatively matches observed behaviors, it faces challenges in accurately capturing the maximum drop and recovery phases, particularly in the eastern regions, likely due to insufficient consideration of plasmaspheric refilling, which significantly influences the recovery of the upper ionospheric layers. The path of the eclipse totality delineates a boundary where positive asymmetries are observed to the south while negative asymmetries appear to the north.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty-Aware Machine Learning Bias Correction and Filtering for OCO-2: 1","authors":"Steffen Mauceri,&nbsp;William Keely,&nbsp;Josh Laughner,&nbsp;Christopher W. O’Dell,&nbsp;Steven Massie,&nbsp;Robert Nelson,&nbsp;David Baker,&nbsp;Matthäus Kiel,&nbsp;Otto Lamminpää,&nbsp;Jonathan Hobbs,&nbsp;Abhishek Chatterjee,&nbsp;Tommy Taylor,&nbsp;Paul Wennberg,&nbsp;Sean Crowell,&nbsp;Britton Stephens,&nbsp;Vivienne H. Payne","doi":"10.1029/2025EA004328","DOIUrl":"10.1029/2025EA004328","url":null,"abstract":"<p>The Orbiting Carbon Observatory-2 (OCO-2) makes space-based radiance measurements of reflected sunlight. Using a physics-based retrieval algorithm, these measurements are inverted to estimate column-averaged atmospheric carbon dioxide dry-air mole fractions (XCO₂). However, biases are present in the retrieved XCO₂ due to sensor calibration errors and discrepancies between the physics-based retrieval and nature. We propose a Random Forest (RF), a non-linear, interpretable machine learning (ML) technique, to correct these biases. The approach is rigorously validated, comes with quantified uncertainties, and is derived independent of carbon flux models. Compared to the operational approach, our method reduces unphysical variability over land and ocean and shows closer agreement with independent ground-based observations from the Total Carbon Column Observing Network. The RF-bias correction is suitable for integration into the operational processing pipeline for the next version of OCO-2 products, pending additional testing and validation. It is inherently generalizable to other existing and planned greenhouse gas monitoring missions. This paper (Part 1) describes the RF bias correction, while a second paper (Part 2) describes the development of a data filtering strategy specifically designed for a subset of retrievals exhibiting irreducible errors that remain inadequately corrected by the ML bias correction.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Gap-Filling of NASA Deep Blue Satellite Aerosol Optical Depth Over the Contiguous United States (CONUS) Using the UNet 3+ Architecture","authors":"Jeffrey S. M. Lee,&nbsp;S. Marcela Loría-Salazar,&nbsp;Heather A. Holmes,&nbsp;Andrew M. Sayer","doi":"10.1029/2025EA004338","DOIUrl":"10.1029/2025EA004338","url":null,"abstract":"<p>Due to sensor and algorithmic constraints, satellite aerosol optical depth (AOD) retrievals are spatially incomplete and have gaps caused by clouds and bright surfaces. These gaps represent a barrier in characterizing daily aerosol loadings, which is important for air quality applications. In particular, recent studies in aerosol studies have shown satellite AOD to be a useful predictor of particulate matter, but are often limited to monthly or longer temporal resolution because of missing AOD retrievals. In this study, we propose using a UNet 3+ to fill gaps in satellite AOD retrievals. We tested the hypothesis that UNet 3+ trained on deep blue (DB) AOD and supplemental data sets (e.g., Modern-Era Retrospective analysis for Research and Applications, Version 2 reanalysis AOD, meteorological and land-use variables from North American Mesoscale Forecast System, and Hazard Mapping System smoke polygons) will improve the availability of AOD data accurately. We created spatiotemporal data sets of daily, gap-filled DB AOD from 2012 to 2023 over the CONtinental United States (CONUS) at a 12 × 12 km² resolution. We were able to train the model and perform the gap-filling in ∼10 hr, resulting in an increase of AOD data availability by 281%. We demonstrated that our approach is feasible over CONUS through quantitative and qualitative evaluations against AERONET and DB AOD. In statistical evaluations, our gap-filled AOD data set attained an RMSE ∼ 0.09 and a <i>r</i> ∼ 0.87 against collocated AERONET retrievals, compared to an RMSE ∼ 0.11 and a <i>r</i> ∼ 0.86 that the original DB AOD retrievals scored against AERONET. We plan to use this data set for future air quality and health investigations.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assimilating GOES-16 ABI All-Sky Brightness Temperature Into the HAFS Dual-Resolution Self-Consistent EnVar DA System: Methods for Observation Error Estimation and Impact on Hurricane Laura (2020)","authors":"Xu Lu,&nbsp;Xuguang Wang","doi":"10.1029/2024EA004058","DOIUrl":"10.1029/2024EA004058","url":null,"abstract":"<p>This study investigates the impact of assimilating GOES-16 all-sky Advanced Baseline Imager (ABI) brightness temperature observations using a newly developed, continuously self-cycled, dual-resolution, 3DEnVar data assimilation system within the Hurricane Analysis and Forecast System. Focusing on the pre-rapid intensification period of Hurricane Laura, the results demonstrated that assimilating ABI observations without proper observation error treatment can be neutral or even detrimental. However, using a symmetric cloud impact approach to adaptively estimate observation errors enhances the Gaussianity of the Observation-Minus-Background Probability Distribution Functions, and significantly improves the analysis and predictions of Hurricane Laura. The improvements in the track forecasts can be attributed to better environmental analyses due to more effective use of clear sky observations, while the improved intensity forecasts stem from improved inner-core dynamic and thermodynamic structures, achieved through the more effective use of cloudy-sky observations.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction and Demonstration of a Seaplane-Type UAV-Based High-Precision GNSS-A Seafloor Crustal Deformation Observation System","authors":"Y. Yoshizumi,&nbsp;Y. Yokota,&nbsp;M. Kaneda,&nbsp;S. Yamaura,&nbsp;Y. Kameta,&nbsp;T. Inoue,&nbsp;K. Kouno","doi":"10.1029/2025EA004237","DOIUrl":"10.1029/2025EA004237","url":null,"abstract":"<p>The Global Navigation Satellite System—Acoustic combination (GNSS-A) is used to observe seafloor crustal deformation by determining the global position of the seafloor with GNSS positioning and acoustic ranging between a sea surface platform and a transponder station located on the seafloor. However, GNSS-A observations are currently conducted mainly by vessels, and economic and physical constraints limit the ability to improve the frequency and real-time nature of observations. Therefore, unmanned arial vehicle (UAV) -based GNSS-A observation has been proposed. In this study, we constructed a GNSS-A system based on a seaplane-type UAV capable of centimeter-level measurements and evaluated its seafloor positioning performance. An experiment in a tank showed that the system has an acoustic ranging accuracy of less than 2.1 cm, and allowed us to evaluate the previously unreported fractional wavelength instrumental biases. UAV-based GNSS-A tests were conducted using the constructed system at an actual GNSS-A site. Under optimal sea conditions devoid of significant waves or wind, the observation result suggested that seafloor positioning could be achieved with a horizontal RMS of approximately 1–2 cm—comparable to that of vessel-based systems. Thus, we established the foundations for practical UAV observation technology for the operation of high-frequency observations and emergency observations for detecting postseismic deformation. It has also become possible to verify instrument bias in a vessel-based system.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of FY-3G and GPM Observations on Precipitation Structure and Microphysical Characteristics: A Case of Super Typhoon Krathon","authors":"Shengnan Zhou,&nbsp;Yang Gao,&nbsp;Meng Fang,&nbsp;Song Yuan,&nbsp;Yunfei Fu","doi":"10.1029/2025EA004353","DOIUrl":"10.1029/2025EA004353","url":null,"abstract":"<p>China's first precipitation measurement satellite, FY-3G, became operational in April 2024. This study presents the first comparison of Level-2 Ku-band precipitation products from FY-3G and Global Precipitation Measurement (GPM), focusing on the three-dimensional precipitation structure and microphysical characteristics of Super Typhoon Krathon (2418). Both FY-3G and GPM observed that convective precipitation dominated the eyewall and stratiform precipitation prevailed in the inner and outer rainbands. However, FY-3G reported higher overall precipitation intensities than GPM. In the eyewall, both satellites observed that high concentrations (dB<i>N</i>_w &gt; 40) of large raindrops (<i>D</i>_m &gt; 2 mm) led to intense precipitation, where droplet concentration was the primary determining factor. FY-3G exhibited a broader drop size distribution (DSD) for convective precipitation than GPM, while for 10–20 mm/hr stratiform precipitation, both <i>D</i>_m and lg<i>N</i>_w had a narrower distribution. The inner rainbands exhibited the most distinct DSD differences, with FY-3G observing <i>D</i>_m from 0.6 to 2.6 mm, while GPM detected 1.3–2.3 mm with lower lg<i>N</i>_w. In the outer rainbands, both satellites observed large raindrops (<i>D</i>_m &gt; 2.1 mm), driven by the super typhoon's radial circulation dynamics. For most stratiform precipitation, FY-3G showed 1.5–2.0 mm raindrops as the main contributors, while GPM also had a high proportion of 1.0–1.5 mm drops. Both satellites consistently observed 1.5–2.0 mm drops as the main contributors in convective precipitation. Additionally, FY-3G detected relatively higher number of raindrops smaller than 1.0 mm across various precipitation intensities, which may be primarily attributed to the higher sensitivity of the FY-3G Precipitation Measurement Radar compared to the GPM Dual-frequency Precipitation Radar.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deriving Tidal Constituent Estimates From GNSS Buoy Data in the Arctic","authors":"A. N. Vasulkar,&nbsp;M. Verlaan,&nbsp;D. C. Slobbe","doi":"10.1029/2024EA003775","DOIUrl":"10.1029/2024EA003775","url":null,"abstract":"<p>Measurements of tides are relatively sparse in the Arctic. This paper studies GNSS buoy tracks to complement existing data. Existing methods to perform tidal harmonic analysis of the buoy data are inadequate in the Arctic region because these methods for tidal analysis combine data from multiple buoy tracks, which is often infeasible in the Arctic. Moreover, we find that there are significant spatial and temporal variations in amplitudes and phases in baroclinic zones. To address these complexities, we introduce a new approach–Model-derived Fitting Method–to estimate the tidal current constituents (TCC) from a single buoy trajectory. Our study assesses the proposed method by analyzing GNSS buoy data from three Arctic regions characterized by barotropic or baroclinic tidal currents. Through detailed case studies in the Barents Sea, Chukchi Sea, and Baffin Bay, our approach demonstrates accuracy, robustness, and operational capabilities. In the Barents Sea, TCC estimates from two buoys were compared at a common location within their trajectories and compared against model estimates. In the Chukchi Sea's barotropic dominant zone, our method's estimates were evaluated against nearby ADCP mooring data. In Baffin Bay, known for baroclinic currents, a synthetic evaluation confirmed the method's effectiveness. Our study also highlights that phase variations along buoy trajectories can lead to frequency shifts in the spectrum, similar to the Doppler shift effect, particularly notable in regions with baroclinic tides.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003775","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Model Using the Fokker–Planck Equation and Alfvénic Drift for Estimating Proton Flux in Solar Energetic Particle Events","authors":"Ji-Hoon Ha,&nbsp;Jae-Hyung Lee,&nbsp;JaeHun Kim,&nbsp;Sang Cheol Han,&nbsp;Wonhyeong Yi,&nbsp;Hyun-Jun Nah","doi":"10.1029/2025EA004319","DOIUrl":"10.1029/2025EA004319","url":null,"abstract":"<p>Observations of solar energetic particles (SEPs) accelerated at collisionless shocks driven by coronal mass ejections (CMEs) highlight the importance of understanding proton transport in velocity space. In this study, we present an ensemble model based on the one-dimensional Fokker–Planck equation to estimate proton flux from CME-driven shocks propagating toward Earth. Using a CME analysis tool with coronagraph data, we derived initial conditions from key CME characteristics, including CME speed, angular width, Alfvén Mach number, and plasma beta. We then solved the one-dimensional Fokker–Planck equation under the test-particle regime, applicable to weak CME-driven shocks (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>M</mi>\u0000 <mi>A</mi>\u0000 </msub>\u0000 <mo>∼</mo>\u0000 <mn>1</mn>\u0000 <mo>–</mo>\u0000 <mn>4</mn>\u0000 </mrow>\u0000 <annotation> ${M}_{A}mathit{sim }1mbox{--}4$</annotation>\u0000 </semantics></math>) where the dynamical pressure of shock-accelerated particles is low compared to thermal pressure. Notably, our model incorporates the effects of Alfvén wave transmission and reflection at shocks, which significantly influence the efficiency of diffusive shock acceleration (DSA) and the transport of shock-accelerated particles in low-beta CME-driven shocks. To address systematic uncertainties from initial conditions obtained through the CME analysis tool and the nonlinear dynamics of the shock surface, including turbulence, we employed an ensemble approach for critical variables impacting DSA efficiency, such as the Alfvén Mach number, plasma beta, and upstream wave amplitude. By estimating proton flux during SEP events in 2024, our ensemble model produced predictions consistent with observations within a 1-sigma deviation, highlighting the importance of Alfvénic drift physics in SEP models of particle acceleration at shocks.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories","authors":"Qiqi Li,&nbsp;Manudeo Singh,&nbsp;Sonia Silvestri","doi":"10.1029/2025EA004201","DOIUrl":"10.1029/2025EA004201","url":null,"abstract":"<p>Alpine peatlands are one of the carbon reservoirs, provide vital ecosystem services, and support endangered biodiversity. However, they are globally understudied, including those in the Italian Alps, which host thousands of small sites averaging under 1 ha. Their complex geomorphology makes detection challenging with single-sensor, low-resolution remote sensing imagery. In the last decade, high resolution multi-source imagery (e.g., Sentinel series) and the cloud-based computation platforms (e.g., Google Earth Engine—GEE) have become available. Using these advancements, we developed a method to map the distribution of alpine peatlands. Utilizing 1 and 30 m digital elevation models (DEMs), optical, and microwave data sets, our method exploits a pixel-based Random Forest (RF) machine-learning algorithm on the GEE platform to map alpine peatlands in the Avisio River basin of the Italian Alps. The results show that the data set of single-year time series multi-source imagery, binary samples (peatland or non-peatland), and 30 m DEM is the most effective for mapping alpine peatlands. The method achieved an overall accuracy of 90.5%, with 81.8% true positives and 0.8% false positives. The method identified 11.635 km² of alpine peatlands, surpassing the 7.764 km² documented in official inventories, this discrepancy may be due to overestimation but also gaps in the existing reference inventory. In the classification process, DEM derived variables proved more effective than optical and microwave derived variables. Variable importance analysis in the RF model indicated that elevation is the most influential factor, while the microwave derived VV-VH difference (ascending track) contributes the least.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}