Remote Sensing of Environment最新文献

{"title":"Global multi-scale surface soil moisture retrieval coupling physical mechanisms and machine learning in the cloud environment","authors":"Zhenghao Li ,&nbsp;Qianqian Yang ,&nbsp;Jie Li ,&nbsp;Taoyong Jin ,&nbsp;Qiangqiang Yuan ,&nbsp;Huanfeng Shen ,&nbsp;Liangpei Zhang","doi":"10.1016/j.rse.2025.114928","DOIUrl":"10.1016/j.rse.2025.114928","url":null,"abstract":"<div><div>Surface soil moisture (SSM) is a critical state variable for water cycle research, and the advances in satellite remote sensing technology have provided a novel means for acquiring large-scale SSM data. While satellite microwave remote sensing-based SSM retrieval has emerged as the dominant approach for global SSM product development, offering numerous advantages, it still faces significant challenges. These include the trade-off between model accuracy and generalizability, the limitations of applying uniform retrieval models across diverse environments, and the inherent complexity and computational demands of the retrieval process. To address these common issues in microwave remote sensing-based SSM retrieval studies, this study proposed a cloud-based intelligent retrieval framework for global high-accuracy SSM estimation. This framework integrated physical mechanisms with machine learning models to ensure robust generalization and high retrieval accuracy; additionally, a model selection module was incorporated to enhance the overall retrieval accuracy by providing environment-specific retrieval models. In an assessment based on global validation sites for 1-km resolution SSM retrieval, the proposed framework performed well, with an R value of 0.851 and an ubRMSE of 0.058 m³·m⁻³. Furthermore, to mitigate the computational resource demands and time-consuming of the retrieval process, the SSM retrieval framework was implemented in a cloud environment utilizing Google Earth Engine, Drive, and Colab, thereby enabling seamless online operation of the entire retrieval process. This cloud-based intelligent retrieval framework facilitates real-time point-scale SSM retrieval on a global scale and rapid production of high-accuracy SSM products at the regional scale (SSM products for China at 1 km resolution can be accessed via <span><span>https://tinyurl.com/SSMproduct</span><svg><path></path></svg></span>). The SSM retrieval framework can significantly contribute to agricultural, environmental, and other related fields, and serve as a reference for the retrieval of other environmental variables.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114928"},"PeriodicalIF":11.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678294","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":"Balancing accuracy and feasibility in diurnal temperature modeling: A comparison of data-driven and physical-based models using geostationary satellite observations","authors":"Nur Fajar Trihantoro,&nbsp;Karin J. Reinke,&nbsp;Simon D. Jones","doi":"10.1016/j.rse.2025.114902","DOIUrl":"10.1016/j.rse.2025.114902","url":null,"abstract":"<div><div>The derivation of Diurnal Temperature Cycle (DTC) models from geostationary satellite data plays a critical role in temperature monitoring of the landscape and thermal anomaly applications such as wildfire detection. This study compares the performance of physical-based and data-driven DTC models on 1,305 study sites across Australia, leveraging Himawari-8 AHI middle-infrared (MIR) band 7 data. The physical-based model, GOT09 (based on Göttsche and Olesen study), achieved the highest accuracy, with a mean validation Root Mean Square Error (RMSE) of 2.41 K, but its practical application was limited by a lower model generation rate (48.77%), especially under high cloud cover conditions. Among data-driven methods, the proposed TRI model (named after the first author) balances accuracy and practical feasibility, achieving a validation RMSE of 3.62 K and a generation rate of 85.07%. The TRI model consistently generated reliable DTCs under various environmental conditions, including high cloud cover, outperforming alternative data-driven models such as RW (from Roberts-Wooster study), XIE (from Xie et al. study), and HAL (from Hally et al. study). Additionally, the TRI model maintained reliability across diverse land cover and climate types, showing only minimal variations in performance. The study further highlights strategies for addressing cloud and data availability challenges, proposing methods such as the use of previous day’s DTC or adjusting training data criteria in cloudy conditions. These approaches ensure a continuous temperature background where continuity of measurements is required, such as for wildfire detection. Overall, the research underscores the importance of balancing accuracy and model generation rates in DTC modeling, particularly for real-time applications. Future work could explore hybrid models and additional factors to further improve performance.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114902"},"PeriodicalIF":11.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670487","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 hybrid neural network for mangrove mapping considering tide states using Sentinel-2 imagery","authors":"Longjie Ye ,&nbsp;Qihao Weng","doi":"10.1016/j.rse.2025.114917","DOIUrl":"10.1016/j.rse.2025.114917","url":null,"abstract":"<div><div>Mangroves, as cradles of biodiversity and blue carbon reservoirs, are facing survival challenges due to climate change and anthropogenic disturbance. Precise and rapid mapping of mangrove forests has thus become highly relevant, which can provide essential information to support the conservation practices of such blue carbon resources. Existing machine learning algorithms for mangrove mapping are incapable of delivering precise cartographic solutions under dynamic tidal conditions because of poor transferability. This study developed a generalized approach for large-area mangrove mapping using a hybrid neural network integrated with a vision transformer to effectively capture representative features. To adapt mangrove mapping to the variety of tidal conditions, a vision transformer architecture was developed by encoding the fusion of three Sentinel-2 bands: Green, NIR, and SWIR. The ground truth dataset for the year 2021 was created from the composited Sentinel-2 images after interpreting Google Earth and drone images, which comprised 88,645 training samples (256 × 256 pixels per sample) and 24,969 test samples. We selected 30 coastal counties as test dataset in China to evaluate the effectiveness of the proposed network and produced a 10 m mangrove map that reported a total mangrove area of 28,006.24 ha in China in 2021, yielding an overall accuracy (OA) of 95.91 %. Compared to existing data products, Global Mangrove Watch 3.0, ESA WorldCover V200 and HGMF, our method outperformed the second-best product HGMF in mixed tide regions, by a margin of 9.19 % in OA and by 9.36 % in mean F1 score. Despite fluctuations in tide levels captured by Sentinel-2 imagery, the proposed method consistently yielded robust mangrove mapping results, highlighting effective derivation of tidal information. In comparison with previous mapping methods, the superior efficacy of the proposed network is distinctly discernible in distinguishing and delineating mangrove ecosystems in mixed tide regions, presenting prospects for improved monitoring of mangroves at regional and global scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114917"},"PeriodicalIF":11.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662679","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":"Discrepancies between time-based and real depth profiles in ocean lidar due to multiple scattering","authors":"Mingjia Shangguan,&nbsp;Yirui Guo,&nbsp;Zhuoyang Liao,&nbsp;Zhongping Lee","doi":"10.1016/j.rse.2025.114910","DOIUrl":"10.1016/j.rse.2025.114910","url":null,"abstract":"<div><div>Due to its ability to provide day-and-night profiling and high depth resolution, ocean lidar has become an important tool for marine remote sensing. However, a lidar system provides time-based measurements of backscattered photons, where the distance (or depth for vertical profiling) is a product of light speed in water and the time photons pass. When there are significant contributions of multiple scattering in the backscattered signals of ocean lidar, the perceived depth of these measured photons will be deeper than the real depth. Therefore, if the objective of a lidar system is to sense the vertical profile of particles, the present time-based depth profile will not match the real depth profile of particles in the water column. To address this discrepancy, we carried out semi-analytical Monte Carlo simulations for a wide range of water properties (represented by scattering coefficient, <em>b</em>), focusing on Case-1 water, with platforms including spaceborne, airborne, shipborne, and underwater. In the simulation process, it is assumed that the water column is vertically homogeneous, and the influence of sea surface fluctuations is ignored. Based on the simulated data, relationships between the discrepancy and <em>b</em>, as well as the radius of the received footprint on the water surface (<em>r</em>_<em>s</em>), are established. Sensitivity analysis indicates that the discrepancy is more sensitive to <em>b</em> than to <em>r</em>_<em>s</em>. Further, the impact of the absorption coefficient, scattering phase function, rough sea surface, and vertically non-uniform inherent optical properties on this discrepancy is discussed. Our results not only highlight the significance of considering multiple scattering, particularly for airborne and spaceborne platforms, in sensing the vertical profiles of particles but also provide guidance for interpreting backscattered signals in ocean lidar applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114910"},"PeriodicalIF":11.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662680","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":"Cross-scenario transfer learning for estimating mangrove nitrogen and phosphorus content from field hyperspectral data to SDGSAT-1 and Sentinel-2 images","authors":"Bolin Fu ,&nbsp;Yan Wu ,&nbsp;Li Zhang ,&nbsp;Weiwei Sun ,&nbsp;Yeqiao Wang ,&nbsp;Tengfang Deng ,&nbsp;Hongchang He ,&nbsp;Keyue Huang","doi":"10.1016/j.rse.2025.114923","DOIUrl":"10.1016/j.rse.2025.114923","url":null,"abstract":"<div><div>Mangroves play a critical role in maintaining biodiversity, supporting global carbon and nitrogen cycles, and contributing to the achievement of the United Nations Sustainable Development Goals (SDGs). Accurate estimation of their nitrogen and phosphorus content is essential for assessing the status of mangrove ecosystems. However, the spectral response characteristics of mangrove leaf nitrogen content (LNC) and leaf phosphorus content (LPC) remain unclear. These knowledge gaps hinder the development of robust predictive models across diverse environmental contexts. To overcome these issues, we collected 375 samples and 16,590 <em>in situ</em> full-spectrum hyperspectral data, and further proposed a novel Global-Fractional Order Sensitivity Analysis (G-FOSA) method. We analyzed for the first time the apparent and deep spectral characteristics of LNC and LPC for four typical mangrove species in China (<em>Avicennia marina</em>, <em>Acanthus ilicifolius</em>, <em>Kandelia candel</em> and <em>Aegiceras corniculatum</em>) using G-FOSA method. This study revealed that the LNC diagnostic wavelengths concentrated in the range of 697 nm–704 nm, while the LPC diagnostic wavelengths were mostly distributed between 691 nm–834 nm and 1869 nm–2236 nm. We developed a mechanism-guided retrieval framework based on these diagnostic wavelengths, and achieved the quantitative inversion from field diagnostic wavelengths to optical satellite (SDGSAT-1 and Sentinel-2) bands. Our experiment results confirmed that SDGSAT-1, the world's first science satellite dedicated to serving the 2030 Agenda for SDGs, performs better in estimating LNC and LPC (R² = 0.63). Finally, we utilized the advantages of cross-scenario transfer learning technology to design a novel domain adaptive transfer learning (DTL) model, which realized the cross-scenario retrieval of mangrove LNC and LPC across three typical mangrove regions, reducing estimation error (RMSE) by 0.6 %–41.1 % compared to the traditional FTL model. Our work provides new insights and a scientific basis for global mangrove conservation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114923"},"PeriodicalIF":11.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662681","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":"Understanding flood detection models across Sentinel-1 and Sentinel-2 modalities and benchmark datasets","authors":"Enrique Portalés-Julià ,&nbsp;Gonzalo Mateo-García ,&nbsp;Luis Gómez-Chova","doi":"10.1016/j.rse.2025.114882","DOIUrl":"10.1016/j.rse.2025.114882","url":null,"abstract":"<div><div>In recent years, research in flood mapping from remote sensing satellite imagery has predominantly focused on deep learning methods. While new flood segmentation models are increasingly being proposed, most of these works focus on advancing architectures trained on single datasets. Therefore, these studies overlook the intrinsic limitations and biases of the available training and evaluation data. This often leads to poor generalization and overconfident predictions when these models are used in real-world scenarios. To address this gap, the objective of this work is twofold. First, we train and evaluate flood segmentation models on five publicly available datasets including data from Sentinel-1, Sentinel-2, and both SAR and multispectral modalities. Our findings reveal that models achieving high detection accuracy on a single dataset (intra-dataset validation) do not necessarily generalize well to unseen datasets. In contrast, models trained on more diverse samples from multiple datasets demonstrate greater robustness and generalization ability. Furthermore, we present a dual-stream multimodal architecture that can be independently trained and tested on both single-modality and dual-modality datasets. This enables the integration of all the diversity and richness of the available data into a single unified framework. The results emphasize the need for a more comprehensive validation using diverse and well-designed datasets, particularly for multimodal approaches. If not adequately addressed, the shortcomings of current datasets can significantly limit the potential of deep learning-based operational flood mapping approaches.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640938","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":"Corrigendum to “One-step retrieval of water-quality parameters from satellite top-of-atmosphere measurements” [Remote Sensing of Environment, volume 323 (2025), 114709]","authors":"Hanyang Qiao, Zhongping Lee, Daosheng Wang, Zhihuang Zheng, Xiaomin Ye, Changyong Dou","doi":"10.1016/j.rse.2025.114908","DOIUrl":"https://doi.org/10.1016/j.rse.2025.114908","url":null,"abstract":"The authors regret an error in the affiliation of Changyong Dou in the published article. The incorrect affiliation:","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"52 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645286","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":"Distinct contribution of the blue spectral region and far-red solar-induced fluorescence to needle nitrogen and phosphorus assessment in coniferous nutrient trials with hyperspectral imagery","authors":"Peiye Li ,&nbsp;Tomas Poblete ,&nbsp;Alberto Hornero ,&nbsp;Jagannath Aryal ,&nbsp;Pablo J. Zarco-Tejada","doi":"10.1016/j.rse.2025.114915","DOIUrl":"10.1016/j.rse.2025.114915","url":null,"abstract":"<div><div>Accurate monitoring of plant nutrient status, especially nitrogen (N) and phosphorus (P) content, via remote sensing can facilitate precision forestry, with environmental and management benefits. In previous studies, plant traits derived from hyperspectral data via radiative transfer models (RTMs) and solar-induced chlorophyll fluorescence (SIF) effectively explained the observed variability in leaf N concentrations in crops. However, their contribution to leaf P concentration is unknown. Furthermore, such an approach might not be transferrable to coniferous stands, which are structurally complex and heterogeneous. We evaluated the potential of using physiological plant traits derived from airborne hyperspectral imagery to explain the observed variability in needle N and P concentrations in <em>Pinus radiata D. Don</em> (radiata pine) with four datasets collected over three years in established nutrient trials. RTM-derived data on pigment content in needles, including chlorophyll <em>a</em> + <em>b</em> (C_ab), carotenoid (C_ar), and anthocyanin contents (A_nth), as well as SIF quantified at the O₂A absorption band (SIF₇₆₀), explained variability in N (R² = 0.67–0.97 and NRMSE = 0.07–0.30) and P concentrations (R² = 0.60–0.95 and NRMSE = 0.09–0.27) in needles. Although C_ab was the most important predictor of needle N concentration (ranking C_ab &gt; A_nth &gt; SIF₇₆₀ &gt; C_ar), SIF₇₆₀ contributed the most to explain the variability of needle P concentration (SIF₇₆₀ &gt; A_nth &gt; C_ab &gt; C_ar). Moreover, the blue spectral region was essential for assessing P but not for explaining N variability in needles. Among all reflectance-based indices and inverted traits evaluated, the blue indices best explained the variability in needle P concentration, followed by C_ab, C_ar, and A_nth. The study revealed the distinct contribution of far-red SIF vs. the blue spectral region for needle P compared to needle N, describing new insights for the physiological assessment of nutrient levels in forest stands using hyperspectral imagery.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114915"},"PeriodicalIF":11.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645503","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":"Methodological considerations for studying spectral-plant diversity relationships","authors":"Christine I.B. Wallis ,&nbsp;Anna L. Crofts ,&nbsp;Robert Jackisch ,&nbsp;Shan Kothari ,&nbsp;Guillaume Tougas ,&nbsp;J. Pablo Arroyo-Mora ,&nbsp;Paul Hacker ,&nbsp;Nicholas Coops ,&nbsp;Margaret Kalacska ,&nbsp;Etienne Laliberté ,&nbsp;Mark Vellend","doi":"10.1016/j.rse.2025.114907","DOIUrl":"10.1016/j.rse.2025.114907","url":null,"abstract":"<div><div>The Spectral Variation Hypothesis (SVH) posits that higher spectral diversity indicates higher biodiversity, which would allow imaging spectroscopy to be used in biodiversity assessment and monitoring. However, its applicability varies due to ecological and methodological factors. Key methodological factors impacting spectral diversity metrics include spatial resolution, shadow removal, and spectral transformations. This study investigates how these methodological considerations affect the application of the SVH across ecosystems and sites. Using field and hyperspectral data from forest and open (e.g., wetland, grassland, savannah) ecosystems from five sites of the Canadian Airborne Biodiversity Observatory (CABO), we analyzed three variance-based spectral diversity metrics across and within vegetation sites, examining the effects of illumination corrections, spatial resolution, and shadow filtering on the spectral-plant functional diversity relationship. Our findings highlight that the relationship between spectral diversity metrics and functional diversity are strongly influenced by methods, especially spectral transformations. These illumination corrections notably impacted the spectral regions of importance and the resulting relationships to plant functional diversity. Depending on methodological choices, we observed correlations that varied not only in strength but also direction: in open vegetation we saw negative correlations when using brightness normalization, and positive correlations when using continuum removal. Shadow removal and spatial resolution were important but had less impact on the correlations. By systematically analyzing these methodological aspects, our study not only aims to guide researchers through potential challenges in SVH studies but also highlights the inherent sensitivity of spectral-functional diversity relationships to methodological choices. The variability and context-dependence of these relationships across and within sites emphasize the need for adaptable, site-specific approaches, presenting a key challenge in developing robust methods to enhance biodiversity monitoring and conservation strategies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114907"},"PeriodicalIF":11.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645504","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":"Correction and validation of Sentinel-1 IW radial velocity products using drifter and HF radar across the entire ocean environment","authors":"Lihua Wang ,&nbsp;Benhua Tan ,&nbsp;Xiaoqing Chu ,&nbsp;Hongmei Wang ,&nbsp;Yunxuan Zhou ,&nbsp;Weiwei Sun","doi":"10.1016/j.rse.2025.114909","DOIUrl":"10.1016/j.rse.2025.114909","url":null,"abstract":"<div><div>Since Sentinel-1 synthetic aperture radar (SAR) was launched in 2014, Interferometric Wide swath (IW) mode Level-2 radial velocity (RVL) products have been widely used to map fine-scale ocean surface current (OSC) in coastal zones. However, RVL product applications are restricted by non-geophysical and Wind-wave Induced Artifact Surface Velocity (WASV) errors. Previous studies have focused on improving the current retrieval accuracy in coastal zones, while neglecting open ocean regions and insufficient uncertainty analysis. To address these issues, a non-geophysical correction scheme suitable for both coastal and open sea is proposed by considering land coverage within SAR scenes. Corrected RVL products are validated using 1282 drifters and 78,054 HF radar points collected from the U.S. East Coast, West Coast, and Hawaiian Islands, showing overall accuracy improvements exceeding 60 %. To investigate the impact of WASV correction under different sea states (e.g. pure wind wave, wind wave dominant mix sea, swell dominant mix sea, and pure swell), a total of 127,534 matching points collected from January 2018 to May 2019 are used to assess the performance of four correction schemes. These include CDOP, KaDOP with wind and swell inputs, KaDOP with wind and wind-sea inputs, and CDOP-Y_n. A comprehensive comparison with HF radar current reveals that CDOP performs poorly in pure wind wave sea (RMSE up to 0.34 m/s), while incorporating sea state parameters enhances the retrieval accuracy. KaDOP and CDOP-Y_n yield comparable performance, while KaDOP performs better in pure wind or wind wave dominant mix sea, achieving RMSE of 0.21 m/s and a correlation coefficient (r) of 0.62. The correlation between SAR-derived and in-situ currents also varies with incidence angle, satellite track, and polarization. Overall, these results provide reliable OSC data for mesoscale and sub-mesoscale ocean dynamics research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114909"},"PeriodicalIF":11.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634005","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}