Pub Date : 2025-12-01DOI: 10.1016/j.rse.2025.115165
Dong Li , Anirudh Belwalkar , Tao Cheng , Kang Yu
Swath-based remote sensing data often exhibit spatial discontinuities after mapping to latitude-longitude grids at a regular spacing due to uneven sampling caused by varying viewing angles and limitations of the conventional center point-based gridding method (CPGrid). A more complex area-weighted gridding method can enhance spatial continuity, but it requires geometric calculations for each grid and is computationally intensive, especially for large-scale satellite imagery. To balance accuracy and efficiency, we proposed a probabilistic area-weighted gridding method (PAGrid), which approximates area-weighting by aggregating results from multiple randomized spatial perturbations. The performance of PAGrid was evaluated using all available Sentinel-3A and 3B observations in 2022 over Germany. Using the canopy absorption coefficient by chlorophyll in the red-edge band () as a test variable, we generated 8-day composites and compared results from CPGrid and PAGrid methods. PAGrid increased the median percentage of valid grid cells from 85% to 93% and reduced temporal fluctuations by 21% compared to CPGrid. Additionally, PAGrid improved the correlation (R2) between Sentinel-3A and 3B from 0.73 to 0.84, indicating enhanced data consistency. These improvements indicate that PAGrid is a practical and efficient approach for generating consistent and continuous gridded time series from swath-based satellite observations.
{"title":"PAGrid: A probabilistic area-weighted gridding method for seamless mapping of sentinel-3 swath data","authors":"Dong Li , Anirudh Belwalkar , Tao Cheng , Kang Yu","doi":"10.1016/j.rse.2025.115165","DOIUrl":"10.1016/j.rse.2025.115165","url":null,"abstract":"<div><div>Swath-based remote sensing data often exhibit spatial discontinuities after mapping to latitude-longitude grids at a regular spacing due to uneven sampling caused by varying viewing angles and limitations of the conventional center point-based gridding method (CPGrid). A more complex area-weighted gridding method can enhance spatial continuity, but it requires geometric calculations for each grid and is computationally intensive, especially for large-scale satellite imagery. To balance accuracy and efficiency, we proposed a probabilistic area-weighted gridding method (PAGrid), which approximates area-weighting by aggregating results from multiple randomized spatial perturbations. The performance of PAGrid was evaluated using all available Sentinel-3A and 3B observations in 2022 over Germany. Using the canopy absorption coefficient by chlorophyll in the red-edge band (<span><math><msub><mi>α</mi><mi>RE</mi></msub></math></span>) as a test variable, we generated 8-day composites and compared results from CPGrid and PAGrid methods. PAGrid increased the median percentage of valid grid cells from 85% to 93% and reduced temporal fluctuations by 21% compared to CPGrid. Additionally, PAGrid improved the correlation (R<sup>2</sup>) between Sentinel-3A and 3B <span><math><msub><mi>α</mi><mi>RE</mi></msub></math></span> from 0.73 to 0.84, indicating enhanced data consistency. These improvements indicate that PAGrid is a practical and efficient approach for generating consistent and continuous gridded time series from swath-based satellite observations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115165"},"PeriodicalIF":11.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657975","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}
Pub Date : 2025-11-29DOI: 10.1016/j.rse.2025.115161
Mingxia Dong , Shouyang Liu , Marie Weiss , Aojie Yin , Chen Zhu , Benoit de Solan , Wei Guo , Fernandes Richard , Wenjuan Li , Xia Yao , James Burridge , Zhen Chen , Yanfeng Ding , Frédéric Baret
Green Area Index (GAI) is a key crop trait obtained through remote sensing with wide applications in agriculture. Although 3D model-driven approaches to retrieve GAI from multispectral reflectance observations are appealing, they are constrained by limitations in the realism of simulated datasets used for training. This study comprehensively explored how to integrate prior information—such as soil background, leaf optical properties, and canopy structure—into radiative transfer models to improve GAI retrieval. A suite of models (MARMIT-2 for soil reflectance, PROSPECT for leaf optical properties, ADEL-Wheat for canopy structure, and LESS for radiative transfer) was employed to generate five simulation datasets incorporating different combinations of prior information. Support Vector Regression (SVR) models were independently trained on these simulated datasets and validated against an extensive data set made of 310 samples of GAI ground measurements and the corresponding SuperDove satellite data. Our results show that stage-specific GAI retrieval integrating detailed prior information on soil and leaf properties (R2 = 0.93, RMSE = 0.47) notably outperforms standard model inversion approaches (R2 = 0.82, RMSE = 0.73). The improved realism of the training dataset stems from three key strategies was discussed in detail including: (1) employing models that integrates physical and biological knowledge; (2) narrowing the training space; and (3) minimizing distribution shifts. While this study focused on GAI estimation for wheat crops using SuperDove observations, the findings can be extended to other crops, vegetation variables, and satellite systems.
{"title":"Integrating prior information for improving 3D model-driven GAI estimation with application to wheat crops","authors":"Mingxia Dong , Shouyang Liu , Marie Weiss , Aojie Yin , Chen Zhu , Benoit de Solan , Wei Guo , Fernandes Richard , Wenjuan Li , Xia Yao , James Burridge , Zhen Chen , Yanfeng Ding , Frédéric Baret","doi":"10.1016/j.rse.2025.115161","DOIUrl":"10.1016/j.rse.2025.115161","url":null,"abstract":"<div><div>Green Area Index (GAI) is a key crop trait obtained through remote sensing with wide applications in agriculture. Although 3D model-driven approaches to retrieve GAI from multispectral reflectance observations are appealing, they are constrained by limitations in the realism of simulated datasets used for training. This study comprehensively explored how to integrate prior information—such as soil background, leaf optical properties, and canopy structure—into radiative transfer models to improve GAI retrieval. A suite of models (MARMIT-2 for soil reflectance, PROSPECT for leaf optical properties, ADEL-Wheat for canopy structure, and LESS for radiative transfer) was employed to generate five simulation datasets incorporating different combinations of prior information. Support Vector Regression (SVR) models were independently trained on these simulated datasets and validated against an extensive data set made of 310 samples of GAI ground measurements and the corresponding SuperDove satellite data. Our results show that stage-specific GAI retrieval integrating detailed prior information on soil and leaf properties (R<sup>2</sup> = 0.93, RMSE = 0.47) notably outperforms standard model inversion approaches (R<sup>2</sup> = 0.82, RMSE = 0.73). The improved realism of the training dataset stems from three key strategies was discussed in detail including: (1) employing models that integrates physical and biological knowledge; (2) narrowing the training space; and (3) minimizing distribution shifts. While this study focused on GAI estimation for wheat crops using SuperDove observations, the findings can be extended to other crops, vegetation variables, and satellite systems.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115161"},"PeriodicalIF":11.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613834","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}
Pub Date : 2025-11-29DOI: 10.1016/j.rse.2025.115163
Aakash Chhabra , Christoph Rüdiger , James Hilton , Rachael H. Nolan , Eli R. Bendall , Marta Yebra , Thomas Jagdhuber
Wildfires, intensified by climate change, necessitate advanced methods for accurate and near-real-time fire severity mapping to improve emergency response and post-fire recovery strategies. Satellite remote sensing, combined with supervised learning approaches, enhances the accuracy and efficiency of fire severity mapping. This study introduces Decision-Based Hierarchical Learning (DBHL), a novel multi-sensor fire severity classification model that integrates Synthetic Aperture Radar (SAR; Sentinel-1 backscatter) and optical (Sentinel-2 reflectance) data. The model was applied to assess wildfire impacts on temperate forests during the 2019/20 “Black Summer” wildfire season in south-eastern Australia. DBHL incorporated SAR-based RADAR-Vegetation Structure Perpendicular Index (R-VSPI) and optical-based Vegetation Structure Perpendicular Index (VSPI) as candidate indices. By integrating these complementary datasets, DBHL leverages both structural and physiological changes as fire severity indicators, addressing limitations in single-sensor approaches. A pixel-wise approach was employed to spatially upscale the applicability of the R-VSPI and VSPI indices for fire severity assessment across the entire region. Using field data, the sensitivities of the R-VSPI and VSPI indices were validated during the immediate post-fire to one-year post-fire period. DBHL was trained and evaluated with a focus on comparing its performance against independent R-VSPI and VSPI classifications. The findings reveal the unique strengths of each index across various fire severity classes, demonstrating their complementary value. R-VSPI is more sensitive to structural changes in forests, while VSPI excels in identifying changes related to canopy-level disturbances. One-year post-fire recovery analysis shows distinct spatial patterns, with VSPI indicating faster recovery in surface vegetation and R-VSPI highlighting prolonged structural recovery. The DBHL model demonstrates the complementary strengths of the indices, allowing fire severity assessments to be contextualized across vertical vegetation strata, distinguishing between canopy-based damage indicators and underlying structural changes. DBHL outperformed single-sensor approaches, achieving the highest classification accuracy (overall accuracy=88.89%, kappa=0.86), particularly improving differentiation of Moderate (partial canopy scorch) and High (full crown scorch) severity with a producer’s accuracy of 100%, and 80%, respectively. Future research is aimed at integrating multi-wavelength SAR, including L-band (1.25 GHz) and P-band (0.43 GHz), along with LiDAR measurements to enhance structural fire severity assessments.
{"title":"Combined use of R-VSPI and VSPI for enhanced quantification of fire severity in south-eastern Australian forests","authors":"Aakash Chhabra , Christoph Rüdiger , James Hilton , Rachael H. Nolan , Eli R. Bendall , Marta Yebra , Thomas Jagdhuber","doi":"10.1016/j.rse.2025.115163","DOIUrl":"10.1016/j.rse.2025.115163","url":null,"abstract":"<div><div>Wildfires, intensified by climate change, necessitate advanced methods for accurate and near-real-time fire severity mapping to improve emergency response and post-fire recovery strategies. Satellite remote sensing, combined with supervised learning approaches, enhances the accuracy and efficiency of fire severity mapping. This study introduces Decision-Based Hierarchical Learning (DBHL), a novel multi-sensor fire severity classification model that integrates Synthetic Aperture Radar (SAR; Sentinel-1 backscatter) and optical (Sentinel-2 reflectance) data. The model was applied to assess wildfire impacts on temperate forests during the 2019/20 “Black Summer” wildfire season in south-eastern Australia. DBHL incorporated SAR-based RADAR-Vegetation Structure Perpendicular Index (R-VSPI) and optical-based Vegetation Structure Perpendicular Index (VSPI) as candidate indices. By integrating these complementary datasets, DBHL leverages both structural and physiological changes as fire severity indicators, addressing limitations in single-sensor approaches. A pixel-wise approach was employed to spatially upscale the applicability of the R-VSPI and VSPI indices for fire severity assessment across the entire region. Using field data, the sensitivities of the R-VSPI and VSPI indices were validated during the immediate post-fire to one-year post-fire period. DBHL was trained and evaluated with a focus on comparing its performance against independent R-VSPI and VSPI classifications. The findings reveal the unique strengths of each index across various fire severity classes, demonstrating their complementary value. R-VSPI is more sensitive to structural changes in forests, while VSPI excels in identifying changes related to canopy-level disturbances. One-year post-fire recovery analysis shows distinct spatial patterns, with VSPI indicating faster recovery in surface vegetation and R-VSPI highlighting prolonged structural recovery. The DBHL model demonstrates the complementary strengths of the indices, allowing fire severity assessments to be contextualized across vertical vegetation strata, distinguishing between canopy-based damage indicators and underlying structural changes. DBHL outperformed single-sensor approaches, achieving the highest classification accuracy (overall accuracy=88.89%, kappa=0.86), particularly improving differentiation of Moderate (partial canopy scorch) and High (full crown scorch) severity with a producer’s accuracy of 100%, and 80%, respectively. Future research is aimed at integrating multi-wavelength SAR, including L-band (1.25 GHz) and P-band (0.43 GHz), along with LiDAR measurements to enhance structural fire severity assessments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115163"},"PeriodicalIF":11.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613832","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}
Pub Date : 2025-11-28DOI: 10.1016/j.rse.2025.115154
Song Jiang , Yongling Zhao , Lei Zhao , Dominik Strebel , Dominique Derome , Diana Ürge-Vorsatz , Jan Carmeliet , Jian Peng
Urban overheating, a confluence of urban heat island and climate change, is escalating alongside the rapid global urbanization. While previous studies have examined how urbanization and vegetation influence surface urban heat islands (SUHI), their nonlinear effects across climate zones remain insufficiently understood. Here, we present a globally consistent assessment of 6022 cities using MODIS Aqua data (MYD11A2) from the summer of 2019, validated with multi-year records (2017–2021), through a self-developed, scalable SUHI quantification method that enables cross-climate comparisons. Our results reveal distinct rapid- and slow-growth zones in SUHI intensification with urban size, with the fastest increase occurring in small cities below the top 20% of global urban size. This uneven rise in SUHI intensity stems from the synergistic effects of urban expansion and vegetation loss. Vegetation cooling exhibits a clear saturation beyond an inflection point, with the equatorial zone showing both weaker cooling efficiency and earlier saturation onset. Using a dual-perspective framework that integrates absolute and relative temperature metrics, we further show that Global South cities experience compounded thermal stress—featuring not only 3.37 ± 0.14 °C higher absolute temperatures due to their geographical setting, but also 0.24 ± 0.05 °C greater SUHI intensity than cities in the Global North. Together, these findings demonstrate that the effectiveness of heat mitigation strategies varies across climates and urbanization stages, underscoring the heightened vulnerability of smaller cities and the need for context-specific, climate-sensitive planning interventions. This study provides a globally integrated yet regionally differentiated understanding of surface urban heat and establishes a planning-relevant framework to guide targeted and climate-sensitive urban heat mitigation strategies.
{"title":"Nonlinear impacts of urban size and vegetation cover on global surface urban heat: Insights from 6022 cities","authors":"Song Jiang , Yongling Zhao , Lei Zhao , Dominik Strebel , Dominique Derome , Diana Ürge-Vorsatz , Jan Carmeliet , Jian Peng","doi":"10.1016/j.rse.2025.115154","DOIUrl":"10.1016/j.rse.2025.115154","url":null,"abstract":"<div><div>Urban overheating, a confluence of urban heat island and climate change, is escalating alongside the rapid global urbanization. While previous studies have examined how urbanization and vegetation influence surface urban heat islands (SUHI), their nonlinear effects across climate zones remain insufficiently understood. Here, we present a globally consistent assessment of 6022 cities using MODIS Aqua data (MYD11A2) from the summer of 2019, validated with multi-year records (2017–2021), through a self-developed, scalable SUHI quantification method that enables cross-climate comparisons. Our results reveal distinct rapid- and slow-growth zones in SUHI intensification with urban size, with the fastest increase occurring in small cities below the top 20% of global urban size. This uneven rise in SUHI intensity stems from the synergistic effects of urban expansion and vegetation loss. Vegetation cooling exhibits a clear saturation beyond an inflection point, with the equatorial zone showing both weaker cooling efficiency and earlier saturation onset. Using a dual-perspective framework that integrates absolute and relative temperature metrics, we further show that Global South cities experience compounded thermal stress—featuring not only 3.37 ± 0.14 °C higher absolute temperatures due to their geographical setting, but also 0.24 ± 0.05 °C greater SUHI intensity than cities in the Global North. Together, these findings demonstrate that the effectiveness of heat mitigation strategies varies across climates and urbanization stages, underscoring the heightened vulnerability of smaller cities and the need for context-specific, climate-sensitive planning interventions. This study provides a globally integrated yet regionally differentiated understanding of surface urban heat and establishes a planning-relevant framework to guide targeted and climate-sensitive urban heat mitigation strategies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115154"},"PeriodicalIF":11.4,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611937","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}
Forest ecosystems, covering about one-third of the Earth’s mainland, are vital for providing essential ecosystem services. However, their extent and health are threatened by climate change. Remote sensing has the potential to evaluate the condition and functionality of global forests, but methodological and technological challenges impede the quantitative estimation of forest traits from spaceborne imagery. The development of next-generation sensors and advanced retrieval algorithms offers the chance to overcome these obstacles, though such data and models need development and testing. In this study we investigated and compared the potential of machine learning regression algorithms (MLRA) and hybrid approaches combining MLRA and radiative transfer simulations for the retrieval of forest traits from PRISMA spaceborne imagery. We tested the method in the Ticino Park, a mid-latitude forest located in northern Italy. We conducted an intensive field campaign in the summer of 2022 concurrently with four PRISMA overpasses to collect trait samples for calibrating and validating the retrieval schemes. Our results demonstrated the capability of PRISMA images and hybrid models to accurately quantify Leaf Chlorophyll Content (LCC) (r2=0.67, nRMSE=13.5 %), Leaf Nitrogen Content (LNC) (r2=0.82, nRMSE=9.5 %), Leaf Water Content (LWC) (r2=0.98, nRMSE=3.5 %), Leaf Mass per Area (LMA) (r2=0.93, nRMSE=6.6 %) and Leaf Area Index (LAI) (r2=0.83, nRMSE=11.7 %) in forest ecosystems. Conversely, Leaf Carotenoid Content (Ccx) yielded a lower accuracy (r2=0.43, nRMSE=13.5 %), indicating potential for improvement. The results evidenced a slightly superior performance of hybrid approaches over purely statistical approaches. The application of the models to PRISMA images acquired before and after a severe drought event corroborated the effectiveness of the models to provide reliable estimates in operational conditions. This underscores the valuable role of next-generation models and hyperspectral spaceborne imagery for forest monitoring. To our knowledge, this is the first study to appraise hybrid retrieval schemes with real spaceborne hyperspectral data for mapping multiple forest traits, thereby providing a reference framework for future applications.
{"title":"Appraising retrieval schemes from spaceborne hyperspectral imagery for mapping leaf and canopy traits in forest ecosystems","authors":"Giulia Tagliabue , Cinzia Panigada , Beatrice Savinelli , Luigi Vignali , Micol Rossini","doi":"10.1016/j.rse.2025.115145","DOIUrl":"10.1016/j.rse.2025.115145","url":null,"abstract":"<div><div>Forest ecosystems, covering about one-third of the Earth’s mainland, are vital for providing essential ecosystem services. However, their extent and health are threatened by climate change. Remote sensing has the potential to evaluate the condition and functionality of global forests, but methodological and technological challenges impede the quantitative estimation of forest traits from spaceborne imagery. The development of next-generation sensors and advanced retrieval algorithms offers the chance to overcome these obstacles, though such data and models need development and testing. In this study we investigated and compared the potential of machine learning regression algorithms (MLRA) and hybrid approaches combining MLRA and radiative transfer simulations for the retrieval of forest traits from PRISMA spaceborne imagery. We tested the method in the Ticino Park, a mid-latitude forest located in northern Italy. We conducted an intensive field campaign in the summer of 2022 concurrently with four PRISMA overpasses to collect trait samples for calibrating and validating the retrieval schemes. Our results demonstrated the capability of PRISMA images and hybrid models to accurately quantify Leaf Chlorophyll Content (LCC) (r<sup>2</sup>=0.67, nRMSE=13.5 %), Leaf Nitrogen Content (LNC) (r<sup>2</sup>=0.82, nRMSE=9.5 %), Leaf Water Content (LWC) (r<sup>2</sup>=0.98, nRMSE=3.5 %), Leaf Mass per Area (LMA) (r<sup>2</sup>=0.93, nRMSE=6.6 %) and Leaf Area Index (LAI) (r<sup>2</sup>=0.83, nRMSE=11.7 %) in forest ecosystems. Conversely, Leaf Carotenoid Content (Ccx) yielded a lower accuracy (r<sup>2</sup>=0.43, nRMSE=13.5 %), indicating potential for improvement. The results evidenced a slightly superior performance of hybrid approaches over purely statistical approaches. The application of the models to PRISMA images acquired before and after a severe drought event corroborated the effectiveness of the models to provide reliable estimates in operational conditions. This underscores the valuable role of next-generation models and hyperspectral spaceborne imagery for forest monitoring. To our knowledge, this is the first study to appraise hybrid retrieval schemes with real spaceborne hyperspectral data for mapping multiple forest traits, thereby providing a reference framework for future applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115145"},"PeriodicalIF":11.4,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609774","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}
Pub Date : 2025-11-27DOI: 10.1016/j.rse.2025.115139
Alexis Mouche , Arthur Avenas , Paul Chang , Bertrand Chapron , Théo Cévaër , Clément Combot , Joseph Courtney , Quentin Febvre , Ralph C. Foster , Antoine Grouazel , Masahiro Hayashi , Takeshi Horinouchi , Yasutaka Ikuta , Osamu Isoguchi , Christopher R. Jackson , Zorana Jelenak , John A. Knaff , Sébastien Langlade , Jean-Renaud Miadana , Frédéric Nouguier , Léo Vinour
We examine how, over its first decade, the Sentinel-1 mission has advanced the estimation of ocean surface winds over tropical cyclones, supported their global monitoring, and fostered related research. C-band S1 Synthetic Aperture Radar have been instrumental in refining wind retrieval algorithms, relying on the co- and cross-polarized normalized radar cross-section sensitivity to the ocean wind–waves, especially for major category (3-5) tropical cyclones observed in wide swath modes. Systematic comparisons with airborne multi-frequency radiometer measurements confirm the ability of Synthetic Aperture Radar to provide estimates of the ocean surface wind field at kilometer resolution during a tropical cyclone (bias of 0.08 m/s, standard deviation of 3.84 m/s, correlation of 0.97) and to extract its main characteristics, including the center of the wind circulation, the maximum possible extent of a given wind speed around the tropical cyclone and the radius of maximum wind. Now available globally and in near-real time at operational tropical cyclone forecasting centers, Synthetic Aperture Radar observations are part of the mix used to diagnose the state of the tropical cyclones and issue warning bulletins. Sentinel-1 decametric-backscatter and kilometric-wind resolutions have also been shown to be a reference for interpreting and calibrating other satellite, in situ measurements, and algorithms. Sentinel-1 synoptic observations benefit from new observing systems. Their synergistic use enables us to provide improved temporal resolution of TCs inner core structural parameters. Research efforts exploiting Synthetic Aperture Radar measurements to document such a dynamical system, infer tropical cyclone boundary layer properties, TC-generated waves, and interactions with the upper ocean are presented. This growing increase in acquisitions from multiple C-band Synthetic Aperture Radar missions (e.g. the Radarsat Constellation Mission) over TCs (a factor of 4 over the last decade), combined with other observational data and numerical models, opens opportunities to revisit robust data-driven approaches. These advances shall support a better representation of tropical cyclones in digital twin frameworks. Both algorithm improvements on existing and future Synthetic Aperture Radar missions are attractive perspectives to provide more accurate predictions and a deeper understanding of these complex weather systems.
{"title":"Fostering tropical cyclone research and applications with Synthetic Aperture Radar","authors":"Alexis Mouche , Arthur Avenas , Paul Chang , Bertrand Chapron , Théo Cévaër , Clément Combot , Joseph Courtney , Quentin Febvre , Ralph C. Foster , Antoine Grouazel , Masahiro Hayashi , Takeshi Horinouchi , Yasutaka Ikuta , Osamu Isoguchi , Christopher R. Jackson , Zorana Jelenak , John A. Knaff , Sébastien Langlade , Jean-Renaud Miadana , Frédéric Nouguier , Léo Vinour","doi":"10.1016/j.rse.2025.115139","DOIUrl":"10.1016/j.rse.2025.115139","url":null,"abstract":"<div><div>We examine how, over its first decade, the Sentinel-1 mission has advanced the estimation of ocean surface winds over tropical cyclones, supported their global monitoring, and fostered related research. C-band S1 Synthetic Aperture Radar have been instrumental in refining wind retrieval algorithms, relying on the co- and cross-polarized normalized radar cross-section sensitivity to the ocean wind–waves, especially for major category (3-5) tropical cyclones observed in wide swath modes. Systematic comparisons with airborne multi-frequency radiometer measurements confirm the ability of Synthetic Aperture Radar to provide estimates of the ocean surface wind field at kilometer resolution during a tropical cyclone (bias of 0.08 m/s, standard deviation of 3.84 m/s, correlation of 0.97) and to extract its main characteristics, including the center of the wind circulation, the maximum possible extent of a given wind speed around the tropical cyclone and the radius of maximum wind. Now available globally and in near-real time at operational tropical cyclone forecasting centers, Synthetic Aperture Radar observations are part of the mix used to diagnose the state of the tropical cyclones and issue warning bulletins. Sentinel-1 decametric-backscatter and kilometric-wind resolutions have also been shown to be a reference for interpreting and calibrating other satellite, in situ measurements, and algorithms. Sentinel-1 synoptic observations benefit from new observing systems. Their synergistic use enables us to provide improved temporal resolution of TCs inner core structural parameters. Research efforts exploiting Synthetic Aperture Radar measurements to document such a dynamical system, infer tropical cyclone boundary layer properties, TC-generated waves, and interactions with the upper ocean are presented. This growing increase in acquisitions from multiple C-band Synthetic Aperture Radar missions (e.g. the Radarsat Constellation Mission) over TCs (a factor of 4 over the last decade), combined with other observational data and numerical models, opens opportunities to revisit robust data-driven approaches. These advances shall support a better representation of tropical cyclones in digital twin frameworks. Both algorithm improvements on existing and future Synthetic Aperture Radar missions are attractive perspectives to provide more accurate predictions and a deeper understanding of these complex weather systems.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115139"},"PeriodicalIF":11.4,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611939","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}
Pub Date : 2025-11-26DOI: 10.1016/j.rse.2025.115132
Yiheng Zhou , Ailong Ma , Junjue Wang , Zihang Chen , Yanfei Zhong
Multimodal remote sensing imagery has been widely used in many fields. However, in specific scenarios, it is challenging to acquire the key modality, making it difficult to extract the land cover mapping information in conditions of missing modalities. Existing missing modality learning methods transfer historical complete modal feature knowledge to single-modal features disrupting their feature distribution, and leading to poor performance in downstream tasks. To address the aforementioned issue, a multimodal remote sensing land cover dataset called EarthMiss is designed to simulate real-world missing modality scenarios. EarthMiss comprises 3355 pairs of 0.6-meter high-resolution Optical and SAR images collected from 13 cities spanning five continents, including 8 common types of land cover objects, making it the multimodal remote sensing dataset with the highest number of classes at this high resolution. Besides, a remote sensing meta modal representation framework named MetaRS is proposed for missing modality land cover mapping task. MetaRS presents a meta-modal aware module to extract modality-invariant features for missing modality feature recovery, and a meta-modal representation regularization training strategy to guide meta-modal focus on task-related feature representation. Specifically, we disentangle features by supervising the covariance matrix of multi-modal features, and knowledge transfer takes place solely, thereby ensuring the consistency of the transferred knowledge. Then, a meta-modal representation branch fuses the meta-features of all modalities and calculates the prediction loss for them. Comprehensive experiments conducted across EarthMiss dataset, four additional benchmarks, and a 2023 Libyan-flood case study demonstrate that MetaRS significantly surpasses existing methods, and provides a promising alternative for multimodal remote sensing applications. The code and dataset used in this study are publicly available at https://github.com/Yi-Heng/EarthMiss
{"title":"Remote sensing meta modal representation for missing modality land cover mapping: From EarthMiss dataset to MetaRS method","authors":"Yiheng Zhou , Ailong Ma , Junjue Wang , Zihang Chen , Yanfei Zhong","doi":"10.1016/j.rse.2025.115132","DOIUrl":"10.1016/j.rse.2025.115132","url":null,"abstract":"<div><div>Multimodal remote sensing imagery has been widely used in many fields. However, in specific scenarios, it is challenging to acquire the key modality, making it difficult to extract the land cover mapping information in conditions of missing modalities. Existing missing modality learning methods transfer historical complete modal feature knowledge to single-modal features disrupting their feature distribution, and leading to poor performance in downstream tasks. To address the aforementioned issue, a multimodal remote sensing land cover dataset called <strong>EarthMiss</strong> is designed to simulate real-world missing modality scenarios. EarthMiss comprises 3355 pairs of 0.6-meter high-resolution Optical and SAR images collected from 13 cities spanning five continents, including 8 common types of land cover objects, making it the multimodal remote sensing dataset with the highest number of classes at this high resolution. Besides, a remote sensing meta modal representation framework named <strong>MetaRS</strong> is proposed for missing modality land cover mapping task. MetaRS presents a meta-modal aware module to extract modality-invariant features for missing modality feature recovery, and a meta-modal representation regularization training strategy to guide meta-modal focus on task-related feature representation. Specifically, we disentangle features by supervising the covariance matrix of multi-modal features, and knowledge transfer takes place solely, thereby ensuring the consistency of the transferred knowledge. Then, a meta-modal representation branch fuses the meta-features of all modalities and calculates the prediction loss for them. Comprehensive experiments conducted across EarthMiss dataset, four additional benchmarks, and a 2023 Libyan-flood case study demonstrate that MetaRS significantly surpasses existing methods, and provides a promising alternative for multimodal remote sensing applications. The code and dataset used in this study are publicly available at <span><span>https://github.com/Yi-Heng/EarthMiss</span><svg><path></path></svg></span></div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115132"},"PeriodicalIF":11.4,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598724","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}
Pub Date : 2025-11-26DOI: 10.1016/j.rse.2025.115157
Jianing Wei , Kang Yang , Yuxin Zhu , Yuhan Wang , Xiaoyu Guo
Buried lakes are widely distributed on the Greenland Ice Sheet (GrIS) after summer. Some of these lakes may drain over winter, thereby delivering meltwater into the ice sheet and potentially influencing ice flow dynamics. However, to date, only a limited number of buried lake drainages (BLDs) have been identified and their spatiotemporal dynamics across the GrIS remain unclear. Here we detect pan-GrIS wintertime BLDs by integrating Sentinel-1 and -2 satellite imagery and ArcticDEM data. We begin by locating potential buried lakes with topographic depressions. Then we identify the depressions with significant wintertime backscatter increases as potential BLDs using Sentinel-1 SAR imagery. Next, we map the maximum summertime meltwater area using Sentinel-2 imagery and select the potential BLDs with sufficient summertime meltwater as final BLDs. Finally, we categorize these final BLDs into three types (complete BLDs, partial BLDs, and low-confidence BLDs), and we also investigate potential ice velocity anomalies (IVAs) triggered by BLDs using ice velocity data. The results show that: (1) 167 complete and partial BLDs are identified over seven winters from 2017 to 2023 on the GrIS, including 25 cascade drainages; their spatiotemporal distributions vary significantly, with most BLDs detected at the NW, CW, and SW basins in November. (2) Wintertime BLDs potentially trigger 10 significant IVAs (up to 50 %), and may lead to a net increase in annual ice motion. (3) Wintertime IVAs triggered by BLDs are significantly higher than summertime IVAs triggered by supraglacial lake drainages; they propagate downstream along subglacial hydrologic pathways rather than ice flowlines. (4) BLDs can even trigger rerouting of subglacial hydrologic pathways over a short time period. In conclusion, we present a new method of detecting sparse wintertime BLDs over large areas and reveal that BLDs have a more profound effect on ice flow dynamics than previously assumed.
{"title":"Satellite monitoring of Greenland wintertime buried lake drainage and potential ice flow response","authors":"Jianing Wei , Kang Yang , Yuxin Zhu , Yuhan Wang , Xiaoyu Guo","doi":"10.1016/j.rse.2025.115157","DOIUrl":"10.1016/j.rse.2025.115157","url":null,"abstract":"<div><div>Buried lakes are widely distributed on the Greenland Ice Sheet (GrIS) after summer. Some of these lakes may drain over winter, thereby delivering meltwater into the ice sheet and potentially influencing ice flow dynamics. However, to date, only a limited number of buried lake drainages (BLDs) have been identified and their spatiotemporal dynamics across the GrIS remain unclear. Here we detect pan-GrIS wintertime BLDs by integrating Sentinel-1 and -2 satellite imagery and ArcticDEM data. We begin by locating potential buried lakes with topographic depressions. Then we identify the depressions with significant wintertime backscatter increases as potential BLDs using Sentinel-1 SAR imagery. Next, we map the maximum summertime meltwater area using Sentinel-2 imagery and select the potential BLDs with sufficient summertime meltwater as final BLDs. Finally, we categorize these final BLDs into three types (complete BLDs, partial BLDs, and low-confidence BLDs), and we also investigate potential ice velocity anomalies (IVAs) triggered by BLDs using ice velocity data. The results show that: (1) 167 complete and partial BLDs are identified over seven winters from 2017 to 2023 on the GrIS, including 25 cascade drainages; their spatiotemporal distributions vary significantly, with most BLDs detected at the NW, CW, and SW basins in November. (2) Wintertime BLDs potentially trigger 10 significant IVAs (up to 50 %), and may lead to a net increase in annual ice motion. (3) Wintertime IVAs triggered by BLDs are significantly higher than summertime IVAs triggered by supraglacial lake drainages; they propagate downstream along subglacial hydrologic pathways rather than ice flowlines. (4) BLDs can even trigger rerouting of subglacial hydrologic pathways over a short time period. In conclusion, we present a new method of detecting sparse wintertime BLDs over large areas and reveal that BLDs have a more profound effect on ice flow dynamics than previously assumed.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115157"},"PeriodicalIF":11.4,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598717","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}
Pub Date : 2025-11-25DOI: 10.1016/j.rse.2025.115160
Wenjuan Li , Marie Weiss , Samuel Buis , Aleixandre Verger , Sylvain Jay , Zihan Ren , Wenbin Wu , Jingyi Jiang , Alexis Comar , Benoit De Solan
Near-real-time (NRT) daily crop monitoring at the field scale is crucial for precision agriculture, yet remains challenging due to limitations in the spatial or temporal resolution of existing remote sensing methods. While Sentinel-2 provides adequate spatial resolution for field-level applications, its temporal resolution is insufficient for capturing rapid crop dynamics, especially in cloudy regions. Existing spatiotemporal fusion techniques require multiple clear-sky images and lack true NRT capability, while ground-based sensors offer continuous monitoring but with limited spatial coverage. To address these limitations, this study develops the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm, a novel approach based on a Bayesian dynamic linear model and Kalman filtering. The algorithm uniquely integrates Sentinel-2 imagery with continuous measurements from Internet of Things for Agriculture (IoTA) systems to generate daily 10-m Green Area Index (GAI) products. Its recursive framework supports both forward prediction in NRT mode following satellite overpasses and backward updating to refine historical profiles. Implemented over French wheat fields using 34 IoTA systems and Sentinel-2 time series from 2019, the algorithm effectively enhanced spatiotemporal completeness and accuracy (R = 0.75–0.98, RMSE = 0.1–0.49). A comprehensive leave-one-out Sentinel-2 evaluation demonstrated its superiority over the current Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm. Ground validation using handheld RGB cameras further confirmed the accuracy of the GAI products from the new algorithm (RMSE = 0.5). The NRT-GSF framework offers a robust and operationally solution for daily, high-resolution crop GAI mapping in NRT mode, and it can be extended to other traits or applications in the near-real-time context.
近实时（NRT）田间作物日常监测对于精准农业至关重要，但由于现有遥感方法的空间或时间分辨率的限制，仍然具有挑战性。虽然Sentinel-2为田间应用提供了足够的空间分辨率，但其时间分辨率不足以捕捉作物的快速动态，特别是在多云地区。现有的时空融合技术需要多幅晴空图像，缺乏真正的NRT能力，而地面传感器提供连续监测，但空间覆盖有限。为了解决这些限制，本研究开发了近实时地面卫星融合（NRT-GSF）算法，这是一种基于贝叶斯动态线性模型和卡尔曼滤波的新方法。该算法独特地将Sentinel-2图像与来自农业物联网（IoTA）系统的连续测量相结合，生成每日10米的绿地指数（GAI）产品。它的递归框架既支持NRT模式下的卫星立交桥前向预测，也支持后向更新以优化历史概况。利用34个IoTA系统和2019年的Sentinel-2时间序列在法国麦田上实施，该算法有效提高了时空完整性和准确性（R = 0.75 ~ 0.98, RMSE = 0.1 ~ 0.49）。对Sentinel-2进行了全面的“留一”评价，结果表明其优于现行的CACAO （Consistent Adjustment of Climatology to Actual Observations）算法。手持RGB相机的地面验证进一步证实了新算法的GAI产品的准确性（RMSE = 0.5）。NRT- gsf框架为NRT模式下的日常高分辨率作物GAI制图提供了一个强大的、可操作的解决方案，它可以扩展到近实时环境下的其他性状或应用。
{"title":"NRT-GSF: A novel near-real-time ground-satellite fusion algorithm to retrieve daily green area index at field scale","authors":"Wenjuan Li , Marie Weiss , Samuel Buis , Aleixandre Verger , Sylvain Jay , Zihan Ren , Wenbin Wu , Jingyi Jiang , Alexis Comar , Benoit De Solan","doi":"10.1016/j.rse.2025.115160","DOIUrl":"10.1016/j.rse.2025.115160","url":null,"abstract":"<div><div>Near-real-time (NRT) daily crop monitoring at the field scale is crucial for precision agriculture, yet remains challenging due to limitations in the spatial or temporal resolution of existing remote sensing methods. While Sentinel-2 provides adequate spatial resolution for field-level applications, its temporal resolution is insufficient for capturing rapid crop dynamics, especially in cloudy regions. Existing spatiotemporal fusion techniques require multiple clear-sky images and lack true NRT capability, while ground-based sensors offer continuous monitoring but with limited spatial coverage. To address these limitations, this study develops the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm, a novel approach based on a Bayesian dynamic linear model and Kalman filtering. The algorithm uniquely integrates Sentinel-2 imagery with continuous measurements from Internet of Things for Agriculture (IoTA) systems to generate daily 10-m Green Area Index (GAI) products. Its recursive framework supports both forward prediction in NRT mode following satellite overpasses and backward updating to refine historical profiles. Implemented over French wheat fields using 34 IoTA systems and Sentinel-2 time series from 2019, the algorithm effectively enhanced spatiotemporal completeness and accuracy (<em>R</em> = 0.75–0.98, <em>RMSE</em> = 0.1–0.49). A comprehensive leave-one-out Sentinel-2 evaluation demonstrated its superiority over the current Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm. Ground validation using handheld RGB cameras further confirmed the accuracy of the GAI products from the new algorithm (RMSE = 0.5). The NRT-GSF framework offers a robust and operationally solution for daily, high-resolution crop GAI mapping in NRT mode, and it can be extended to other traits or applications in the near-real-time context.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115160"},"PeriodicalIF":11.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598723","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}
Pub Date : 2025-11-21DOI: 10.1016/j.rse.2025.115150
Cailin Zhou , Tiangang Yin , Shanshan Wei , Bruce D. Cook , Weiwei Tan , Wai Yeung Yan , Qi Chen , Jean-Philippe Gastellu-Etchegorry
The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km2 with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m2/m2) and digital hemispherical photography (DHP) images (RMSE = 0.46 m2/m2) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R2 = 0.70, RMSE = 0.86 m2/m2). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales.
{"title":"FoScenes: A high-fidelity, large-scale 3D forest plant area density product derived from open-access airborne lidar data","authors":"Cailin Zhou , Tiangang Yin , Shanshan Wei , Bruce D. Cook , Weiwei Tan , Wai Yeung Yan , Qi Chen , Jean-Philippe Gastellu-Etchegorry","doi":"10.1016/j.rse.2025.115150","DOIUrl":"10.1016/j.rse.2025.115150","url":null,"abstract":"<div><div>The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km<sup>2</sup> with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m<sup>2</sup>/m<sup>2</sup>) and digital hemispherical photography (DHP) images (RMSE = 0.46 m<sup>2</sup>/m<sup>2</sup>) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R<sup>2</sup> = 0.70, RMSE = 0.86 m<sup>2</sup>/m<sup>2</sup>). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115150"},"PeriodicalIF":11.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567548","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}
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