Pub Date : 2026-02-21DOI: 10.1016/j.jhydrol.2026.135181
Lei Zou, Jiarui Yu, Gangsheng Wang, Feiyu Wang, Dunxian She, Jun Xia
{"title":"Identifying hotspots and impact factors of multi-type compound events over major global river basins","authors":"Lei Zou, Jiarui Yu, Gangsheng Wang, Feiyu Wang, Dunxian She, Jun Xia","doi":"10.1016/j.jhydrol.2026.135181","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2026.135181","url":null,"abstract":"","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"8 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778199","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}
Accurate and efficient monitoring of seedling emergence is critical for early-stage crop management and yield forecasting in sugarcane production. To meet this practical demand for precise field phenotyping, this study developed a high-throughput phenotyping framework leveraging unmanned aerial vehicle (UAV) remote sensing data and machine learning. This framework addresses the critical agricultural challenges of inefficient manual counting and the need for plot-scale monitoring in sugarcane production by enabling high-throughput sugarcane seedling number prediction through the integration of UAV-acquired RGB and multispectral imagery. Specifically, the sugarcane canopy was accurately segmented from the background using K-means clustering, a step that enabled the extraction of canopy area and the generation of a mask for obtaining canopy-level average features (including vegetation indices and texture features). These features together form a comprehensive feature set. Subsequently, six different feature selection methods were used to optimize the feature set, and eight machine learning models were combined for training and evaluation. The results showed that the combination of Gradient Boosting Regression (GBR) and KBest-F feature selection method yielded the optimal prediction performance, with a coefficient of determination (R2) of 0.7641, a root mean square error (RMSE) of 19.42, and a mean absolute error (MAE) of 15.93. Further analysis identified canopy area, the Normalized Difference Red Edge Index (NDRE), red edge contrast, and green entropy as core predictive features. They collectively contribute over 60% of total feature importance, and their synergistic effects support accurate seedling number estimation. This framework offers an efficient, scalable tool for plot-scale seedling monitoring, with substantial potential for precision field management of high-density crops.
{"title":"From canopy segmentation to accurate prediction: An UAV-based multi-feature fusion framework for plot-scale ratoon sugarcane seedling counting","authors":"Hongyan Zhu, Zhihao Dong, Litao Wei, Shuai Qin, Xiaoyan Qin, Yong He","doi":"10.1016/j.jag.2026.105183","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105183","url":null,"abstract":"Accurate and efficient monitoring of seedling emergence is critical for early-stage crop management and yield forecasting in sugarcane production. To meet this practical demand for precise field phenotyping, this study developed a high-throughput phenotyping framework leveraging unmanned aerial vehicle (UAV) remote sensing data and machine learning. This framework addresses the critical agricultural challenges of inefficient manual counting and the need for plot-scale monitoring in sugarcane production by enabling high-throughput sugarcane seedling number prediction through the integration of UAV-acquired RGB and multispectral imagery. Specifically, the sugarcane canopy was accurately segmented from the background using K-means clustering, a step that enabled the extraction of canopy area and the generation of a mask for obtaining canopy-level average features (including vegetation indices and texture features). These features together form a comprehensive feature set. Subsequently, six different feature selection methods were used to optimize the feature set, and eight machine learning models were combined for training and evaluation. The results showed that the combination of Gradient Boosting Regression (GBR) and KBest-F feature selection method yielded the optimal prediction performance, with a coefficient of determination (R<ce:sup loc=\"post\">2</ce:sup>) of 0.7641, a root mean square error (RMSE) of 19.42, and a mean absolute error (MAE) of 15.93. Further analysis identified canopy area, the Normalized Difference Red Edge Index (NDRE), red edge contrast, and green entropy as core predictive features. They collectively contribute over 60% of total feature importance, and their synergistic effects support accurate seedling number estimation. This framework offers an efficient, scalable tool for plot-scale seedling monitoring, with substantial potential for precision field management of high-density crops.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"24 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278239","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}
Pengfei Zhan, Jida Wang, Tan Chen, Shuangxiao Luo, Kai Liu, Linghong Ke, Chenyu Fan, Yaling Lin, Chunqiao Song
Reservoirs play a crucial role in global water resource management. Monitoring reservoir hydrologic dynamics is critical for assessing climate variability and anthropogenic regulation. However, traditional satellite altimetry faces multiple challenges hindering high frequency and accuracy water level monitoring. This study develops a proof-of-concept framework that integrates multi-source satellite data, with the Surface Water and Ocean Topography (SWOT) mission as the primary data source, to generate high-resolution reservoir water level time series. The SWOT-anchored integration framework establishes a unified two-dimensional height reference by rule-based virtual station selection and monthly water surface elevation difference fields. On this reference frame, heterogeneous nadir/laser altimetry from multiple missions are cross-calibrated, while multi-source area series are converted to dense levels via reservoir-specific hypsometry model and then fused. The framework's robustness and re-applicability were confirmed using eight representative Chinese reservoirs. Results demonstrate that the integrated multi-source water level time series significantly enhanced observation frequency, achieving near-daily temporal resolution and capturing detailed non-linear and short-term water level dynamics. The water level observation frequency for all reservoirs based on SWOT exceeds 20 times per yr, with the highest reaching 38 times. After multi-sensor synthesis, the water level observation frequency increased by 3.2–8.1 times, yielding an average of 121 observations annually. Validation results showed strong correlations (R2 > 0.90) and low errors (0.46 m ≥ MAE ≥ 0.11 m), confirming the method's robustness and effectiveness. Instead of treating SWOT as another input, this framework standardizes water levels across sensors and tracks, enabling a scalable and transferable multi-mission synthesis for global reservoir monitoring under changing climatic and anthropogenic pressures.
{"title":"Integrating SWOT With Multi-Source Satellite Observations for Near-Daily Reservoir Water Level Monitoring","authors":"Pengfei Zhan, Jida Wang, Tan Chen, Shuangxiao Luo, Kai Liu, Linghong Ke, Chenyu Fan, Yaling Lin, Chunqiao Song","doi":"10.1029/2024wr039711","DOIUrl":"https://doi.org/10.1029/2024wr039711","url":null,"abstract":"Reservoirs play a crucial role in global water resource management. Monitoring reservoir hydrologic dynamics is critical for assessing climate variability and anthropogenic regulation. However, traditional satellite altimetry faces multiple challenges hindering high frequency and accuracy water level monitoring. This study develops a proof-of-concept framework that integrates multi-source satellite data, with the Surface Water and Ocean Topography (SWOT) mission as the primary data source, to generate high-resolution reservoir water level time series. The SWOT-anchored integration framework establishes a unified two-dimensional height reference by rule-based virtual station selection and monthly water surface elevation difference fields. On this reference frame, heterogeneous nadir/laser altimetry from multiple missions are cross-calibrated, while multi-source area series are converted to dense levels via reservoir-specific hypsometry model and then fused. The framework's robustness and re-applicability were confirmed using eight representative Chinese reservoirs. Results demonstrate that the integrated multi-source water level time series significantly enhanced observation frequency, achieving near-daily temporal resolution and capturing detailed non-linear and short-term water level dynamics. The water level observation frequency for all reservoirs based on SWOT exceeds 20 times per yr, with the highest reaching 38 times. After multi-sensor synthesis, the water level observation frequency increased by 3.2–8.1 times, yielding an average of 121 observations annually. Validation results showed strong correlations (<i>R</i><sup>2</sup> > 0.90) and low errors (0.46 m ≥ MAE ≥ 0.11 m), confirming the method's robustness and effectiveness. Instead of treating SWOT as another input, this framework standardizes water levels across sensors and tracks, enabling a scalable and transferable multi-mission synthesis for global reservoir monitoring under changing climatic and anthropogenic pressures.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"96 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146231305","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 : 2026-02-21DOI: 10.1016/j.gca.2026.02.020
Mason Neuman, Catherine A. Macris, Astrid Holzheid, Katharina Lodders, Bruce Fegley, Heng Chen, Kun Wang
{"title":"Moderately Volatile Elemental Depletion and Potassium Isotope Fractionation during Evaporation in Laser-Heating Aerodynamic-Levitation Experiments","authors":"Mason Neuman, Catherine A. Macris, Astrid Holzheid, Katharina Lodders, Bruce Fegley, Heng Chen, Kun Wang","doi":"10.1016/j.gca.2026.02.020","DOIUrl":"https://doi.org/10.1016/j.gca.2026.02.020","url":null,"abstract":"","PeriodicalId":327,"journal":{"name":"Geochimica et Cosmochimica Acta","volume":"17 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778162","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 : 2026-02-21DOI: 10.1016/j.jhydrol.2026.135174
Jinyu Hua, Detang Lu
{"title":"Surrogate modeling for three-phase flow in porous media based on a temporal-attention-enhanced multiple-input operator network","authors":"Jinyu Hua, Detang Lu","doi":"10.1016/j.jhydrol.2026.135174","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2026.135174","url":null,"abstract":"","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"10 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778196","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}
Infrasound propagation is inherently asymmetric and time-dependent, making accurate atmospheric specifications essential for predicting when and where signals arrive. The Naval Research Laboratory's Ground-to-Space (G2S) model is widely used for this purpose, yet discrepancies between G2S predictions and observations highlight the need for improvement. Here we develop Radiosonde-to-Space (R2S) specifications by merging direct radiosonde-based observations of the lower atmosphere with G2S profiles aloft, producing continuous profiles from the ground surface elevation to 150 km. We compare more than 6,000 G2S–R2S pairs for Albuquerque, New Mexico, and conduct propagation modeling on representative endmembers spanning the range of differences. While G2S typically reproduces most R2S-predicted arrivals, important exceptions occur that would affect event interpretation in real world scenarios. These results demonstrate that although G2S provides robust global coverage, predictions can change when direct lower-atmosphere observations are incorporated, motivating broader integration of real-time radiosonde data into global atmospheric specifications.
{"title":"Radiosonde-to-Space (R2S) Atmospheric Specifications: Bridging Observations and Models for Infrasound Propagation","authors":"Loring P. Schaible, Elizabeth A. Silber","doi":"10.1029/2025gl120188","DOIUrl":"https://doi.org/10.1029/2025gl120188","url":null,"abstract":"Infrasound propagation is inherently asymmetric and time-dependent, making accurate atmospheric specifications essential for predicting when and where signals arrive. The Naval Research Laboratory's Ground-to-Space (G2S) model is widely used for this purpose, yet discrepancies between G2S predictions and observations highlight the need for improvement. Here we develop Radiosonde-to-Space (R2S) specifications by merging direct radiosonde-based observations of the lower atmosphere with G2S profiles aloft, producing continuous profiles from the ground surface elevation to 150 km. We compare more than 6,000 G2S–R2S pairs for Albuquerque, New Mexico, and conduct propagation modeling on representative endmembers spanning the range of differences. While G2S typically reproduces most R2S-predicted arrivals, important exceptions occur that would affect event interpretation in real world scenarios. These results demonstrate that although G2S provides robust global coverage, predictions can change when direct lower-atmosphere observations are incorporated, motivating broader integration of real-time radiosonde data into global atmospheric specifications.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"83 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146231316","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":"Significant differences in the three-dimensional scale of the Northeast China Cold Vortex between summer and winter and the associated thermodynamic-dynamic mechanisms","authors":"Xiaotao Zhao, Yiyu Qing, Shunwu Zhou, Mingcheng Chen, Xulin Ma, Wei Liu, Lin Jiang, Liang Hao","doi":"10.1016/j.atmosres.2026.108880","DOIUrl":"https://doi.org/10.1016/j.atmosres.2026.108880","url":null,"abstract":"","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"46 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Karst conduits play an important role in the process of groundwater and material transport, in which pollutants can move quickly and pollute groundwater easily. Considering the different shapes of karst conduits, hydrodynamic conditions and other factors, eddy generation is very common. After the solute is trapped in the eddy zone, which usually results in the tailing of solutes, typically referred to as non-Fickian migration or anomalous transport. Many solute transport models have been proposed to explain this phenomenon reasonably and prepare to capture the solute breakthrough curve (BTC), although the model parameters lack a clear physical background. In this study, a series of different types of rough conduits were designed to quantitatively evaluate the influence of eddies on solute transport. A new method for the quantitative identification of the eddy zone is proposed. The quantitative relationship between different flow velocities, roughness shape, relative roughness and the eddy area proportion is summarized. A new model of solute transport under the influence of eddies was proposed based on the traditional mobile-immobile model, and the physical meaning of the model parameters was clarified. The ability of the new model to capture the breakthrough curve is satisfactory, which can effectively serve the engineered settings.
{"title":"Eddy-Controlled Anomalous Transport in Rough Conduits: Physics-Based Parameterization and Distributed Modeling","authors":"Zhongxia Li, Yun Yang, Haibo Feng, Junwei Wan, Jianmei Cheng, Hongbin Zhan, Xixian Kang, Chong Ma, Xianshuo Yang, Kun Huang, Taotao Lu","doi":"10.1029/2025wr041456","DOIUrl":"https://doi.org/10.1029/2025wr041456","url":null,"abstract":"Karst conduits play an important role in the process of groundwater and material transport, in which pollutants can move quickly and pollute groundwater easily. Considering the different shapes of karst conduits, hydrodynamic conditions and other factors, eddy generation is very common. After the solute is trapped in the eddy zone, which usually results in the tailing of solutes, typically referred to as non-Fickian migration or anomalous transport. Many solute transport models have been proposed to explain this phenomenon reasonably and prepare to capture the solute breakthrough curve (BTC), although the model parameters lack a clear physical background. In this study, a series of different types of rough conduits were designed to quantitatively evaluate the influence of eddies on solute transport. A new method for the quantitative identification of the eddy zone is proposed. The quantitative relationship between different flow velocities, roughness shape, relative roughness and the eddy area proportion is summarized. A new model of solute transport under the influence of eddies was proposed based on the traditional mobile-immobile model, and the physical meaning of the model parameters was clarified. The ability of the new model to capture the breakthrough curve is satisfactory, which can effectively serve the engineered settings.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"107 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146231301","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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