Estimation of River Ice Thickness in the Shisifenzi Reach of the Yellow River With Remote Sensing and Air Temperature Data

Liu, Bin; Ji, Honglan; Zhai, Yongmei; Luo, Haitao

doi:10.1109/jstars.2023.3285229

Cited by 6 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such limitations underscore the necessity of robust and reliable alternative methods of monitoring. Remote sensing offers a viable solution to this predicament, harnessing the capability of Earth observation systems to capture the expansive and systematic data pertinent to ice dynamics [39][40][41][42][43][44][45][46].…”

Section: Multisatellite Approach For River Ice Detectionmentioning

confidence: 99%

A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics

Abdelkader,

Bravo Mendez,

Temimi

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system’s capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement.

show abstract

Section: Multisatellite Approach For River Ice Detectionmentioning

confidence: 99%

A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics

Abdelkader,

Bravo Mendez,

Temimi

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Previous studies have provided some filed methods for investigating river ice. Firstly, remote sensing is mostly used to monitor ice phenology, ice thickness, and ice dams on a large scale [28][29][30][31]. In general, common high-resolution images from satellites such as Sentinel-2A/B (10 m) and Landsat (30 m) only show the range of the drift ice belt and cannot distinguish the size and shape of the individual ice block, unless higher resolution (0.5 m) commercial satellite images are used.…”

Section: Introductionmentioning

confidence: 99%

A Survey Method for Drift Ice Characteristics of the Yellow River Based on Shore-Based Oblique Images

Zhang

et al. 2023

Water

View full text Add to dashboard Cite

Acquisition of continuous drift ice characteristic parameters such as ice size, shape, concentration, and drift velocity are of great importance for understanding river freezing and thawing processes. This study acquired hourly oblique images captured by a shore-based camera in the winter of 2021–2022 on the Yellow River, China. The pixel point scale method for correcting oblique images is provided. The 61 lines were measured at the calibration site and the absolute error between the measured value and the calculated value was in the range of 0.009–0.850 m, with a mean error of 0.236 m. After the correction of oblique images, the true equivalent diameter of drift ice during the freezing period ranged from 0.52–13.10 m with a mean size of 3.36 m, which was larger than that of 2.30 m during the thawing period which ranged from 0.20–12.54 m. It was found that the size of drift ice increased with time during the freezing period and decreased with time during the thawing period. The fractal dimension and roundness were used to represent drift ice shape. The fractal dimension ranged from 1.0–1.3 and the roundness ranged from 0.1–1.0. A Gaussian distribution was used to estimate drift ice size and shape distributions. There is a nonlinear relationship between ice concentration and drift velocity, which can be well expressed by the logistic function. In the future, drift ice parameters for more years and hydrometeorological data for the same time need to be accumulated, which helps to analyze the freezing and thawing patterns of river ice.

show abstract

Patterns and Trends in Northern Hemisphere River Ice Phenology from 2000 to 2021

Wang,

Feng

2024

Remote Sensing of Environment

View full text Add to dashboard Cite

Estimation of River Ice Thickness in the Shisifenzi Reach of the Yellow River With Remote Sensing and Air Temperature Data

Cited by 6 publications

References 47 publications

A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics

A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics

A Survey Method for Drift Ice Characteristics of the Yellow River Based on Shore-Based Oblique Images

Patterns and Trends in Northern Hemisphere River Ice Phenology from 2000 to 2021

Contact Info

Product

Resources

About