An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery

TY - JOUR

T1 - An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery

AU - Temimi, Marouane

AU - Abdelkader, Mohamed

AU - Tounsi, Achraf

AU - Chaouch, Naira

AU - Carter, Shawn

AU - Sjoberg, Bill

AU - Macneil, Alison

AU - Bingham-Maas, Norman

N1 - Publisher Copyright: © 2023 by the authors.

PY - 2023/10

Y1 - 2023/10

N2 - This study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and employs the U-Net deep learning algorithm for the semantic segmentation of images under varying cloud and land surface conditions. The system autonomously generates detailed maps delineating classes such as water, land, vegetation, snow, river ice, cloud, and cloud shadow. The verification of system outputs was performed quantitatively by comparing with existing ice extent maps in the northeastern US and New Brunswick, Canada, yielding a Probability of Detection of 0.77 and a False Alarm rate of 0.12, suggesting commendable accuracy. Qualitative assessments were also conducted, corroborating the reliability of the system and underscoring its utility in monitoring hydraulic and hydrological processes across northern watersheds. The system’s proficiency in accurately capturing the phenology of river ice, particularly during onset and breakup times, testifies to its potential as a valuable tool in the realm of river ice monitoring.

AB - This study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and employs the U-Net deep learning algorithm for the semantic segmentation of images under varying cloud and land surface conditions. The system autonomously generates detailed maps delineating classes such as water, land, vegetation, snow, river ice, cloud, and cloud shadow. The verification of system outputs was performed quantitatively by comparing with existing ice extent maps in the northeastern US and New Brunswick, Canada, yielding a Probability of Detection of 0.77 and a False Alarm rate of 0.12, suggesting commendable accuracy. Qualitative assessments were also conducted, corroborating the reliability of the system and underscoring its utility in monitoring hydraulic and hydrological processes across northern watersheds. The system’s proficiency in accurately capturing the phenology of river ice, particularly during onset and breakup times, testifies to its potential as a valuable tool in the realm of river ice monitoring.

KW - U-Net

KW - VIIRS

KW - breakup

KW - flood

KW - freeze up

KW - ice jams

KW - natural hazards

KW - river ice

UR - http://www.scopus.com/inward/record.url?scp=85175365948&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85175365948&partnerID=8YFLogxK

U2 - 10.3390/rs15204896

DO - 10.3390/rs15204896

M3 - Article

SN - 2072-4292

VL - 15

JO - Remote Sensing

JF - Remote Sensing

IS - 20

M1 - 4896

ER -