Asset Details
Do you wish to reserve the book?
Potential of Sentinel-3 snow cover fraction data for improving hydrological simulations at the regional scale
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Potential of Sentinel-3 snow cover fraction data for improving hydrological simulations at the regional scale
Do you wish to request the book?
Potential of Sentinel-3 snow cover fraction data for improving hydrological simulations at the regional scale
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Potential of Sentinel-3 snow cover fraction data for improving hydrological simulations at the regional scale
2026
Overview
Satellite snow cover observations have been shown to enhance the calibration of conceptual hydrologic models. Recent advance in the mapping of snow cover fraction brings new satellite products and datasets. This study assesses the accuracy and potential of a newly developed snow cover fraction (SCF) product derived from Sentinel-3 observations. The product is developed using a physically based spectral unmixing approach that maps daily snow cover fractions at a 200 m spatial resolution over mountain regions. The main objective of this study is to evaluate the potential of the SCF for improving hydrological simulations at the regional scale. The specific aims are to compare the accuracy of snow cover mapping with daily snow depth observations at 631 climate stations and to assess and compare the runoff and snow model efficiencies obtained from multiple-objective calibration and calibration to runoff only. The analysis is performed using 188 lowland and alpine catchments in Austria. The results show that SCF agrees very well with snow depth observations at climate stations as documented by the median of overall accuracy, which exceeds 95%. The SCF helps to enhance runoff and snow simulations for 39% and 84% of the overall catchments in validation period, respectively. The use of SCF in model calibration improves the efficiency of runoff model, particularly in lowland catchments.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
