Thresholds and distances to better detect wet snow over mountains with Sentinel-1 image time series - Archive ouverte HAL Access content directly
Book Sections Year : 2021

Thresholds and distances to better detect wet snow over mountains with Sentinel-1 image time series

(1) , (2) , (3) , (4)
1
2
3
4

Abstract

In mountain regions, seasonal snow monitoring is very important for many applications such as hydrology, mountain ecosystems, meteorology and avalanche forecasting. This chapter discusses wet snow extent estimation using T. Nagler's backscatter thresholding method. It examines some other interesting options to improve wet snow detection by exploring the use of similarity metrics. Several distances are calculated and tested on a database of Sentinel-1 Synthetic Aperture Radar (SAR) image time series of different sizes over the French Alps. The resulting metrics are discussed and compared with the reference method, as well as with independent data. Change detection is widely used for wet snow detection from SAR images. The different metrics studied show variations that can be linked to significant changes in the SAR signal which is promising for implementing inversion methods integrating the time series of images as a whole.
Fichier principal
Vignette du fichier
Image_Time_Series_Analysis_V2.pdf (5.23 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03483209 , version 1 (16-12-2021)

Identifiers

Cite

Fatima Karbou, Guillaume James, Philippe Durand, Abdourrahmane Atto. Thresholds and distances to better detect wet snow over mountains with Sentinel-1 image time series. Change Detection and Image Time Series Analysis, 1: Unsupervised Methods, Wiley, pp.1-18, 2021, ⟨10.1002/9781119882268.ch5⟩. ⟨hal-03483209⟩
87 View
40 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More