Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data

Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data

Current terrestrial snow depth mapping from space faces challenges in spatial coverage, revisit frequency, and cost. Airborne lidar, although precise, incurs high costs and has limited geographical coverage, thereby necessitating the exploration of alternative, cost-effective methodologies for snow...

Full description

Saved in:
Bibliographic Details
Main Authors: Ibrahim Olalekan Alabi, Hans-Peter Marshall, Jodi Mead, Ernesto Trujillo
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Remote Sensing
Subjects:
snow depth
InSAR
machine learning
NISAR
remote sensing
Online Access:https://www.frontiersin.org/articles/10.3389/frsen.2024.1481848/full
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items