Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning
Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have larg...
Main Authors: | Linglong Zhu, Yonghong Zhang, Jiangeng Wang, Wei Tian, Qi Liu, Guangyi Ma, Xi Kan, Ya Chu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-02-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/4/584 |
Similar Items
-
Retrieval of Snow Depths on Arctic Sea Ice in the Cold Season from FY-3D/MWRI Data
by: Qianhui Yin, et al.
Published: (2024-02-01) -
Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI
by: Lele Li, et al.
Published: (2021-04-01) -
Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning
by: Linglong Zhu, et al.
Published: (2023-10-01) -
An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China
by: Yanlin Wei, et al.
Published: (2022-03-01) -
Assessment of Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in China
by: Xiaojing Liu, et al.
Published: (2018-03-01)