Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy-Reference-Cited by-同舟云学术

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Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy

Published:2020-02 Issue:2 Volume:56 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Varade Divyesh¹^ORCID,Singh Gulab²^ORCID,Dikshit Onkar¹^ORCID,Manickam Surendar³^ORCID

Affiliation:

1. Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur India

2. Centre for Studies in Resource Engineering Indian Institute of Technology Bombay Mumbai India

3. Department of Civil and Environmental Engineering Duke University Durham NC USA

Funder

Department of Science and Technology, Ministry of Science and Technology, India

Department of Science and Technology, Philippines

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019WR025449

Reference53 articles.

1. Climate warming enhances snow avalanche risk in the Western Himalayas

2. Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision

3. An entropy based classification scheme for land applications of polarimetric SAR

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1. A Review on PolSAR Decompositions for Feature Extraction;Journal of Imaging;2024-03-24

2. High-resolution mapping of seasonal snow cover extent in the Pamir Hindu Kush using machine learning-based integration of multi-sensor data;Acta Geophysica;2024-01-24

3. Polarimetric Analysis of Biseasonal Monostatic and Bistatic Radar Observations of a Glacier Accumulation Zone at Ku-Band;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

4. The Australian Bureau of Meteorology’s requirements for Synthetic Aperture Radar data;2023 IEEE International Radar Conference (RADAR);2023-11-06

5. Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack;Remote Sensing of Environment;2023-10

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