Next Article in Journal
Quantitative Estimation of Carbonate Rock Fraction in Karst Regions Using Field Spectra in 2.0–2.5 μm
Previous Article in Journal
Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(1), 63; doi:10.3390/rs8010063

Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data

1
Shanghai Key Laboratory of Space Navigation and Positioning Techniques, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
2
Department of Geomatics Engineering, Bulent Ecevit University, Zonguldak 67100, Turkey
3
University of Chinese Academy of Sciences, Beijing 100047, China
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Gloaguen and Prasad S. Thenkabail
Received: 8 October 2015 / Revised: 4 January 2016 / Accepted: 12 January 2016 / Published: 15 January 2016
View Full-Text   |   Download PDF [4115 KB, uploaded 15 January 2016]   |  

Abstract

Snow is a water resource and plays a significant role in the water cycle. However, traditional ground techniques for snow monitoring have many limitations, e.g., high-cost and low resolution. Recently, the new Global Positioning System-Reflectometry (GPS-R) technique has been developed and applied for snow sensing. However, most previous studies mainly used GPS L1C/A and L2C Signal-to-Noise Ratio (SNR) data to retrieve snow depth. In this paper, snow depth variations are retrieved from new weak GPS L2P SNR data at three stations in Alaska and evaluated by comparing with in situ measurements. The correlation coefficients for the three stations are 0.79, 0.88 and 0.98, respectively. The GPS-estimated snow depths from the L2P SNR data are further compared with L1C/A results at three stations, showing a high correlation of 0.94, 0.93 and 0.95, respectively. These results indicate that geodetic GPS observations with SNR L2P data can well estimate snow depths. The samplings of 15 s or 30 s have no obvious effect on snow depth estimation using GPS SNR L2P measurements, while the range of 5°–35°elevation angles has effects on results with a decreasing correlation of 0.96 and RMSE of 0.04 m when compared to the range of 5°–30° with correlation of 0.98 and RMSE of 0.03 m. GPS SNR data below 30° elevation angle are better to estimate snow depth. View Full-Text
Keywords: snow depth; multipath; GPS-R; SNR; Alaska snow depth; multipath; GPS-R; SNR; Alaska
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jin, S.; Qian, X.; Kutoglu, H. Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data. Remote Sens. 2016, 8, 63.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top