@article{Ghiasi-2020-Application,
title = "Application of GNSS Interferometric Reflectometry for the Estimation of Lake Ice Thickness",
author = "Ghiasi, Yusof and
Duguay, Claude R. and
Murfitt, Justin and
Sanden, J.J. van der and
Thompson, Aaron and
Drouin, H. and
Pr{\'e}vost, C",
journal = "Remote Sensing, Volume 12, Issue 17",
volume = "12",
number = "17",
year = "2020",
publisher = "MDPI AG",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G20-78001",
doi = "10.3390/rs12172721",
pages = "2721",
abstract = "Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.",
}
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<abstract>Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.</abstract>
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%0 Journal Article
%T Application of GNSS Interferometric Reflectometry for the Estimation of Lake Ice Thickness
%A Ghiasi, Yusof
%A Duguay, Claude R.
%A Murfitt, Justin
%A Sanden, J.J. van der
%A Thompson, Aaron
%A Drouin, H.
%A Prévost, C.
%J Remote Sensing, Volume 12, Issue 17
%D 2020
%V 12
%N 17
%I MDPI AG
%F Ghiasi-2020-Application
%X Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.
%R 10.3390/rs12172721
%U https://gwf-uwaterloo.github.io/gwf-publications/G20-78001
%U https://doi.org/10.3390/rs12172721
%P 2721
Markdown (Informal)
[Application of GNSS Interferometric Reflectometry for the Estimation of Lake Ice Thickness](https://gwf-uwaterloo.github.io/gwf-publications/G20-78001) (Ghiasi et al., GWF 2020)
ACL
- Yusof Ghiasi, Claude R. Duguay, Justin Murfitt, J.J. van der Sanden, Aaron Thompson, H. Drouin, and C Prévost. 2020. Application of GNSS Interferometric Reflectometry for the Estimation of Lake Ice Thickness. Remote Sensing, Volume 12, Issue 17, 12(17):2721.
