@article{
 title = {Sensing snow height and surface temperature variations in Greenland from GPS reflected signals},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {GPS-L4,Nonparametric bootstrapping model,Snow height,Snow surface temperature},
 pages = {1623-1633},
 volume = {53},
 websites = {http://www.sciencedirect.com/science/article/pii/S0273117714001525},
 publisher = {COSPAR},
 id = {5e68d088-9d92-32da-8f2e-506e0a7c04f1},
 created = {2016-01-18T03:43:38.000Z},
 file_attached = {false},
 profile_id = {e57eda5c-b101-32f0-9247-2d465d243832},
 last_modified = {2017-09-25T19:44:20.890Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Jin2014a},
 private_publication = {false},
 abstract = {The in situ measurements of snow surface temperature (SST) and snow height (SH) are very difficult with high costs, particularly in Greenland Ice Sheet (GrIS). Since the snow depth variations coupling with surface temperature are related to GPS multipath, it is possible to estimate the snow depth and surface air temperature variations by incorporating GPS-Reflectometry (GPS-R). In this paper, the reflected signals from ground GPS receivers are used to sense the SST and SH variations based on the thermophysical behavior and variations of snow layer from April to June 2010 at SMM1 site and from March to December 2010 at MARG site in Greenland. The results show that the mean daily changes in the ionospheric geometrical-free linear combination (GPS-L4) of dual-frequency GPS signals are related to daily SST and SH variations. The nonparametric bootstrapping model in direct (forward) and inverse models are developed and applied to estimate the SST and SH variations. The mean biases of SST and SH estimates are 0.18°C and 0.23m at SMM1 site, respectively, and 3.8°C and 0.13m at MARG site, respectively.},
 bibtype = {article},
 author = {Jin, Shuanggen and Najibi, Nasser},
 journal = {Advances in Space Research},
 number = {11}
}

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Since the snow depth variations coupling with surface temperature are related to GPS multipath, it is possible to estimate the snow depth and surface air temperature variations by incorporating GPS-Reflectometry (GPS-R). In this paper, the reflected signals from ground GPS receivers are used to sense the SST and SH variations based on the thermophysical behavior and variations of snow layer from April to June 2010 at SMM1 site and from March to December 2010 at MARG site in Greenland. The results show that the mean daily changes in the ionospheric geometrical-free linear combination (GPS-L4) of dual-frequency GPS signals are related to daily SST and SH variations. The nonparametric bootstrapping model in direct (forward) and inverse models are developed and applied to estimate the SST and SH variations. The mean biases of SST and SH estimates are 0.18°C and 0.23m at SMM1 site, respectively, and 3.8°C and 0.13m at MARG site, respectively.","bibtype":"article","author":"Jin, Shuanggen and Najibi, Nasser","journal":"Advances in Space Research","number":"11","bibtex":"@article{\n title = {Sensing snow height and surface temperature variations in Greenland from GPS reflected signals},\n type = {article},\n year = {2014},\n identifiers = {[object Object]},\n keywords = {GPS-L4,Nonparametric bootstrapping model,Snow height,Snow surface temperature},\n pages = {1623-1633},\n volume = {53},\n websites = {http://www.sciencedirect.com/science/article/pii/S0273117714001525},\n publisher = {COSPAR},\n id = {5e68d088-9d92-32da-8f2e-506e0a7c04f1},\n created = {2016-01-18T03:43:38.000Z},\n file_attached = {false},\n profile_id = {e57eda5c-b101-32f0-9247-2d465d243832},\n last_modified = {2017-09-25T19:44:20.890Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jin2014a},\n private_publication = {false},\n abstract = {The in situ measurements of snow surface temperature (SST) and snow height (SH) are very difficult with high costs, particularly in Greenland Ice Sheet (GrIS). Since the snow depth variations coupling with surface temperature are related to GPS multipath, it is possible to estimate the snow depth and surface air temperature variations by incorporating GPS-Reflectometry (GPS-R). In this paper, the reflected signals from ground GPS receivers are used to sense the SST and SH variations based on the thermophysical behavior and variations of snow layer from April to June 2010 at SMM1 site and from March to December 2010 at MARG site in Greenland. The results show that the mean daily changes in the ionospheric geometrical-free linear combination (GPS-L4) of dual-frequency GPS signals are related to daily SST and SH variations. The nonparametric bootstrapping model in direct (forward) and inverse models are developed and applied to estimate the SST and SH variations. The mean biases of SST and SH estimates are 0.18°C and 0.23m at SMM1 site, respectively, and 3.8°C and 0.13m at MARG site, respectively.},\n bibtype = {article},\n author = {Jin, Shuanggen and Najibi, Nasser},\n journal = {Advances in Space Research},\n number = {11}\n}","author_short":["Jin, S.","Najibi, N."],"urls":{"Website":"http://www.sciencedirect.com/science/article/pii/S0273117714001525"},"bibbaseid":"jin-najibi-sensingsnowheightandsurfacetemperaturevariationsingreenlandfromgpsreflectedsignals-2014","role":"author","keyword":["GPS-L4","Nonparametric bootstrapping model","Snow height","Snow surface temperature"],"downloads":0},"bibtype":"article","creationDate":"2019-11-28T05:00:34.167Z","downloads":0,"keywords":["gps-l4","nonparametric bootstrapping model","snow height","snow surface temperature"],"search_terms":["sensing","snow","height","surface","temperature","variations","greenland","gps","reflected","signals","jin","najibi"],"title":"Sensing snow height and surface temperature variations in Greenland from GPS reflected signals","year":2014}