Name
Abstract | ||
|---|---|---|
More and More efforts have been made concerning the GNSS Reflectometry (GNSS-R) technique since GPS signals being found to be sensitive to geophysical properties, i.e., ocean surface roughness and soil moisture. Compared to airborne observations, ground-based research could focus on the models using the reflected GNSS signal, regardless of the atmospheric attenuation and the reflection zone's movement. Two ground-based GNSS-R experiments were conducted recently in Beijing. This paper proposes a statistical model based on least squares histogram fitting to process the acquired data. Although either the model error or the mismatching of the measuring depth could lead to the discrepancy between the estimated and in situ soil moisture, this approach can isolate the estimated values from different parts of the mixed surface and estimate soil moisture of a homogeneous surface more reasonably than the simply averaging method. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | Global Positioning System, bistatic radar, soil moisture, Gaussian distribution, fitting |
Field | DocType | ISSN |
Least squares,Errors-in-variables models,GNSS reflectometry,Computer science,Remote sensing,Statistical model,GNSS applications,Water content,GPS signals,Surface roughness | Conference | 2153-6996 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuefeng Peng | 1 | 1 | 1.09 |
| Xiuwan Chen | 2 | 33 | 18.04 |
| Han Xiao | 3 | 0 | 0.34 |
| Wei Wan | 4 | 42 | 2.86 |
| Ting Yang | 5 | 0 | 0.34 |
| Zhenyu Yang | 6 | 0 | 0.68 |