A straightforward approach for obtaining quantitative dielectric constant using reflected GNSS signals
Bibliographic record
Abstract
Dielectric constant describes the electrical properties of a material and is related to soil moisture. The latter is known as a critical parameter in hydrological and climate science; however, computing such dielectric constants is a challenging problem as many factors effect the constant values, e.g., soil type, texture and temperature. Global Navigation Satellite System-Reflectometry (GNSS-R) is a relatively new remote sensing technique being used to infer geophysical information by measuring not only the signals coming directly from the GNSS satellites, but also the reflected GNSS signals from the Earth’s surface. This research presents a new, straightforward approach for computing relative dielectric constant by means of reflectivity, which is the ratio between the signal-to-noise ratio (SNR) of direct waves and SNR of reflected waves. With the well-known relationship between the reflectivity, Fresnel coefficient, and surface roughness, the dielectric constant can be expressed as the combination of horizontal and vertical Fresnel coefficients. Dual, circular-polarized antennas in the zenith and nadir directions were used to capture electromagnetic waves emitted from GNSS satellites and transform them into electrical signals. The zenith direction antenna senses the direct signals which have right-hand circular polarization, and the nadir direction antenna senses right and left-hand circular polarization of reflected GNSS signals created by electromagnetic reflections on the surfaces. An in-house Software Defined Radio (SDR) receiver, coupled with a commercial radio frequency frontend were used to collect, store and analyze both direct and reflected signals. Data collection experiments were carried in areas of smooth surface, and the observed SNR values were applied to the method of quantitative dielectric constant. The computation results demonstrate that derived dielectric constants have the values around 10 and are independent on the incident angle of waves coming to the specular point. Applying the additional data processing to the results, it is relevant to the dielectric constant measured by Time Domain Reflectometry techniques used commercial soil moisture probes at the same time. There have been few attempts to establish the dielectric constant model using forward scattered electromagnetic signals, especially GNSS signals. The proposed calculation method is able to solve the difficulties in analyzing with respect to the incident angle, as well as the polarization. Therefore, it is expected that the inversion approach of the retrieval algorithm makes the GNSS-R applicable to not only the scientific but also the industrial applications. In the future, the dielectric constant will be enhanced to include roughness information of the Earth’s surface and to attempt to calibrate surface soil moisture measurements for various soil types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".