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Record W4295946215 · doi:10.1080/07038992.2022.2120858

Soil Moisture and Soil Depth Retrieval Using the Coupled Phase-Amplitude Behavior of C-Band Radar Backscatter in the Presence of Sub-Surface Scattering

2022· article· en· W4295946215 on OpenAlexvenueno aff
Keith Morrison, Wolfgang Wagner

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsnot available
Fundersnot available
KeywordsWater contentRadarSoil scienceRemote sensingEnvironmental scienceMoistureDifferential phaseBackscatter (email)Soil waterSoil horizonGeologyPhase (matter)MeteorologyGeographyPhysicsGeotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

In low-moisture regimes, strongly-reflecting bedrock underlying soil could provide a dominant return. This offers a novel opportunity to retrieve both the volumetric moisture fraction (mv) and depth (d) of a soil layer using a differential phase. A radar wave traversing the overlying soil slows in response to moisture state; moisture dynamics are thus recorded as variations in travel time—captured back at a radar platform as changes in phase. The Phase Scaled Dielectric (PSD) model introduced here converts phase changes to those in soil dielectric as an intermediate step to estimating mv. Simulations utilizing a real soil moisture timeseries from a site in Sudan were used to demonstrate the linked behaviors of the soil and radar variables, and detail the PSD principle. A laboratory validation used soil with a wet top layer variable in depth 1–2 cm and drying from mv ∼ 0.2 m3m−3, overlying a gravel layer at a depth of 11 cm. The scheme retrieved d = 1.49 ± 0.33 cm and a change Δmv = 0.191–0.021 ± 0.009 m3m−3. The PSD scheme outlined here promises a new avenue for the diagnostic measurement of soil parameters which is not currently available to radar remote sensing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.245
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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