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Analysis of Radarsat Constellation Mission Compact Polarimetric Data for Crop Monitoring

2024· article· en· W4402263841 on OpenAlexfundaboutno aff
Bhanu Prakash Mookkuthala Erkaramana, Kalifa Goı̈ta, Ramata Magagi, Hongquan Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
FundersCanadian Space Agency
KeywordsConstellationRemote sensingPolarimetrySynthetic aperture radarEarly-warning radarComputer scienceAerospace engineeringGeologyEnvironmental scienceRadarRadar imagingBistatic radarEngineeringPhysicsAstronomyOptics

Abstract

fetched live from OpenAlex

The compact polarimetric (CP) product of RADARSAT Constellation Mission (RCM) launched by the Canadian Space Agency (CSA) has wide swath and high temporal resolution which helps in continuous agriculture monitoring. Here, we evaluated the potential of RCM CP SAR product in ScanSAR mode to monitor different crops during the phenological cycle. For this study, we have selected the agriculture fields belonging to Agriculture and Agri-Food Canada (AAFC), Lennoxville, Quebec, Canada. We derived various CP parameters such as Stokes parameters, m-chi decomposition, CP Radar Vegetation Index (CpRVI), degree of polarization (DoP), conformity etc., from the RCM CP data. We analyzed their capabilities for crop monitoring with the help of in-situ measurements collected during the intensive field campaign conducted in summer 2022 and 2023. The results are analyzed considering strong effects of incidence angle on the CP parameters during the phenological cycle.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.319
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2024
Admission routes2
Has abstractyes

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