Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques
Bibliographic record
Abstract
Predictive modeling with remotely sensed data requires an accurate representation of spatial variability by ground truth data. In this study, we assessed the reliability of the size and location of ground truth data in capturing the landscape spatial variability embedded in the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral image in an agricultural region in Anand, India. We derived simulated spectral vegetation and soil indices using Gaussian simulation from AVIRIS-NG image for two point-location datasets, (1) ground truth points from adaptive sampling and (2) points from conditional Latin Hypercube Sampling (cLHS). We compared values of the simulated image indices against the actual image indices (measured) through the analysis of mean absolute errors. Modeling the variogram of the measured indices with the hyperspectral image in high spatial resolution (4m), is an effective way to characterize the spatial heterogeneity at the landscape level. We used geostatistical techniques to analyze the shapes of experimental variograms in order to assess whether or not the ground truth points, when compared against the cLHS-derived points, captured the spatial structures and variability of the studied agricultural area using measured indices. In addition, we explored the capability of the variogram by running tests in different point sample sizes. The ground truth and cLHS datasets were able to derive equivalent values for field spatial variability from image indices, according to our findings. Furthermore, this research presents a methodology for selecting spectral indices and determining the best sample size for efficiently replicating spatial patterns in hyperspectral images.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".