Predictive soil mapping using historic bare soil composite imagery and legacy soil survey data
Why this work is in the frame
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Bibliographic record
Abstract
There is an increasing need for detailed soil property maps to support land use planning, soil carbon accounting, and precision agriculture. While soil maps exist in Saskatchewan, they are at coarse scales (1:100,000), which are not always suitable for detailed soil management. One emerging technique for predictive soil mapping is the use of bare soil composite imagery derived from multi-temporal satellite imagery. This study focused on using bare soil composite imagery along with legacy soil data (1958–1998) with high location uncertainty to predict soil organic carbon, clay, and cation exchange capacity. The bare soil composite images were created from Landsat 5 imagery (1985 to 1995) using Google Earth Engine. Predictive models were built using a Random Forest model for each parameter and evaluated using a 75–25 train-test split. The soil organic carbon model had an R2 value of 0.55 with a root mean square error (RMSE) of 0.67 percent, with the near infrared and visible light bands being the most important features in the model. The clay predictive model has an R2 of 0.44 and a RMSE of 5.0 percent, with the shortwave infrared bands being most important. The cation exchange capacity model had an R2 of 0.50 with a RMSE of 5.7 meq 100 g−1, with the shortwave and near infrared bands as the most important predictors. Based on these results, bare soil composite imagery represents a valuable covariate for predictive soil mapping in the Canadian Prairies. This work also illustrates that for regions with extensive adoption of conservation farming practices, satellite imagery should be obtained for time periods before these practices were adopted from the months of the year where crop residues have decomposed. By combining historical soil survey data with historical imagery, maps of legacy soil properties can be generated to make comparisons against with modern data for applications such as monitoring soil organic carbon change over time.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it