Comparative Assessment of Remote Sensing and Holos™ Based Soil Organic Carbon Stock Estimation
Bibliographic record
Abstract
Soil organic carbon (SOC) is a critical component of soil health, influencing climate regulation and sustainable land management. Accurate estimation of SOC stocks using remote sensing (RS), especially across different soil depths, remains challenging due to spatial variability and methodological uncertainties. This study assesses SOC stocks at a 30 cm depth using three remote sensing-based approaches (RS1, RS2, RS3) along with Holos<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> model and AgExpert model across fields in Saskatchewan, Canada. Results indicate that RS3 showed the closest alignment with (Holos<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup>) and AgExpert, achieving RMSE values of 2.54 t/ac and 2.84 t/ac, and mean differences of 1.23 t/ac and 1.66 t/ac, respectively. RS1 showed moderate agreement, while RS2 exhibited the highest variability due to generalized conversion factors from other geographies. Annual carbon stock change trends reveal stability from 2020–2021, followed by significant variability in 2022, particularly for fields F4, F5, and F6. This work emphasizes the need to address surface SOC uncertainties and integrate complementary approaches to improve SOC stock estimation for carbon management and climate action.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".