Satellite Soil Observation (SatSoil): extraction of bare soil reflectance for soil organic carbon mapping on Google Earth Engine
Bibliographic record
Abstract
Accurate mapping of soil organic carbon (SOC) is essential for managing agricultural land and mitigating climate change. However, available bare soil reflectance extraction methods from satellite imagery are limited by vegetation, crop residues, and cloud cover. This study introduces SatSoil, an innovative, multi-temporal approach that uses optical remote sensing to isolate bare soil pixels. This approach combines two novel techniques: Consecutive Differential Series (CDS) and the Crop Residue Mitigation Index (CRMI). Based on the principle that soil reflectance increases with wavelength, CDS effectively isolates bare soil pixels from Landsat-8 imagery (2013–2023) over the study area (i.e., Germany). CRMI reduces interference from crop residues by analyzing differences in NIR and SWIR bands. Validation using Canonical Correlation Analysis revealed stronger correlations in the visible bands between satellite and laboratory measurements. The K-means train-test split was used to address the skewed distribution of SOC for stable predictive accuracy using Random Forest Regression (RFR). RFR models achieved R2 values of 0.90, 0.72, and 0.39 for LUCAS-2015, SatSoil, and GEOS3, respectively, with corresponding RMSE values of 17.84, 16.02, and 6.62 g/kg. SatSoil achieved 19.4% greater bare soil coverage than GEOS3, significantly improving satellite soil reflectance accuracy and enhancing SOC mapping for agricultural management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".