Frontiers in earth observation for global soil properties assessment linked to environmental and socio-economic factors
Bibliographic record
Abstract
Soil has garnered global attention for its role in food security and climate change. Fine-scale soil-mapping techniques are urgently needed to support food, water, and biodiversity services. A global soil dataset integrated into an Earth observation system and supported by cloud computing enabled the development of the first global soil grid of six key properties at a 90-m spatial resolution. Assessing them from environmental and socio-economic perspectives, we demonstrated that 64% of the world's topsoils are primarily sandy, with low fertility and high susceptibility to degradation. These conditions limit crop productivity and highlight potential risks to food security. Results reveal that approximately 900 Gt of soil organic carbon (SOC) is stored up to 20 cm deep. Arid biomes store three times more SOC than mangroves based on total areas. SOC content in agricultural soils is reduced by at least 60% compared to soils under natural vegetation. Most agricultural areas are being fertilized while simultaneously experiencing a depletion of the carbon pool. By integrating soil capacity with economic and social factors, we highlight the critical role of soil in supporting societal prosperity. The top 10 largest countries in area per continent store 75% of the global SOC stock. However, the poorest countries face rapid organic matter degradation. We indicate an interconnection between societal growth and spatially explicit mapping of soil properties. This soil-human nexus establishes a geographically based link between soil health and human development. It underscores the importance of soil management in enhancing agricultural productivity and promotes sustainable-land-use planning.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".