Development of a Daily Multilayer Cropland Soil Moisture Dataset for China Using Machine Learning and Application to Cropping Patterns
Bibliographic record
Abstract
Abstract Soil moisture (SM) links the water and energy cycles over the land–atmosphere interface and largely determines ecosystem functionality, positioning it as an essential player in the Earth system. Despite its importance, accurate estimation of large-scale SM remains a challenge. Here we leverage the strength of neural network (NN) and fidelity of long-term measurements to develop a daily multilayer cropland SM dataset for China from 1981 to 2013, implemented for a range of different cropping patterns. The training and testing of the NN for the five soil layers (0–50 cm, 10-cm depth each) yield R 2 values of 0.65–0.70 and 0.64–0.69, respectively. Our analysis reveals that precipitation and soil properties are the two dominant factors determining SM, but cropping pattern is also crucial. In addition, our simulations of alternative cropping patterns indicate that winter wheat followed by fallow will largely alleviate the SM depletion in most parts of China. On the other hand, cropping patterns of fallow in the winter followed by maize/soybean seem to further aggravate SM decline in the Huang-Huai-Hai region and southwestern China, relative to prevalent practices of double cropping. This may be due to their low soil porosity, which results in more soil water drainage, as opposed to the case that winter crop roots help maintain SM. This multilayer cropland SM dataset with granularity of cropping patterns provides an important alternative and is complementary to modeled and satellite-retrieved products.
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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".