Development and testing of a remote sensing-based model for estimating groundwater levels in aeolian desert areas of China
Bibliographic record
Abstract
Huo, A., Chen, X., Li, H., Hou, M. and Hou, X. 2011. Development and testing of a remote sensing-based model for estimating groundwater levels in aeolian desert areas of China. Can. J. Soil Sci. 91: 29–37. Regional groundwater level is an important data set for understanding the relationships between groundwater resources and regional ecological environments. The decline in water table levels leads to vegetation degradation and thus affects the ecological environment. Such a negative effect is especially apparent in the desertification areas. In this study, a remote-sensing based method was proposed to predict the distribution of the regional groundwater level in an aeolian desert area in northern China. The study used the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and field investigations. Based on field investigation of groundwater level, soil moisture, and other supporting information in the aeolian desert area, as well as the soil moisture distribution derived from the MODIS images, empirical equations describing the relationship between the soil moisture and groundwater level were obtained. The groundwater levels derived using the MODIS image data were verified by groundwater levels measured from 58 wells. The results show that the correlation coefficient between the measured groundwater levels and the remote sensing-based estimated water levels was 0.868, indicating that the error is small and the predictions closely reflect the real water levels. This model can be used to predict groundwater levels in aeolian desert areas based on remote sensing data sets.
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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 | 0.001 | 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 | 0.001 |
| 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".