Soil Salinity Estimating from Satellite Dataset Using Multiple Regression Analysis Over Semi-Arid Region, UAE
Bibliographic record
Abstract
Saline soil presents a real threat to the infrastructure and surface/subsurface resources of arid and semi-arid regions. Satellite-based datasets provide a promising source for monitoring the Earth’s surface, along with the development of computer programs and algorithms over the past decades. This study attempted to estimate the spatial variation of soil salinity over the semi-arid climate of the United Arab Emirates using the statistical technique of multiple linear regression analysis with satellite images from the Landsat 8 OLI optical sensor and Sentinel-1 SAR radar sensor. To develop a model to estimate soil salinity, 396 soil samples were collected from the study site, and soil salinity was measured. Raw OLI and SAR images were converted to reflectance values and backscatter coefficients, respectively. Multiple regression models were generated between the measured soil salinity and extracted satellite dataset, and the best performance models were chosen for validation. The validation analysis showed that the best performance model utilized reflectance values from the near-infrared, shortwave infrared, and backscatter coefficient of the VV channel. This model showed a coefficient of determination of R 2 = 0.5868, which makes the model unsuitable for future applications. This low model performance was expected because of the complex relationship between soil spectral reflectance and soil physical and chemical properties.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".