Soil salinity mapping using remote sensing and GIS
Bibliographic record
Abstract
The monitoring of soil salinity plays a vital role in agricultural society. Soil salinity causes land degradation processes, especially in arid and semi-arid regions, which influence soil properties, reduce yield production of crops, and affect infrastructure. This research produces soil salinity mapping of the East Delta in Egypt in 1995 using remote sensing technology. A Landsat 5 image taken on 26 September 1995 was used. Radiometric and atmospheric corrections for satellite data were applied. Different salinity indices (SIs) were used, such as the normalized difference salinity index, SI1, SI2, SI3, SI4, SI5, SI6, and SI7, in addition to the normalized difference vegetation index, which was used for data filtration. The field’s electrical conductivity was measured during the period from 22 to 26 September 1995 by the Japanese International Cooperation Agency. These data were used as ground truth for the correlation analysis with different indices image bands values. Simple linear regression and mean relative error were used to find the best index, which was SI5 with a 0.87 correlation with field truth data and mean relative error equal 22.7%. This index was used to produce a salinity map of the Eastern Delta with acceptable accuracy. Finally, it is concluded that using remote sensing in salinity detection and mapping is highly appreciated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".