Integrating Proximal Sensing, high-resolution Imagery, and Machine Learning for Field-Scale Soil Salinity Mapping in Semi-Arid Region
Bibliographic record
Abstract
Soil salinity is a major environmental challenge that reduces agricultural productivity and degrades soil health, especially in arid and semi-arid regions. Conventional soil salinity assessment methods involve extensive manual labor and are time-consuming In this study, we explored alternative approaches by using a combination of proximal sensing data (i.e., electromagnetic (EM) induction instruments, EM 38-MK2) with two very high-resolution multi-spectral and -sources imagery (i.e., a UAV (Unnamed Aerial Vehicle) and PlanetScope (PS)), topographic attributes, and machine learning methods to achieve field-scale soil salinity mapping under data-scarce conditions. To do so, an initial set of 26 topsoil samples (0–5 cm) were collected from a saline field in the semi-arid area of Sehb El Masjoune in Southern Morocco. and their Electrical conductivity (EC, a proxy of salinity) was determined at the lab. Then, proximal sensed data from EM38 were collected along the same field and measured apparent soil electrical conductivity (ECa – dS/m) was correlated with measured topsoil EC. We used proximal sensing technology to generate 500 EC (electrical conductivity) observations for spatialization, thereby creating a robust dataset for training four machine learning models: partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and an ensemble (stacked) model. Among these models, the RF and ensemble approaches delivered the highest accuracy, with RF outperforming all others. Performance assessments indicated that PlanetScope data achieved R² = 0.91 and RMSE = 3.47, while UAV data showed R² = 0.89 and RMSE = 3.83. These findings underscore that integrating multisource data, even in data-scarce environments, enhances reliability and robustness in soil salinity mapping at the field scale. Our results highlight a cost-effective, high-precision strategy for characterizing saline and sodic soils, offering valuable insights for targeted reclamation and management interventions in arid and semi-arid regions. We conclude that the used approach not only contributes to the scientific understanding of soil salinity dynamics but also provides practical implications for sustainable land management and agricultural planning. The research highlights the potential of combining cutting-edge technology with environmental predictors to address critical global issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".