Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data
Bibliographic record
Abstract
Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R2 on a 10% independent test subset.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".