Soil Moisture Estimation From Smap Observations Using Long Short- Term Memory (LSTM)
Bibliographic record
Abstract
Soil Moisture (SM) estimation is of growing interest. In the recent years, different Machine Learning (ML) methods were developed in order to better understand the spatio-temporal variability of SM. Among the different ML methods, neural networks were the most used for SM estimation. The purpose of this paper is to propose a Long Short-Term Memory (LSTM) based methodology to estimate the SM. The input data used in the LSTM model include the Soil Moisture Active and Passive mission (SMAP) Brightness Temperature (TB), the Moderate Resolution Imaging Spectroradiometer Vegetation Water Content (MODIS-VWC) and the soil temperature. The target SM data used to train the LSTM model is provided by the Real-time In situ Soil Monitoring for Agriculture (RISMA) network installed by Agriculture and Agri-Food Canada (AAFC). LSTM shows good ability to estimate the SM values with good accuracy.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".