Soil moisture content estimation using water absorption bands
Bibliographic record
Abstract
Soil moisture content (SMC) is a crucial component in various environmental studies. Although many models have been proposed for SMC estimation, developing new models for accurate estimation of SMC is still an interesting subject. This study aimed to develop new models for SMC estimation using the water absorption bands in the spectral signatures of three different soil types: loam, silty loam, and sandy loam. Based on the three absorption bands (i.e., 1400, 1900, and 2200 nm) and regression analyses, six approaches were considered. These scenarios were generally based on the reflectance value and its logarithm, as well as the difference between the wet and dry reflectance values for the absorption bands. Finally, 24 models were developed for SMC estimation from the three different soil types, as well as the entire soil samples. The most accurate SMC, as indicated by the lowest root mean squared error (RMSE) and the highest correlation coefficient (r), was obtained from the model developed using the logarithm of the average values reflectance in the three water absorption bands for sandy loam (RMSE = 0.31 g/kg, r = 0.99). Overall, using the spectrometry data derived in the lab, the results of the proposed models were promising and demonstrate great potential for SMC estimation using spectral data collected by satellites in the future studies.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".