Retrieval of soil moisture using a decomposition‐based model and optical‐thermal model with Sentinel‐1 and Landsat‐8 images
Bibliographic record
Abstract
Abstract Water content of the soil has a significant role to play in the hydrological cycle and environmental processes. This study includes two phases: (1) approximation of soil moisture at the farm level based on optical‐thermal images of Landsat‐8 satellite and (2) retrieval of soil moisture by the dual‐polarized basis decomposition model with the help of approximate soil moisture that is obtained by the optical‐thermal model. In this research, two mechanisms, volume scattering and surface scattering, are considered. Furthermore, in order to model surface scattering, Bragg matrix has been used. The proposed radar model estimates soil moisture without using ground data, although few ground measurements have been used in the optical‐thermal model. The Carlson triangular model has been used to approximate soil moisture using optical‐thermal images. Three indices, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), and moisture stress index (MSI), have been used in the optical‐thermal model. Two ground soil moisture datasets are used in this study: (1) Cook Agronomy Farm (CAF) soil moisture data located in the United States and (2) real‐time in situ soil monitoring for agriculture (RISMA) soil moisture data located in Canada. The radar model (base decomposition model) achieved a lowest root mean square error (RMSE) of 3.33% and a highest of 11.21%, showing strong accuracy in soil moisture retrieval. The optical‐thermal model had a slightly higher minimum RMSE of 4.04% and a maximum of 9.68%. These results suggest that the radar model generally outperforms the optical‐thermal model, making it more reliable for accurate soil moisture estimation in agricultural applications, which is crucial for optimizing irrigation and managing resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".