Estimation of Soil Moisture Content Using Deep Learning and High-Resolution Satellite Imagery (Sentinel-1 and 2)
Bibliographic record
Abstract
Soil moisture is an important factor affecting global circulation (climate, carbon, and water), disasters (drought, floods, and forest fires), and crop growth, so the production of soil moisture data is important. Currently, satellite-based soil moisture data is available from NASA’ SMAP (Soil Moisture Active Passive) and ESA’ SMOS (Soil Moisture and Ocean Salinity) data. Since these data are based on passive microwave sensor, they have low spatial resolution. Therefore, it is difficult to observe the distribution of soil moisture on a local scale. The purpose of this study is to produce high resolution soil moisture for monitoring on a local scale. For this purpose, we performed soil moisture modeling using high resolution satellite data (Sentinel-1 SAR (synthetic-aperture radar), Sentinel-2 MSI (multispectral instrument)) and deep learning. Deep learning is a method improving the problems of traditional neural networks such as overfitting, gradient vanishing, and local optimal solution through development of learning methods such as dropout, ReLU (Rectified Linear Unit), and so on. Recently, it has been used for estimation of surface hydrologic factors (soil moisture, evapotranspiration, etc.). The study area is an agricultural area located in Manitoba and Saskatoon, Canada. In-situ soil moisture data was constructed from RISMA (Real-Time In-Situ Soil Monitoring for Agriculture). In order to develop an optimal soil moisture model, various condition experiments on hyper-parameters affecting the performance of model were carried out and their performance was evaluated.
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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) | 0.001 | 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".