Inferring soil moisture variability in the Mediterrean Sea area using infrared and passive microwave observations
Bibliographic record
Abstract
The objective of this study was to infer soil moisture variability from a combination of passive microwave and infrared satellite observations. The proposed approach is mainly based on the concept of apparent thermal inertia (ATI) and makes use of the daily gradient in brightness temperature from MODIS AQUA to infer soil moisture at moderate spatial resolution. Soil moisture retrievals from optical polar orbiting satellites are affected by discontinuities due to the presence of clouds and spurious fluctuations because of low temporal sampling, which is not sufficient for a reliable daily cycle sampling. To mitigate these limitations, we propose using soil moisture temporal trend derived from passive microwave based product, namely the NASA AMSR-E soil moisture product, to filter estimates from MODIS observations. Passive microwave-based soil moisture products exhibit less fluctuation because of their coarse resolution and lower sensitivity to atmosphere. They can therefore be considered as natural “low pass filters” thus reducing the effect of noise in the infrared based estimates. A sensitivity test was conducted to identify to determine the contribution of various factors to the inferred soil moisture from ATI and the error that they may introduce in the estimates. The ATI-based approach was then applied to qualitatively describe the spatial distribution of soil moisture. The algorithm was validated over two different test areas in Italy and France where reference measurements are available. For the test site in Italy, the obtained ATI values were clustered around four different values corresponding to different levels of wetness. The determined four classes of soil moisture (low, medium, medium-high, and high) were compared to available in situ observations. An agreement with in situ observations of 81% was obtained. In densely vegetated areas, only three classes of soil moisture were instead distinguishable. The obtained agreement between observed and inferred soil moisture values was 88%. Also, in the second study area in France, where vegetation is more dominant, only three classes of soil moisture were determined with a lower agreement of 73%. In addition, the ATI trends are in agreement with thermal inertia values determined from physics-based formulation. This study showed that a combination of infrared and passive microwave observation may lead to a better mapping of soil moisture at the regional scale.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".