Application of remotely sensed and modeled soil moisture for anticipating crop production shocks in food-insecure countries 
Bibliographic record
Abstract
Soil moisture estimates are widely used as indicators of agricultural drought. Despite their ability to signal trends in vegetative water content months before vegetation greenness responses, their direct application in operational crop yield forecasting and the early anticipation of production shocks remains limited. Early warning of crop production shocks is a critical component of food insecurity scenario generation process. Previous research in southern Africa demonstrated promising skill in crop yield forecasting when using modeled soil moisture products as predictors, outperforming traditional indicators such as December-to-February ENSO. Similarly, a study in East Africa identified when and where soil moisture outperforms other Earth observations as a predictor of crop yield. Building on this foundation, we present a comprehensive investigation into the applicability of soil moisture products for sub-national crop yield forecasting across several countries in Sub-Saharan Africa. Our analysis evaluates the performance of various soil moisture datasets, including remotely sensed (e.g., ESA-CCI), modeled (e.g., FEWS NET Land Data Assimilation System), and data-assimilated (e.g., Global Land Evaporation Amsterdam Model) products, in within-season crop yield forecasts. We focus on three key areas: 1. The comparative value of remotely sensed surface soil moisture relative to root zone soil moisture from modeled and data-assimilated products. 2. The effectiveness of remotely sensed soil moisture in irrigated regions, where it may better capture agricultural drought than rainfall or modeled products. 3. The influence of anomalous soil moisture conditions at the onset of growing seasons, such as delayed rains or sequential droughts. Finally, we diagnose the sources of performance differences between remotely sensed and modeled soil moisture as predictors of crop yields. Our findings highlight the potential of remotely sensed soil moisture products as effective predictors for operational crop yield forecasting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".