Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2
Why this work is in the frame
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Bibliographic record
Abstract
Remote Sensing affords the opportunity to monitor and evaluate data scarce regions where field collection efforts are costly. A particular challenge is monitoring and evaluation in regions with smallholder agricultural systems (∼1 ha) that are often subsistence focused, vulnerable to food insecurity and data scarce. Using multi-day moderate resolution Sentinel-2 and Random Forest models, this study shows that crop type and rice yields in Burkina Faso can be predicted with greater than ∼80% accuracy in the rainy season. Model optimization using varying spectral and vegetation index inputs can increase crop type and yield prediction accuracy in the dry season where denser cultivation is a challenge for the 10–20 m resolution of Sentinel-2. However, there is a trade-off between opting for very high-resolution imagery (<2 m) or the number of bands offered by Sentinel-2 as the bands that occupy and vegetation indices that utilize the red through NIR ranges were most important across all models. In addition, model type, linear Regression or nonlinear Random Forest, matters little when estimating yield in these landscapes, unless Harmonic regression is utilized for the linear model. This study also showed that a model trained with high quality 2019 dry season crop cut data can predict the subsequent dry season's interannual crop type with overall accuracy as high as 60%, comparable to crop type models trained with 2020 survey data and used to estimate crop type in the concurrent season, as the survey collection. This indicates some utility in leveraging the calibrated Random Forest models to make skillful predictions of interannual crop type and ultimately food availability of nearby communities for years with no training data. Given increasing global food prices and restricted commodity trade, understanding local agricultural productivity using affordable and timely remote sensing-based methods is essential for ensuring appropriate humanitarian interventions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it