Improving crop yield forecasts with satellite-based soil moisture estimates: An example for township level canola yield forecasts over the Canadian Prairies
Bibliographic record
Abstract
Satellite-derived vegetation indices are widely utilized in yield forecasting models; however, they can be heavily impacted by atmospheric conditions due to their reliance on visible and near-infrared portions of the electromagnetic spectrum. Given the importance of soil moisture (SM) for crop development, the objective of this study was to investigate the use of passive microwave-derived estimates of surface SM obtained by the SM Ocean Salinity Mission (SMOS) satellite for forecasting canola yields across the Canadian Prairies within Agriculture and Agri-Food Canada’s (AAFC) Canadian Crop Yield Forecaster (CCYF) model. Weekly SMOS SM observations were combined with climate variables and normalized difference vegetation index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR) platform and used as an input for forecasting canola yields at the township-scale across the Canadian Prairies from 2010 to 2016. Top predictors were identified, and regression models were built using a robust least angle regression (RLARS) and leave-one-out cross-validation (LOOCV) scheme. SM was found to provide a better descriptor of canola stress than the more widely utilized NDVI, being selected as a predictor in 74.2 % of developed ecodistrict models over the 7-year period, compared to just 41.2 % for NDVI. The difference between model R2 values (i.e. R2diff) when SMOS SM predictors were included and excluded from the forecast, respectively, revealed varying degrees of model improvements; however, the majority of ecodistricts under study (53.3 %) showed improved model fit (i.e. R2diff > 0) with observed canola yields when SMOS SM indices were included as potential predictors within the CCYF. Overall, greater improvements in the CCYF performance were observed in Manitoba and Saskatchewan where meteorological stations are more sparsely distributed. However, performance for both sets of model inputs was relatively low with R2 values ranging from 0 to 0.74 (mean = 0.13) and from 0 to 0.52 (mean = 0.12) across the study area both when SM was included and excluded from the model, respectively. These findings suggest that while SMOS SM observations may provide a more effective indicator of canola yields, the CCYF’s performance at the township-scale, where interannual yield variability is often quite high, is limited by the short temporal satellite record.
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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 | 0.000 | 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 | 0.001 |
| 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".