International Journal of Applied Earth Observations and Geoinformation (Jul 2020)
Improving crop yield forecasts with satellite-based soil moisture estimates: An example for township level canola yield forecasts over the Canadian Prairies
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.
