Dokoohaki, H.; Rai, T.; Kivi, M.; Lewis, P.; Gómez-Dans, J.L.; Yin, F. Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction. Remote Sens.2022, 14, 5389.
Dokoohaki, H.; Rai, T.; Kivi, M.; Lewis, P.; Gómez-Dans, J.L.; Yin, F. Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction. Remote Sens. 2022, 14, 5389.
Dokoohaki, H.; Rai, T.; Kivi, M.; Lewis, P.; Gómez-Dans, J.L.; Yin, F. Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction. Remote Sens.2022, 14, 5389.
Dokoohaki, H.; Rai, T.; Kivi, M.; Lewis, P.; Gómez-Dans, J.L.; Yin, F. Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction. Remote Sens. 2022, 14, 5389.
Abstract
The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.
Environmental and Earth Sciences, Environmental Science
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