Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

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 Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed 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. The lowest RMSE 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 for 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

Development of a data-assimilation system to forecast agricultural systems: A case study of constraining soil water and soil nitrogen dynamics in the APSIM model

As we face today\u27s large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through the joint estimation of system error matrices. We tested this system at the Energy Farm, a well-monitored research site in central Illinois, where we assimilated observed in situ soil moisture at daily time steps for two years and evaluated how assimilation impacted model forecasts of soil moisture, yield, leaf area index, tile flow, and nitrate leaching by comparing estimates with in situ observations. The system improved the accuracy and precision of soil moisture estimates for the assimilation layers by an average of 42% and 48%, respectively, when compared to the free model. Such improvements led to changes in the model\u27s soil water and nitrogen processes and, on average, increased accuracy in forecasts of annual tile flow by 43% and annual nitrate loads by 10%. Forecasts of aboveground measures did not dramatically change with assimilation, a fact which highlights the limited potential of soil moisture as a constraint for a site with no water stress. Extending the scope of previous work, our results demonstrate the power of data assimilation to constrain important model estimates beyond the assimilated state variable, such as nitrate leaching. Replication of this study is necessary to further define the limitations and opportunities of the developed system

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

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 Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed 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. The lowest RMSE 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 for 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