A Crop Simulation System for Integrating Remote Sensing and Climate Information to Reduce Model Uncertainty in Crop Yield Assessments GC13B-1086 1384612 Uncertainties in crop yield assessments are caused by many factors, including an imperfect model, model parameters and modeling assumptions, as well as errors in data inputs, e.g. climate. Here, we present a crop simulation system that aims to reduce uncertainty in crop yield assessment due to model and data uncertainties. The system uses DSSAT-CSM as the core crop simulation model. The simulation strategy is two-folds: i) crop model parameter estimation and ii) simulation and prediction mode. In i) a noisy Monte Carlo genetic algorithm (NMCGA) is used to estimate crop, soil and management parameters and their uncertainties, where field and remote sensing data can be used in the process. In ii) simulations can be done in an incremental way, where climate data until the current day is used as inputs to the crop model while the climate inputs for rest of the simulation period are generated by a stochastic weather generator based on climatological or climate forecasts information. Also, in the prediction mode, an ensemble Kalman filter (EnKF) can be used to update crop model state variables, e.g., leaf area index (LAI) and soil moisture from remote sensing and field sensors, this can be used in tandem with the climate merging mechanism within the crop simulation system. A case study on wheat modeling in Hokkaido, Japan will be presented. Model uncertainty assessment and implications of the crop simulation system for crop assessment will be discussed.
Amor V M Ines1, Kiyoshi Honda2, Akihiro Yui3 1. IRI-Columbia University, Palisades, NY, USA; 2. IDEAS, Chubu University, Kusagai, Aichi, Japan; 3. IHI Corporation, Tokyo, Japan