Enhancing crop growth forecasting by incorporating estimated uncertainties for time-series hyperspectral data and crop model GECROS simulations into Ensemble Kalman Filter

Crop status forecasting by crop model simulations can benefit from assimilating remote sensing observations. When conducting data assimilation (DA) using a common procedure – the Ensemble Kalman Filter (EnKF), arbitrary inflation factors are normally adopted to account for unspecified uncertainties, so as to alleviate filter divergence. Here, we developed a more effective Bayesian methodology, in which the uncertainties were systematically quantified by combining multiple methods in one framework. Its applicability and performance in the EnKF were tested using the crop model GECROS (Genotype-by-Environment interaction on CROp growth Simulator) and the data collected from two years of field experiments for rice. Aboveground biomass (W_above), grain weight (W_grains), aboveground nitrogen (N) content (N_above), grain N content (N_grains) and leaf traits like leaf dry weight, leaf N content and leaf area index were measured in the experiments. Using only the observations from the first year, the uncertain parameters in GECROS were calibrated by a Markov Chain Monte Carlo approach, while the parameters in the uncertainty model that describes the errors of crop model simulations were estimated simultaneously. The calibrated model parameters performed well in the validation year, except for the simulated leaf traits (Normalized Root Mean Squared Error (NRMSE) > 0.38). Remotely sensed leaf traits predicted by a Gaussian Process Regression (GPR) model were more accurate (NRMSE < 0.32), with uncertainties of the remote sensing observations estimated from the GPR model itself. Assimilating simulated and predicted leaf traits with their estimated uncertainties into EnKF prevented filter divergence, and the forecast accuracy of crop model improved in the validation year. Compared with simulation without assimilating in-season remote sensing observations, the assimilation procedure led the NRMSE to decrease from 0.37 to 0.20 for whole-season W_above and N_above and from 0.39 to 0.20 for the end-season W_grains and N_grains. The updated crop traits of our method also agreed better with the measurements than those of common EnKF with arbitrarily assumed uncertainties and with adjusted inflation factors. The developed method contributes to systematic uncertainty analysis in DA and accurate forecasting of crop growth and yield for smart farming.