Abstract
To improve crop model performance for regional crop yield estimates, a new four-dimensional variational algorithm (POD4DVar) merging the Monte Carlo and proper orthogonal decomposition techniques was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)-Wheat model. Two winter wheat yield estimation procedures were conducted on a field plot and regional scale to test the feasibility and potential of the POD4DVar-based strategy. Winter wheat yield forecasts for the field plots showed a coefficient of determination (R2) of 0.73, a root mean square error (RMSE) of 319 kg/ha, and a relative error (RE) of 3.49%. An acceptable yield at the regional scale was estimated with an R2 of 0.997, RMSE of 7346 tons, and RE of 3.81%. The POD4DVar-based strategy was more accurate and efficient than the EnKF-based strategy. In addition to crop yield, other critical crop variables such as the biomass, harvest index, evapotranspiration, and soil organic carbon may also be estimated. The present study thus introduces a promising approach for operationally monitoring regional crop growth and predicting yield. Successful application of this assimilation model at regional scales must focus on uncertainties derived from the crop model, model inputs, data assimilation algorithm, and assimilated observations.
Highlights
Regional crop yield information is critical for food security and sustainable agriculture [1,2,3].obtaining such data in a timely and accurate manner is challenging if global climate change is considered
These factors led to a lower accuracy in regional crop yield estimation and limited the potential application of the crop model
Compared with the leaf area index (LAI) simulation process with no data assimilation (Figure 2a), the POD4DVar-based data assimilation performed the incorporation between the observed and modeled LAIs within the dynamic framework of the winter wheat growth process such that the LAI simulation over the entire growing season was optimized and the simulated LAI was more consistent with the actual LAI, with an R2 of 0.92, an root mean square error (RMSE) of 0.31, and an relative error (RE) of 10.25% (Figure 2b)
Summary
Regional crop yield information is critical for food security and sustainable agriculture [1,2,3]. Obtaining such data in a timely and accurate manner is challenging if global climate change is considered. Remote sensing data assimilations based on crop growth simulations [4,5]. By making better use of crop-growth models such as the World Food Studies (WOFOST) [6], Erosion. The use of spatial observations from remotely sensed data is an ideal option for reducing regional simulation uncertainties [10]. Several remote sensing data assimilation strategies based on crop-growth models have been proposed to accurately estimate regional crop yields
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