Abstract
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Highlights
Understanding spatial and temporal variability in crop yield is essential to site-specific management [1]
This paper presented a data assimilation case study for the Cora Lynn area with the
The ground-measured and remotely sensed wheat and soil states were assimilated, including Leaf Area Index (LAI), biomass, organ weight of leaf and stem, soil moisture in six soil layers from depth 0 to 55 cm, and soil nitrogen in the top two soil layers from depth 0 to 15 cm. These results were compared to scenarios where the state variables were assimilated into the APSIM-Wheat model under different spatial scales, availability of observations, and model parameters
Summary
Understanding spatial and temporal variability in crop yield is essential to site-specific management [1]. Based crop models simulate daily crop growth based on energy, water, and nutrient exchange with the atmosphere, soil, climate, and field management [1], with such simulations providing insights into the reasons for yield variability [2]. A common issue with crop modelling is that they typically require site specific parameters that can only be calibrated from long-term observations over several growing seasons [1,3]. As it is impractical to collect the spatial input and parameter data required due to the high labour and time costs, broad-scale application of these models for yield prediction is difficult [1].
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