Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study

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.

Plant phenotyping relevance

植物のLAI・バイオマス等の状態観測をリモートセンシングとデータ同化で作物モデルへ統合し、収量推定性能を評価する計算・計測ワークフローが研究の中心であるため、植物表現型計測手法として収載する。

Code and data availability

The paper's field validation dataset (wheat/soil state observations from the 2018/19 Cora Lynn experiment) is openly available on the authors' PRISM (Monash) site, and the authors' APSIM-EnKF data assimilation source code is explicitly stated to be publicly available on GitHub. Weather data sources (BoM, Weather Underg