Combining Data Assimilation with Machine Learning to Predict the Regional Daily Leaf Area Index of Summer Maize (<i>Zea mays</i> L.)

Abstract

The prediction of the daily crop leaf area index (LAI) plays a crucial role in forecasting crop growth trends and guiding field management decisions in the realm of scientific research. However, research on the daily prediction of LAI is scarce, and the challenges associated with acquiring sufficient training data pose limitations to the application of machine learning in this context. This study aimed to synergize the strengths of data assimilation and machine learning algorithms to forecast the daily LAI of maize. Initially, a data assimilation algorithm was employed to minimize the disparity between moderate-resolution imaging spectroradiometer-derived LAI and LAI generated through the CERES-Maize model. This effort resulted in a dataset comprising 289 LAI curves. Building upon this dataset, long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF) algorithms were formulated, incorporating N-day LAI input history (N = 5, 10, 15, 20, and 25) to predict LAI for days N + 1 to N + 15. The outcomes revealed that, in contrast to the LAI simulated by the crop model before assimilation, the assimilated LAI closely approximated the observed LAI, with an R2 value of 0.90 and an RMSE of 0.44 m2/m2. Furthermore, when compared to SVR and RF, the LSTM-based LAI prediction model exhibited superior accuracy at N = 15, achieving R2 values of 0.99 and 0.99 for the training and testing datasets, respectively, along with RMSE values of 0.12 and 0.14 m2/m2. It was evident that data assimilation supplied an ample number of samples for the training of machine learning algorithms. The integration of data assimilation technology with machine learning algorithms proved to be an effective methodology for forecasting daily crop LAI.

Keywords

• leaf area index
• CERES-Maize model
• data assimilation
• machine learning