Adjusted CBA-Wheat model for predicting aboveground biomass in winter wheat from hyperspectral data

Abstract

Crop aboveground biomass (AGB) is a key indicator of photosynthesis and carbon cycle dynamics in agricultural ecosystems. The availability of accurate, real-time AGB data enables efficient resource management and precision farming. The crop biomass algorithm for wheat (CBA-Wheat) estimates winter wheat AGB using vegetation index (VI) and Zadoks stage (ZS), but acquiring ZS data through field surveys is challenging for large-scale applications. This study aimed to optimize the CBA-Wheat model by incorporating the concept of the relative day of the year (RDOY) as a replacement for ZS and combining it with VI to enhance the performance of the wheat growth model. We proposed the concept of RDOY to replace the traditional ZS, thereby optimizing the CBA-Wheat model. The study used data from Xiaotangshan, Beijing, from 2013 to 2020 for model development. The validation dataset included 2021 Xiaotangshan data, 2010 suburban Beijing data, and 2012 Yucheng, Shandong data for testing the model’s temporal and spatial transferability. Additionally, we compared the performance of the CBA-WheatRDOY model with machine learning models, including Partial Least Squares Regression (PLSR) and Random Forest (RF). We found that the modified CBA-WheatRDOY model, utilizing the modified simple ratio vegetation index (MSR) as an input parameter, achieved the highest AGB estimation accuracy, with a coefficient of determination (R²) of 0.82 and a root mean square error (RMSE) of 1.71 t/ha. This result surpassed the performance of partial least squares regression (R² = 0.78, RMSE = 1.48 t/ha) and random forest (R² = 0.73, RMSE = 2.03 t/ha) models when RDOY was introduced. Our findings highlight the effectiveness of introducing RDOY in improving the accuracy of winter wheat biomass estimation within the CBA-Wheat model. Moreover, RDOY is a superior alternative to traditional phenological observations and can potentially enhance the performance of conventional machine learning models. Compared with existing algorithms, the CBA-WheatRDOY model, grounded in RDOY, not only responds sensitively to various phenological stages but also exhibits improved inversion accuracy. This approach holds promising potential for enhancing the timeliness and spatial extrapolation of winter wheat AGB predictions, advancing precision agriculture and ecosystem management.

Plant phenotyping relevance

冬小麦の地上部バイオマスという植物形質を、ハイパースペクトル由来の植生指数と生育時期情報から推定するモデルを開発・最適化し、時空間移 transferability と他の機械学習モデルとの比較検証を行っているため、表現型取得・推定手法が中心である。

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