Growth curve parameters of AGB estimated from multi-source remote sensing data enhance wheat yield prediction accuracy

doi:10.1016/j.jia.2026.05.005

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Dongwei Han¹, Weijun Zhang¹, Muhammad Zain², Shaolong Zhu³, Zhaosheng Yao³, Jianliang Wang¹, Yuanyuan Zhao¹, Tao Liu¹, Chengming Sun¹^,⁴^#, Wenshan Guo¹

¹Cultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province; Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops; Research Institute of Smart Agriculture, Yangzhou University, Yangzhou 225009, China

²College of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China

³School of Computer and Information, Anqing Normal University, Anqing 246011, China

⁴Biological breeding Zhongshan Laboratory of Jiangsu Province, Yangzhou University, Yangzhou 225009, China

Highlights

Proposed a novel wheat yield prediction method based on logistic growth curve parameters of aboveground biomass (AGB).

Developed multi-source remote sensing models integrating vegetation indices (VIs), texture indices (TIs), canopy structure (CS), and canopy temperature (CT) for AGB estimation.

Demonstrated that growth curve parameters extracted from estimated AGB achieved higher accuracy than traditional single-stage remote sensing features.

Abstract
References

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摘要

小麦作为全球最重要的粮食作物之一，其产量的精准估测对保障粮食安全、实现精准农业管理以及应对气候变化具有重要意义。以往研究多聚焦于单时期特征提取或时序遥感特征进行产量估测，但对产量形成机制的深入解析不足。因此，本研究旨在构建基于地上生物量（AGB）生长曲线参数的小麦产量估测模型。通过实测地上生物量拟合Logistic S型生长曲线，提取关键生长参数（如K，V_max, S_GIP，S_RIP，S_SIP，V_GIP，V_RIP，V_SIP，等），并将其整合到机器学习模型中进行产量估测。结果表明，该方法具有较高的估测精度（R²=0.97，RMSE=355.38 kg ha^-¹，MAE=255.74 kg ha^-¹），且提取的生长参数具有明确的生理意义。为实现地上生物量的快速获取，进一步利用多源遥感特征（包括植被指数、纹理指数、冠层结构参数及冠层温度）构建地上生物量估算模型，随着生育期推进，多源遥感特征互补性增强，在开花后30天达到最高估测精度（R²=0.83），有效缓解了植被指数饱和问题。此外，基于估算地上生物量拟合的生长曲线参数，同样实现了精准的产量估测（R²=0.87，RMSE=746.07 kg ha^-¹，MAE=570.16 kg ha^-¹），且该模型在不同区域和年份均表现出稳定的估测性能（R²=0.85，RMSE=784.52 kg ha^-¹，MAE=569.56 kg ha^-¹）。综上，本研究提出的地上生物量生长曲线参数为小麦产量估测提供了新的思路，既提高了估测精度，又增强了模型的生理可解释性，为田间尺度的高效管理和产量估测提供了理论支撑与技术参考。

Abstract

Being one of the most crucial food crops globally, accurate yield prediction of wheat is essential for ensuring food security, enabling precision agricultural management, and addressing climate change challenges. Previous studies mainly focused on single-period feature extraction or time-series remote sensing features for yield prediction, but lacked in-depth explanation of the yield formation mechanism. Therefore, this study aimed to develop a yield prediction model based on growth curve parameters of aboveground biomass (AGB). A logistic S-shaped growth curve was fitted using measured AGB, and key growth parameters (K, V_max, S_GIP, S_RIP, S_SIP, V_GIP, V_RIP, V_SIP, etc.) were extracted and integrated into machine learning models for yield prediction. Results showed that this approach achieved high accuracy (R²=0.97, RMSE=355.38 kg ha^-¹, MAE=255.74 kg ha^-¹), and the extracted parameters had clear physiological significance. To enable rapid AGB acquisition, an AGB estimation model was further developed using multi-source remote sensing features, including vegetation indices (VIs), texture indices (TIs), canopy structure (CS), and canopy temperature (CT). As the growing season progressed, these multi-source features exhibited strong complementarity, reaching the highest accuracy at 30 days after anthesis (R²=0.83) and effectively alleviating the saturation problem of VIs. Moreover, growth parameters derived from the fitted curves of the estimated AGB also achieved accurate yield prediction (R²=0.87, RMSE=746.07 kg ha^-¹, MAE=570.16 kg ha^-¹). The model further demonstrated stable performance across different regions and years (R²=0.85, RMSE=784.52 kg ha^-¹, MAE=569.56 kg ha^-¹). In conclusion, this study introduced novel AGB growth curve parameters for wheat yield estimation, which improved prediction accuracy and enhanced physiological interpretability, providing insights for efficient field-scale management and yield prediction across regions.

Keywords: wheat yield prediction aboveground biomass (AGB) growth curve multi-source remote sensing

Online: 01 May 2026

Fund:

This research was funded by the Biological breeding Zhongshan Laboratory Program of Jiangsu Province, China (ZSBBL-KY2023-05) and the Key Research and Development Program (Modern Agriculture) of Jiangsu Province, China (BE2022338 and BE2022335).

About author: #Correspondence Chengming Sun, E-mail: cmsun@yzu.edu.cn

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Dongwei Han, Weijun Zhang, Muhammad Zain, Shaolong Zhu, Zhaosheng Yao, Jianliang Wang, Yuanyuan Zhao, Tao Liu, Chengming Sun, Wenshan Guo. 2026. Growth curve parameters of AGB estimated from multi-source remote sensing data enhance wheat yield prediction accuracy. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2026.05.005

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