利用无人机多光谱数据识别夏玉米产量预测的最佳物候期

doi:10.1016/j.jia.2025.02.026

摘要/Abstract

摘要：

及时准确地预测作物产量对粮食管理和贸易至关重要。然而，很少有研究探讨不同物候期的作物表型参数（CPPs）与无人机（UAV）数据的结合对玉米产量预测的影响。与单时相数据相比，多时相数据能够在多大程度上提高产量预测的准确性和可靠性，这一点也尚未得到系统的研究。为了实现作物产量估算精度与监测成本之间的平衡，本研究提出了一种结构化框架，旨在识别利用无人机多光谱数据进行夏玉米产量预测的最佳物候期。本研究首先采用了三种经典方法，即自定义平均精度减少法（C-MDA）、基于最优参数的地理探测器（OPGD）和灰色关联分析（GRA），对六个生长阶段中的CPPs和基于无人机信息计算的植被指数（VI）进行排序和筛选。进而分别建立了基于单时相数据和多时相数据组合的岭回归模型，并对其在产量预测中的表现进行比较，以确定最佳物候期和相应的关键因素。研究结果表明，与OPGD和GRA相比，C-MDA在因子筛选排名方面表现出明显的优势。绿色归一化差异植被指数（GNDVI）、归一化差异植被系数（NDVI）和归一化差异红边指数（NDRE）表现最佳，而叶面积指数（LAI）和地上生物量（AGB）被证明是最有效的CPPs。当仅使用单时相数据进行产量预测时，腊熟期的预测精度最高（R2=0.871，RMSE=0.407 t ha^-1），而抽雄期则能够最早实现精度尚可的产量估算（R2=0.810，RMSE=0.493 t ha^-1）。相比之下，整合来自不同作物生长阶段的无人机数据显著提高了产量估算的准确性。研究建议采用抽雄期、吐丝期和腊熟期的数据组合（R2=0.942，RMSE=0.291 t ha-1）。以上发现表明，在小农户田块中进行精确的玉米产量估算是可行的，研究为精准农业的发展提供了重要的理论依据和实践指导。

Abstract:

Timely and accurate forecasting of crop yields is critical for food management and trade. However, only limited research has explored the impact of integrating crop phenotypic parameters (CPPs) with unmanned aerial vehicle (UAV) data across different phenological stages on maize yield prediction. The extent to which multi-temporal data enhances the accuracy and reliability of yield projections compared to mono-temporal data has yet to be systematically investigated. To attain the proper balance between accuracy and cost in crop yield estimation, this study proposed a structured framework for identifying the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data. Three classical methods of custom mean decrease accuracy (C-MDA), optimal parameters-based geographical detector (OPGD), and grey relational analysis (GRA) were first used to sort and screen both the CPPs and vegetation indices (VIs) derived from UAV-based information over six growth stages. Ridge regression models based on multi-temporal data combinations and mono-temporal data were established separately, and their performance in yield prediction were compared to identify the optimal phenological stages and the corresponding key factors. Our results showed that C-MDA was much better at factor screening and ranking compared to OPGD and GRA. The green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), and normalized difference red edge index (NDRE) emerged as the top-performing VIs, while the leaf area index (LAI) and above ground biomass (AGB) proved to be the most effective CPPs. When predicting yield using only mono-temporal data, the dough stage delivered the highest predictive accuracy (R²=0.871, RMSE=0.407 t ha^–1), while the tasseling stage was the earliest that achieved yield estimates with acceptable precision (R²=0.810, RMSE=0.493 t ha^–1). In contrast, the integration of UAV data from different crop growth stages markedly enhanced the accuracy of yield estimation. Combinations of data from the tasseling, silking, and dough stages were recommended as the best option (R²=0.942, RMSE=0.291 t ha^–1). These findings indicate that the precise estimation of maize yields in smallholder fields may be attainable, and present both substantial theoretical insights and practical benefits for the advancement of precision agriculture.

Key words: UAV , yield estimation , multispectral data , geographical detector

Qin Dai, Hong Chen, Ziqiang Chen, Chang Liu, Gaoliang Li, Yakun Wang, Xiaotao Hu. 利用无人机多光谱数据识别夏玉米产量预测的最佳物候期[J]. Journal of Integrative Agriculture, 2026, 25(6): 2396-2413.

Qin Dai, Hong Chen, Ziqiang Chen, Chang Liu, Gaoliang Li, Yakun Wang, Xiaotao Hu. Identification of the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data[J]. Journal of Integrative Agriculture, 2026, 25(6): 2396-2413.

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