Real-time monitoring of maize phenology with the VI-RGS composite index using time-series UAV remote sensing images and meteorological data

文献类型： 外文期刊

第一作者： Feng, Ziheng

作者： Feng, Ziheng;Ma, Xinming;Feng, Ziheng;Cheng, Zhida;Ren, Lipeng;Liu, Bowei;Zhang, Chengjian;Zhao, Dan;Sun, Heguang;Feng, Haikuan;Long, Huiling;Xu, Bo;Yang, Hao;Song, Xiaoyu;Yang, Guijun;Zhao, Chunjiang

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关键词： UAV; Real-time; Composite index; Maize phenology; BBCH

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 224 卷

页码：

收录情况： SCI

摘要： Real-time crop phenological information can provide crucial guidance for field management, agricultural machinery scheduling, and other agricultural activities. However, in previous research, phenological monitoring is often done post-seasonally, which typically entails a certain degree of lag. To overcome this, here we combined time-series UAV data, image-derived structural information, and cumulative temperature required for maize growth to explore the mapping relationships between multiple data and maize phenology (Bundessortenamt and CHemical Industry scale, BBCH). Then we developed a model for phenology's real-time monitoring for applications requiring only single-time-phase UAV imagery and a single environmental factor (cumulative temperature). Specifically, a composite index was built using the one-to-one multiplication of vegetation index (VI), structural features (SF), and relative growing degree-days (RGS). Finally, the real-time monitoring model of maize BBCH was constructed via the ordinary least squares (OLS) fitting method. The results reveal the DATTRGS model performs best, showing significant advantages over other combinations (VI-PH, VI-CC, CC-RGS, and PH-RGS), with R2, RMSE, NRMSE, and RMSEd values of 0.92, 7.66, 13.57, and 38.14 d, respectively. Plant phenology is a combined response outcome to genotype, field management, and regional environment; and while a VI can indicate the maize genotype and mode of field management, cumulus temperature is indicative of the regional environment and hence more mechanistic. Moreover, fluctuations in the sowing date had less of an effect on VI-RGS. However, when meteorological data is unavailable, the VI-PH model is recommended. Further, the VI-RGS model is able to determine the phenological differences and growth rates of maize in various breeding plots. This study presents new insights for the real-time monitoring of phenology from single-time-phase UAV imagery, providing timely crop phenotypic information for enhancing maize field management and smart breeding. The findings also offer technical support for the identification and selection of maize varieties.

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