Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams
Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams
复制标题
使用 PhenoCams 识别玉米物候的多种方法比较
DOI：
10.3390/rs14020244
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发表时间：
2022-01
期刊：
影响因子：
5
通讯作者：
Kirsten de Beurs
中科院分区：
文献类型：
--
作者：
Yahui Guo;Shouzhi Chen;Yongshuo Fu;Yi Xiao;Wenxiang Wu;Hanxi Wang;Kirsten de Beurs
准确识别夏玉米的物候期是精准农业中品种选育和施肥控制的关键。在这项研究中，使用phenocams在中国商丘（2018年，2019年和2020年）和南皮（2020年）的站点收集了覆盖夏玉米整个生长的每日RGB图像。从基于phenocam的图像中预先定义并提取了夏玉米的6片叶、孕穗、抽穗和成熟4个物候期。采用改进的自适应特征加权法计算了光谱指数、纹理指数和光谱纹理综合指数。应用双Logistic函数、时间序列调和分析、Savitzky-Golay和样条插值等方法对这些指标进行滤波，识别出预定义的物候期，并与地面观测值进行比较。结果表明，DLF的准确度最高，决定系数（R2）和均方根误差（RMSE）分别为0.86和9.32天。新的指数表现优于单一使用光谱和纹理指数，其中R2和RMSE分别为0.92和9.38天。基于PhenoCam数据，采用新指标和双逻辑斯蒂函数进行物候提取，方法简便有效，准确率高。因此，建议采用光谱和纹理指数相结合的新指数，利用PhenoCam数据提取玉米物候。
Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data.
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