利用无人机高光谱估算冬小麦叶绿素含量

叶绿素含量（SPAD）是作物长势评价的重要指标，可以监测农作物的生长状况，对农业管理至关重要，因此快速、准确地估算SPAD具有重要意义。以冬小麦为研究对象，利用无人机高光谱获取了拔节期、挑旗期和开花期的影像数据，获取植被指数和红边参数，研究植被指数与红边参数估算SPAD的能力。先将植被指数与红边参数分别与不同生育期的SPAD进行相关性分析，再基于植被指数、植被指数结合红边参数，通过偏最小二乘回归（PLSR）方法估算SPAD，最后制作SPAD分布图验证模型的有效性。结果表明，（1）大部分植被指数与红边参数在3个主要生育期与SPAD相关性均达到极显著水平（0.01显著）；（2）单个植被指数构建的SPAD估算模型中，LCI表现最好（R2=0.56，RMSE=2.96，NRMSE=8.14%），红边参数中Dr/Dr_min表现最好（R2=0.49，RMSE=3.18，NRMSE=8.76%）；（3）基于植被指数结合红边参数构建的SPAD估算模型效果最佳，优于仅基于植被指数构建的SPAD估算模型，同时，随着生育期推移，两种模型均在开花期达到最高精度，R2分别为0.73和0.78，RMSE分别为2.49和2.22，NRMSE分别为5.57%和4.95%。因此，基于植被指数结合红边参数，并使用PLSR方法可以更好地估算SPAD，可以为基于无人机遥感的SPAD监测提供一种新的方法，也可为农业管理提供参考。

Estimation of Chlorophyll Content in Winter Wheat Based on UAV Hyperspectral

Chlorophyll content (SPAD) is a vital index for crop growth evaluation, which can monitor the growth of crops and is crucial for agricultural management, so it is important to estimate SPAD quickly and accurately. In this study, the remote sensing images of the jointing, flagging, and flowering stages were acquired using UAV hyperspectral for winter wheat. The vegetation indices and red edge parameters were extracted to explore the ability of vegetation indices and red edge parameters to estimate SPAD. Firstly, the vegetation indices and red edge parameters were correlated with the SPAD of different fertility stages. Then, the SPAD was estimated based on the vegetation indices, vegetation indices combined with red edge parameters , and using partial least square regression (PLSR) method. Finally, the SPAD distribution map was produced to verify the validity of the model. The results showed that (1) most of the vegetation indices and red edge parameters were correlated with SPAD at highly significant levels (0.01 significant) in all three major reproductive stages; (2) the SPAD estimation model constructed from individual vegetation index had the best performance for LCI among vegetation indexes (best R2=0.56, RMSE=2.96, NRMSE=8.14%) and Dr/Dr_min performed best (best R2=0.49, RMSE=3.18, NRMSE=8.76%); (3) SPAD estimation model based on vegetation indices combined with red edge parameters was the best and better than SPAD estimation model based on vegetation indices only. Meanwhile, both models reached the highest accuracy at the flowering stage as the fertility stage progressed, with R2 of 0.73 and 0.78, RMSE of 2.49 and 2.22, and NRMSE of 5.57% and 4.95%, respectively. Therefore, based on the vegetation indices combined with the red edge parameters, using the PLSR method can improve the estimation effect of SPAD, which can provide a new method for SPAD monitoring based on UAV remote sensing, and also provide a reference for agricultural management.