使用聚类回归方法从无人机高光谱数据估计冬小麦 SPAD

摘要：土壤植物分析发展 (SPAD) 值表示叶片中的相对叶绿素含量。叶绿素作为光合剂在小麦生长和施肥管理中起着至 关重要的作用。无人机 (UAV) 高光谱数据与田间测量的 SPAD 值相结合，被广泛用于研究小麦叶绿素浓度随时间的变 化。在本研究中，考虑到不同土壤肥力田地的光谱差异，我们提出了一种名为聚类回归的新建模方法，用于估算中国安 徽北部国家土壤观测和实验站的冬小麦 SPAD。根据光谱角距离将小麦光谱分为几个簇，并为每个簇建立小麦光谱与测 量的 SPAD 之间的回归模型。我们使用 K-means 作为聚类方法，并使用两种类型的集成学习算法，即随机森林和极端 梯度提升 (XGBoost) 作为回归方法。以均方根误差（（ RMSE）、中位绝对百分比误差（（ MAPE）和R2作为模型精度指标。 结果表明，XGBoost模型在小麦SPAD估算中略优于随机森林。结合第一步K均值聚类，回归模型的整体性能可以轻松 提高，其中cluster-XGBoost表现最佳，RMSE为1.444，MAPE为2.36％，R2为0.925。此外，土壤有机质和土壤总氮的 加入对 SPAD 估算模型的精度有积极影响。本研究为小麦 SPAD 浓度估算提供了一种有效的无人机高光谱技术，并展示 了土壤有机质和总氮对小麦SPAD估算的影响。

关键词：SPAD估算，冬小麦，极端梯度提升（XGBoost），无人机高光谱数据

期刊：International Journal of Applied Earth Observations and Geoinformation

DOI：https://DOI.org/10.1016/j.jag.2021.102618

设备：GaiaSky

Winter wheat SPAD estimation from UAV hyperspectral data using cluster- regression methods

Abstract：Soil plant analysis development (SPAD) values indicate the relative chlorophyll content in leaves. Chlorophyll plays a vital role in wheat growth and fertilization management as a photosynthetic agent. Unmanned aerial vehicle (UAV) hyperspectral data, combined with measured SPAD values in fields, are widely used to study wheat chlorophyll concentrations over time. In this study, considering the spectral differences in fields with different soil fertility, we propose a new modeling method named as cluster- regression to estimate the winter wheat SPAD at a national soil observation and experimental station in northern Anhui, China. The wheat spectrum was divided into several clusters according to the spectral angular distance, and regression models were built between wheat spectrum and the measured SPAD for each cluster. We used K-means as the clustering method and two types of ensemble learning algorithms, namely, random forests and Extreme Gradient Boosting (XGBoost) as the regression methods. The root-mean-square error (RMSE), median absolute percentage error (MAPE), and R2 were used as model accuracy indicators. The results showed that the XGBoost model slightly outperformed the random forest in wheat SPAD estimation. Coupled with the first step of K-means clustering, the overall performance of the regression models can be easily improved, and cluster-XGBoost showed the best performance, with an RMSE of 1.444, MAPE of 2.36%, and R2 of 0.925. Moreover, addition of soil organic matter and soil total nitrogen positively impacted the accuracy of SPAD estimation models. This study provides an effective UAV hyperspectral technique for wheat SPAD concentration estimation and demonstrates the impact of soil organic matter and total nitrogen on wheat SPAD estimation.

Keywords：SPAD estimation Winter wheat Extreme Gradient Boosting (XGBoost) Cluster-RF Cluster-XGBoost UAV hyperspectral data