Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing

作者：Yin Quan, Yuting Zhang, Weilong Li, Jianjun Wang, Weiling Wang, Irshad Ahmad, Guisheng Zhou, Zhongyang Huo · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15143595 · 被引用次数：53 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Soil Geostatistics and Mapping

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R2 = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R2 = 0.8069, RPD = 2.3135, RMSE = 2.0...