Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage

作者：Weilong Li, Jianjun Wang, Yuting Zhang, Yin Quan, Weiling Wang, Guisheng Zhou, Zhongyang Huo · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15245715 · 被引用次数：38 · 研究领域：Remote Sensing in Agriculture、Leaf Properties and Growth Measurement、Remote Sensing and LiDAR Applications

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R2 = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the b...