Zou Mengxi, Liu Yu, Fu Maodong, Li Cunjun, Zhou Zixiang, Meng Haoran, Xing Enguang, Ren Yanmin
College of Geomatics, Xi'an University of Science and Technology, Xi'an, China.
Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
Front Plant Sci. 2024 Jan 3;14:1272049. doi: 10.3389/fpls.2023.1272049. eCollection 2023.
Leaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider.
Taking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples.
The ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R (R0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54).
This study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing.
叶面积指数（LAI）是一个关键的生理生化参数，对植被生长有着深远影响。准确估算拔节期冬小麦的叶面积指数对于监测小麦生长状况和优化变量施肥决策尤为重要。近年来，无人机（UAV）数据和机器学习/深度学习方法被广泛应用于作物生长参数估计。在传统方法中，植被指数（VI）和纹理通常用于估算叶面积指数。与它们不同，株高（PH）包含了植物垂直结构的信息，应予以考虑。
本文以中国河北省沧州市西辛店乡为研究区域，应用支持向量机（SVM）、反向传播神经网络（BPNN）、随机森林（RF）、极端梯度提升（XGBoost）这四种机器学习算法以及卷积神经网络（CNN）和长短期记忆神经网络（LSTM）这两种深度学习算法，通过整合无人机多光谱图像的光谱、纹理特征以及株高信息来估算拔节期冬小麦的叶面积指数。首先，生成数字表面模型（DSM）和数字正射影像图（DOM）。随后，提取株高、植被指数和纹理特征，并进一步构建纹理指数（TI）。收集同期地面实测叶面积指数，并计算其与株高、植被指数和纹理指数的皮尔逊相关系数，以挑选出相关性高的特征变量。将植被指数、纹理指数、株高及融合特征作为独立特征，采用基于联合x - y距离的样本集划分（SPXY）方法划分样本的校准集和验证集。
评估了不同输入和算法估算冬小麦叶面积指数的能力。结果表明：（1）添加株高作为特征变量显著提高了叶面积指数估算的准确性，表明小麦株高作为基于传统指数的叶面积指数反演建模的补充参数发挥了重要作用；（2）包括归一化差异纹理指数（NDTI）、差异纹理指数（DTI）和比值纹理指数（RTI）在内的纹理特征组合显著提高了纹理特征与叶面积指数之间的相关性；此外，植被指数、纹理指数和株高的多特征组合在估算冬小麦叶面积指数方面表现出卓越能力；（多特征组合在估算冬小麦叶面积指数方面表现出卓越能力；（3）六种回归算法在估算叶面积指数方面均取得了较高精度，其中XGBoost算法估算冬小麦叶面积指数的总体精度最高且效果最佳，达到最高的R（R = 0.88）、最低的均方根误差（RMSE = 0.69）以及大于2的相对分析性能指标（RPD = 2.54）。
本研究提供了有力证据，表明利用XGBoost并整合从无人机数据中提取光谱、纹理和株高信息能够准确监测冬小麦拔节期的叶面积指数。研究结果将为通过遥感准确监测作物参数提供新的视角。
