Fan Xianglong, Gao Pan, Zhang Mengli, Cang Hao, Zhang Lifu, Zhang Ze, Wang Jin, Lv Xin, Zhang Qiang, Ma Lulu
Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi, Xinjiang, China.
College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang, China.
Front Plant Sci. 2024 Jul 4;15:1357193. doi: 10.3389/fpls.2024.1357193. eCollection 2024.
Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status.
In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI.
The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively.
Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.
叶面积指数（LAI）的快速准确估算对精准农业具有重要意义，因为LAI是评估作物冠层结构和生长状况的重要参数。
本研究利用棉花冠层光谱构建了20种植被指数。然后，基于多种机器学习（ML）方法[极限学习机（ELM）、随机森林（RF）、反向传播（BP）、多元线性回归（MLR）、支持向量机（SVM）]构建棉花LAI估算模型，并选择了最优建模策略（RF）。最后，将与LAI相关性高的植被指数进行融合，构建VI融合RF模型，以探索多植被指数融合在棉花LAI估算中的潜力。
在LAI估算模型中，RF模型的估算精度最高，融合多个植被指数构建的模型估算精度高于基于单一植被指数构建的模型。在多VI融合模型中，基于7种植被指数（MNDSI、SRI、GRVI、REP、CIred-edge、MSR和NVI）融合构建的RF模型估算精度最高，决定系数（R2）、均方根误差（RMSE）、归一化均方根误差（NRMSE）和平均绝对误差（MAE）分别为0.90、0.50、0.14和0.26。
适当融合植被指数可在建模中纳入更多光谱特征，显著提高棉花LAI估算精度。本研究将为提高棉花LAI估算精度提供技术参考，所提方法在作物生长监测应用中具有巨大潜力。
