Fan Jiejie, Liu Yang, Fan Yiguang, Yao Yihan, Chen Riqiang, Bian Mingbo, Ma Yanpeng, Wang Huifang, Feng Haikuan
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
College of Geomatics, Xi'an University of Science and Technology, Xi'an, China.
Front Plant Sci. 2024 Nov 22;15:1492372. doi: 10.3389/fpls.2024.1492372. eCollection 2024.
The Leaf Area Index (LAI) is a crucial parameter for evaluating crop growth and informing fertilization management in agricultural fields. Compared to traditional methods, UAV-based hyperspectral imaging technology offers significant advantages for non-destructive, rapid monitoring of crop LAI by simultaneously capturing both spectral information and two-dimensional images of the crop canopy, which reflect changes in its structure. While numerous studies have demonstrated that various texture features, such as the Gray-Level Co-occurrence Matrix (GLCM), can be used independently or in combination with crop canopy spectral data for LAI estimation, limited research exists on the application of Haralick textures for evaluating crop LAI across multiple growth stages. In this study, experiments were conducted on two early-maturing potato varieties, subjected to different treatments (e.g., planting density and nitrogen levels) at the Xiaotangshan base in Beijing, during three key growth stages. Crop canopy spectral reflectance and Haralick textures were extracted from ultra-low-altitude UAV hyperspectral imagery, while LAI was measured using ground-based methods. Three types of spectral data-original spectral reflectance (OSR), first-order differential spectral reflectance (FDSR), and vegetation indices (VIs)-along with three types of Haralick textures-simple, advanced, and higher-order-were analyzed for their correlation with LAI across multiple growth stages. A model for LAI estimation in potato at multiple growth stages based on spectral and textural features screened by the successive projection algorithm (SPA) was constructed using partial least squares regression (PLSR), random forest regression (RFR) and gaussian process regression (GPR) machine learning methods. The results indicated that: (1) Spectral data demonstrate greater sensitivity to LAI than Haralick textures, with sensitivity decreasing in the order of VIs, FDSR and OSR; (2) spectral data alone provide more accurate LAI estimates than Haralick textures, with VIs achieving an accuracy of R² = 0.63, RMSE = 0.38, NRMSE = 28.36%; and (3) although Haralick textures alone were not effective for LAI estimation, they can enhance LAI prediction when combined with spectral data, with the GPR method achieving ² = 0.70, RMSE = 0.30, NRMSE = 20.28%. These findings offer a valuable reference for large-scale, accurate monitoring of potato LAI.
叶面积指数（LAI）是评估作物生长以及指导农田施肥管理的关键参数。与传统方法相比，基于无人机的高光谱成像技术在通过同时获取作物冠层的光谱信息和二维图像来无损、快速监测作物LAI方面具有显著优势，这些图像反映了作物冠层结构的变化。虽然众多研究表明，各种纹理特征，如灰度共生矩阵（GLCM），可单独使用或与作物冠层光谱数据结合用于LAI估计，但关于Haralick纹理在评估多个生长阶段作物LAI方面的应用研究较少。本研究在北京小汤山基地对两个早熟马铃薯品种进行了实验，在三个关键生长阶段对其进行了不同处理（如种植密度和氮水平）。从超低空无人机高光谱图像中提取作物冠层光谱反射率和Haralick纹理，同时使用地面方法测量LAI。分析了三种类型的光谱数据——原始光谱反射率（OSR）、一阶微分光谱反射率（FDSR）和植被指数（VIs）——以及三种类型的Haralick纹理——简单纹理、高级纹理和高阶纹理——在多个生长阶段与LAI的相关性。使用偏最小二乘回归（PLSR）、随机森林回归（RFR）和高斯过程回归（GPR）机器学习方法构建了基于连续投影算法（SPA）筛选出的光谱和纹理特征的马铃薯多生长阶段LAI估计模型。结果表明：（1）光谱数据对LAI的敏感性高于Haralick纹理，敏感性顺序为VIs、FDSR和OSR；（2）单独的光谱数据比Haralick纹理提供更准确的LAI估计，VIs的估计精度为R² = 0.63，RMSE = 0.38，NRMSE = 28.36%；（3）虽然单独的Haralick纹理对LAI估计无效，但与光谱数据结合时可提高LAI预测效果，GPR方法的R² = 0.70，RMSE = 0.30，NRMSE = 20.28%。这些发现为大规模、准确监测马铃薯LAI提供了有价值的参考。
