Analyzing scale effects of crop LAI based on PCA method

In order to quantitatively analyze the scale effects of leaf area index (LAI) of crop canopies, an analyzing method based on principal component analysis (PCA) theory was proposed in this paper. In this method, PCA theory was introduced for data mining and reorganization, which fully considered the correlation and variability of multi-resolution data. Dynamical multiple linear regression theory was selected for LAI estimation by taking principal components (PCs) as independent variables. Barley and corn were chosen as experimental objects. Firstly, observed data at different spatial scales were constructed by polymerization method based on the small scale observed data. Secondly, the scale effects analyzing method proposed in this work was used for data processing and LAI estimation. Finally, the number of effective principle components (NEPCs), coefficient of determination (R2), and mean relative accuracy (MRA) were selected as testing indicators to analyze the above results to quantitatively describe scale effects of crop LAI. The theory analyses and numerical practices verified the feasibility and validity of this proposed method in analyzing scale effects of crop LAI.