Analyzing the Spatial Scaling Bias of Rice Leaf Area Index From Hyperspectral Data Using Wavelet–Fractal Technique

Leaf area index (LAI) is a basic quantity indicating crop growth situation and plays a significant role in ecological model and interaction model between earth surface and atmosphere. However, nonlinear estimation processes of LAI from heterogeneous remote sensing data would induce a scaling bias. The purpose of this study is to provide a method to evaluate and correct the scaling bias. For the effectiveness of the method, first both statistical and physical models were built to estimate LAI directly from modified soil-adjusted vegetation index (MSAVI) as a function with univariate and also from red and near infrared reflectances as a bivariate model. The analysis of wavelet transform and fractal theory revealed that the scaling bias and the high-frequency coefficient from LAI at fine resolution decomposed by Haar wavelet were fractal relation. Based on the wavelet-fractal method, scaling bias could be well denoted by high-frequency coefficient in log-log coordinate for both univariate model and bivariate model, and the root-mean-square error (RMSE) and relative error (RE) of estimated LAI caused by the scaling bias could be greatly reduced after scaling correction. Additionally, to analyze the influence of spatial heterogeneity and nonlinearity of the retrieval model, the scaling bias was investigated on horizontal comparison of LAI retrieval models with univariate and bivariate at a certain resolution and was longitudinally discussed in a retrieval model at different aggregation scales. This study suggests that it is feasible to successfully correct and analyze the scaling bias using the wavelet-fractal method.