Integration of multi-resolution data for crop LAI estimation based on continuous wavelet

Abstract

Leaf area index (LAI) of crop canopies is important for crop growth monitoring and yield estimation. Considering the practical need of achieving distribution properties of LAI at a special spatial scale, and the difficult acquisition of corresponding observations at the same scale, a method integrating multi-resolution data at larger scales based on continuous wavelet theory is proposed to provide a more effective LAI dataset. For this method, firstly multi-scale wavelet theory is selected for multi-resolution data decomposition, and then decomposed signals and statistics of observations are coupled for wavelet reconstruction. Finally, the new constructed data is used for LAI estimation through multiple linear regression method. Barley is selected as experimental object. The performance of this method is quantitatively analyzed by testing indicators, i.e. Number of effective bands, R2, and MRA. Theory analysis and numerical practices fully confirm the feasibility and validity of the proposed method in crop LAI estimation.