Abstract: Empirical-model-based inversion is an important approach to the retrieval of leaf area index (LAI).The ground sampling data is the primary data resource of Empirical modeling,and the sample size affects directly the precision of the empirical model.However,few studies were investigated about the effects of sample size on remote sensing empirical modeling accuracy of LAI.This article,based on the ground sample data sampled repeatedly with different sample sizes,built an empirical model of LAI to explore the effects of sample size on modeling accuracy.The results showed that:① remote sensing model accuracy index (RMAI) decreases with increase in sample size in the power function form; ② when the number of samples is less than 30,RMAI is more sensitive and the modeling accuracy is lower,while the sample size approaches 45,the modeling accuracy reaches a steady state; ③ the larger the sample size,the more stable the modeling based on sample data; ④ giving consideration to the change trends of the mean and standard deviation of RMAI,it comes to the conclusion that the sample data,whose sample size reaches 40,can build a stable empirical model with high precision.