To explore the optimization of the KNN algorithm and use Sentinel-2 for large-scale estimation of forest AGB.In this study,Xiangtan City,Ningxiang City and Wangcheng District in Changsha City in Hunan Province were selected as the study area,and Quercus×Leana and Cunninghamia lanceolata were used as the target tree species.A KNN optimization algorithm based on the optimal K-value(Optimal-K KNN,OK-KNN)was proposed to achieve remote sensing estimation and spatial mapping of forest AGB,using Sentinel-2 as the source of remote sensing data in combation with ground survey data.To examine the performance of the OK-KNN model,the OK-KNN model was compared with the traditional KNN model,the distance-weighted KNN(DW-KNN)model and the multiple linear regression(MLR)model,and the three metrics-coefficient of determination(R²),root mean square error(RMSE)and relative RMSE(rRMSE)were calculated for evaluating the model's estimation results.The results showed that the three KNN models had better forest AGB prediction performance than the MLR model,and among the three KNN models,the OK-KNN model obtained the optimal estimation results,with the R² of Cunninghamia lanceolata samples improved by 17.02% and 13.04%,and the RMSE reduced by 17.21% and 7.03%,respectively,when compared to the ordinary KNN and DW-KNN models;R² for Quercus×Leana samples improved by 20.93% and 13.04%,and RMSE decreased by 15.17% and 9.24%,respectively.This study demonstrates that the optimal K-value adaptive selection of different samples can be realized using the OK-KNN model,which effectively improves the estimation accuracy of forest AGB.