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The emerging unmanned aerial vehicle (UAV) remote sensing technology has gradually become a popular approach to achieve precise management of field crops. Some researches have been conducted on high spatiotemporal resolution, lowcost and accurate monitoring of crop growth. However, there is relatively little research about the estimation of rice leaf green content by integrating UAV multispectral vegetation index and texture features. UAV multispectral remote sensing images and ground measured chlorophyll content of rice were collected during tillering, flowering, and filling growth stages. A total of 50 features, 15 vegetation indices and 35 texture features, were calculated from multispectral images. The max-relevance and min-redundancy (mRMR) algorithm was applied to screen ten vegetation indices and ten texture features from these features. Three modeling strategies were adopted, namely based solely on vegetation indices, based solely on texture features, and based on the combination of vegetation indices and texture features. Four regression modeling algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were used to establish the rice chlorophyll content estimation models. The results showed that both the vegetation indices and texture features were highly correlated with the rice chlorophyll content. Among them, the NGBDI index and the B_M texture feature had the highest correlation, with Pearson coefficients of 0.77 and 0.73, respectively. The fusion of vegetation indices and texture features can effectively improve the estimation accuracy of rice chlorophyll content. Compared with the ANN model based on vegetation indices, the R2 was improved by 0.08 when adding texture features to the models. Among the four regression algorithms, the artificial neural network had the best regression estimation accuracy with R2 of 0.72 and RMSE of 1.52. Therefore, the fusion of vegetation indices and texture features derived from UAV multispectral images can accurately estimate rice chlorophyll content, providing information support for the refined management of rice in the field.