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In order to solve the problem of large canopy equivalent water thickness (EWT) inversion error caused by spatial heterogeneity, taking four maize fields with large growth differences as the research object, EWT data of six key growth nodes was collected, and UAV multispectral remote sensing technology was used to obtain orthophoto images in the field, and the spectral and texture information of different window space sizes (0.1m×0.1m to 2.0m×2.0m) of remote sensing images in the form of sliding windows was extracted, and after multicollinearity testing, principal component analysis (PCA) was used to reduce the dimensionality of spectral parameters (S), texture parameters (T) and combinatorial parameters (S+T), respectively, and then the EWT inversion model was constructed by partial least squares (PLS), random forest (RF) and support vector machine (SVM), respectively, and then the accuracy of the model was tested by Kruskal-Wallis, and the choice of optimal window size was discussed according to the results of multiple tests. The results showed that with the gradual increase of the window space scale, the accuracy of the EWT inversion model was increased firstly and then decreased. The accuracy of the model constructed with the S+T as the input variable was significantly better than that of the S and the T, and the adjusted R-square (R2adj) of the optimal window size of the model based on PLS, RF and SVM was increased by 0.16, 0.05 and 0.12, respectively, and the relative root mean square error (RRMSE) was decreased by 4.95%, 1.17% and 3.80%, respectively. The results showed that it was feasible to use texture features to improve the inversion accuracy of EWT model. Comprehensively comparing the nine sets of models constructed by different modeling methods, the optimal sampling window spatial size was finally determined to be 0.7m×0.7m, with R2adj up to 0.82 (corresponding RRMSE of 16.57%). The research result can provide a reference for information mining and EWT monitoring based on UAV multi-spectral image analysis.