Abstract: The accurate acquisition of planting area and spatial distribution information is essential to monitor the growth and estimate the production of fruit trees (orchard). Remote sensing has been widely used in crop identification and monitoring in recent decades. Numerous classification algorithms have been developed based on various requirements for remote sensing data analysis. However, distinguishing fruit tree orchard and tea garden remains challenging, due to their similar spectral characteristics. Two GF-2 WFV (wide field of view) images, taken in summer and winter, were used to extract the spatial distribution of fruit trees in Jinting Town in the Taihu Lake Basin in this study. The normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and texture features were used to construct a decision tree model. Vegetation and non-vegetation were quickly identified by analyzing the spectral curves of ground features in the study area. However, spectral characteristic was a poor parameter to differentiate fruit trees from tea trees. Since fruit trees and tea trees have distinct textural features, GF-2 images with rich texture information on ground objects can help distinguish fruit trees from tea trees. Thus, texture is one of the most important features in fruit tree extraction. In this study, the method of cumulative difference (Δf) was used to determine the optimal size of the texture window. Among the Δf values of each texture under 15 different window scales (3×3, 5×5, 7×7, 9×9, 11×11, 13×13, 15×15, 17×17, 19×19, 21×21, 23×23, 25×25, 27×27, 29×29, 31×31), the 15×15 window was determined as the optimum texture window. In addition, five texture features that were easy to distinguish from other objects were selected according to the cumulative difference of variables such as mean, variance, contrast, entropy, and correlation under the optimal texture window. The results showed that the decision tree model based on spectral index NDVI and NDWI, combined with texture features, effectively distinguished fruit trees from tea trees. The method of cumulative difference can quickly determine the best texture window size and texture combination. The extraction results showed that fruit trees were widely distributed in all locations of Jinting Town and that the planting area in the south was larger than that in the north. The local detail map indicated that the distribution of fruit trees was relatively neat and mainly in the plain area. The extraction accuracy of fruit trees in this study was 95.23%. The overall accuracy of the model in this study was 89.57% and the kappa coefficient was 89.00%. The producer accuracy and user accuracy were 90.00% and 87.30%, respectively. Using spectral indices combined with textural features achieved a higher overall accuracy than using spectral indices or textural features alone, with an overall accuracy increase of 10.65% and 12.04%, respectively. This method can be applied to the remote sensing extraction of fruit trees on a large scale and can provide an important reference in fruit tree extraction by using texture characteristics of sub-meter images. Moreover, the cumulative difference proposed in this study provides a new method for selecting the best texture window.