Surface water is an essential carrier for the balance of ecosystems and human existence. As applied hyperspectral remote sensing has improved, the research into water extraction from the hyperspectral image gradually attracted more attention. However, delineating water from the hyperspectral image is challenging due to shadows of buildings and trees, and other dark pixels. Furthermore, water indexes that come from multispectral imagery are directly applied to hyperspectral imagery leads to some defects, such as underutilized abundant information and higher misclassifications. Here, we proposed a decision tree water index (DTWI) method to extract water bodies from hyperspectral imagery. Combining the threshold of the near-infrared reflectance and the magnitude of the difference between reflectance of 700 and 730 nm, the DTWI method could effectively extract water from hyperspectral imagery. Three hyperspectral images derived from the study areas within the city, a mixture of city and suburban zone, and suburban zone only, were applied to test the feasibility and flexibility of this method in various sensors and environments. Four alternative methods were used to verify the robustness of the proposed method, including hyperspectral difference water index, normalized difference water index, shaded building index, and support vector machine (SVM). Our results indicated that the proposed method was better suited to extracting water bodies from hyperspectral imagery when compared with other methods and could effectively capture water on different hyperspectral sensors, various heterogeneous surroundings, and multi-spatial scales. The proposed DTWI method has high stability in threshold and band selection, which is comparable to the SVM method in the mean overall accuracy and kappa coefficient (DTWI: 0.98 and 0.81; SVM: 0.98 and 0.82). For the view of elapsed time, calculation, and applicability, the proposed method has the potential of better utilization and rapid response for water extraction in hyperspectral imagery.