Accurately obtaining annual cropland area and spatiotemporal change information is crucial for safeguarding the agricultural land redline and improving the efficient, economical, and intensive utilization of water resources. It also provides a scientific basis for navigation and intelligent scheduling of agricultural machinery, thereby effectively enhancing the precision and efficiency of mechanized agricultural operations. Taking the Aksu river basin in the Xinjiang Uygur Autonomous Region as an example, this study proposes a method for extracting cropland spatiotemporal change information based on time-series index features. First, based on NDVI data derived from time-series remote sensing images covering the entire crop growth phenological period, annual composite vegetation index images are generated using the maximum value compositing method, and these images are stacked into bands to construct a time-series index feature image. Second, an object-oriented segmentation approach is applied to perform multi-scale segmentation on the feature image to obtain segmentation results. Finally, the change patterns of cropland across different years revealed by the feature image are analyzed, and extraction rules for different cropland change types are established. A decision tree classification method is then employed to achieve rapid extraction of annual cropland spatiotemporal change information. Experimental results demonstrate that the proposed method can extract annual cropland change information rapidly and accurately. Analysis of the annual change information shows that the cropland area in the Aksu river basin exhibited an overall increasing trend from 2021 to 2024, with relatively high growth rates of 1.24% and 1.28% in 2022 and 2023, respectively, while the growth rate slowed to 0.42% in 2024. To validate the accuracy of the extraction results, a comparison between the statistical yearbook data of selected counties and the remote sensing monitoring data was conducted, revealing a relative error of approximately 5%. This confirms that the proposed method attains satisfactory accuracy in extracting cropland area.