Abstract: Cropping intensity (CI) is essentially related to the utilization condition of arable land. A classification method based on high spatial resolution data (TM), and a peak-counting method based on high temporal resolution NDVI data (MODIS NDVI) are mainly used to extract CI in remotely sensed field. However, it is difficult to acquire the high spatial resolution data with high quality characteristics in the most classification method, as three requirements cannot be satisfied concurrently. The first condition is the time requirement, where the high spatial resolution data can be observed in the growing season. The second one is the frequency requirements, where at least one period of data can be obtained in the growing season. The third one is the quality requirement, where the data cannot be polluted by the cloud, mist and other environmental surroundings. Furthermore, the performance was relatively low, when the peak-counting method was applied in some complex surface regions, since the high temporal resolution data has the low spatial resolution (>250 m). A feasible strategy can be made to obtain a high extraction accuracy of CI, in order to improve the spatial resolution of high temporal resolution data in the use of spatiotemporal data fusion (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM). Taking the hilly region in the eastern Sichuan as a research area, the 2010-2011 time series Landsat-like NDVI dataset with high temporal and spatial resolution was collected to extract the spatial distribution of cropping area for the winter-season in2010 and the CI for summer- and winter-seasons in 2011 in Yanting County, using the STARFM algorithm, Landsat TM and MODIS data. Precision validation between different data in various methods was done at a series of spatial scales (30-4 000 m), referring to the visual interpretation of 1509 random samples, and the spatial distribution of cropping area for winter-season in2010 using Landsat data. The results showed that: 1) Compared to the peak-counting method using Landsat-like time-series NDVI dataset, the overall accuracy of verification was higher for the classification method using Landsat data (89.73% vs. 54.94%), at the scale of 30 m. 2) Compared to the peak-counting method using MODIS time-series NDVI dataset, the overall accuracy of verification was higher 3%-7% for the peak-counting method using Landsat-like time-series NDVI dataset, at the scales from 250 m to 4 000 m. 3) The overall accuracy of verification was higher 0.17, when the data from the new method was spatially aggregated from 30 m to 4 000 m. In the CI result of 2011 in Yanting County, the data in the Mianyang Statistical Yearbook of 2011, and 73 samples from the field survey data in August 2013 were used to validate the practicability of new method. There were very close CI values from the statistical data (1.69) and new method (1.67), indicating that the overall accuracy of verification was 73.97% in the field survey. A high accuracy of extraction and fine spatial details of CI can be acquired, when using the fusion of spatiotemporal data in some complex surface regions. The findings can contribute to the evaluation of food production and carbon sequestration potential in complex surface regions.