Abstract: Heilongjiang province is the main area for paddy cultivation in China, and the phenomenon of paddy field expansion has contributed to huge changes in the land types in agricultural areas. Remote sensing is employed to rapidly and dynamically monitor the spatial and temporal changes of paddy fields, thus providing scientific support and decision-making basis for rational cultivation of crops and exploitation of land resources. Based on the above, the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and vegetation index data sets were selected as the main data source and the Landsat data set was chosen as the auxiliary data source in the present study. As MODIS images have the characteristics of large width and high update frequency, it is an ideal tool for accurate identification of large area crops. The current study remotely decoded paddy fields, drylands, river beaches, swampy meadows, forests, water, and towns in Heilongjiang province from 2003 to 2018 based on the decision tree model. Besides, the data from 2003 to 2010 was the calibration group, and the data from 2011 to 2018 was the validation group. Since the phenological characteristics and exponential intervals of the land classes all showed the difference, the classification rules of the land classes were also different. Statistical analysis was performed based on the spectral characteristics and time-series curves of the indices, including the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Land Surface Water Index (LSWI). Meanwhile, the classification rules for each land class were presented as follows: forests were extracted by the EVI and slope data threshold method on April 6. Water was extracted by the NDVI threshold method on October 16. Supervised classification was used to extract towns from the LSWI time-series and wetlands from the NDVI time-series. After carrying out repeated experiments, NDVI, LSWI, and Band 6 were used to identify the paddy field, and the threshold conditions included 0.45-0.77, 0-0.56, and 120-1 530 nm, respectively. The classification result images were verified by high-resolution Landsat images and statistical almanac data, respectively. The Kappa coefficient of the 2003-2018 paddy fields identification reached 0.899-0.961, the overall classification accuracy reached 85.5%-92.3%, and the paddy fields matched the statistical almanac data. To compare the advantages and disadvantages of decision tree model construction, the maximum likelihood method was selected for the comparison. In terms of the control group, the maximum likelihood classification method was used to identify paddy fields under the condition that other land classification rules were unchanged. From 2003 to 2010, the accuracy of the maximum likelihood method was 0.643-0.756, which was significantly lower than that of the decision tree method from 0.923-0.961, indicating that the classification of paddy fields using the threshold method was more effective compared with the maximum likelihood method. The classification results suggested that the area of paddy fields in Heilongjiang province expanded 3 times from 2003 to 2018, and the center of gravity of paddy fields in the sowing area extended approximately 160 km to the north. Paddy field expansion increased linearly, with an average expansion of 158 100 hm2 per year. From 2003 to 2018, the cumulative conversion from dry land was 2 502 400 hm2, and 154 900 hm2 of wetlands had been reclaimed in total. Moreover, the decision tree model proposed in the present study had provided an effective method for extracting paddy cultivated areas in Heilongjiang province, which could also offer lessons for land class identification in similar areas.