The change detection method is extensively used in the extraction of paddy rice using remote sensing images. However, the precision of paddy rice classification is reduced by "cloud contamination" and "salt and pepper." "Cloud contamination" occurs frequently in paddy rice planting areas during the autumn season. The images obtained by using the change detection method to extract paddy information lead to missing spectral information and constraints in using change detection. "Salt and pepper" occurs by misregistration, and a variety of errors are encountered in the classification, which yields false information and results in the accumulation of deviation during extraction. However, these two crucial issues have a detrimental effect on the ability to extract paddy accurately, which must be solved to increase the accuracy of extraction. In this study, an innovative model called temporal–spatial–fusion model（TSFM） is proposed to reduce the effect of these noises.In this model, we built a temporal–spatial–belonging degree algorithm. First, the TSFM calculated the attribution probability of the target pixel by using the spectral information of neighborhood pixels in spatial dimensions. We searched the classification of each thematic map in a critical period of paddy growing with a window and calculated the belonging degree in spatial dimensions. Second, we computed the mean of the belonging degree of pixels in the same geospatial location by using the time series of remote sensing images in time dimensions, which is the temporal–spatial–belonging degree of the pixel. Third, the paddy rice was extracted by defining the threshold derived by using the change magnitude threshold determination method.Post-classification comparison（PCC） and majority voting（MV） were introduced to map the paddy rice and to validate the proposed algorithm. We assessed the precision of the result of paddy rice in the entire study area and the "cloud contamination" area with confusion matrix method. The degree of landscape fragmentation was used to assess the effect of the mixture of spectral information of crop, which should be analyzed. Thus, two districts were selected as study areas with different degrees of landscape fragmentation based on a visual appraisal of the study area. The accuracy was compared, and the applicability and difference using TSFM were analyzed in these two regions.Experimental results show that the precision of the user, accuracy of the producer, and overall accuracy of the TSFM with 3 × 3 window size are 93.4%, 83.5%, and 87.9%,respectively. Compared with the traditional change detection method of PCC, these precisions are higher than 2.3%, 12.3%, and 9.3%. When different window sizes are used to identify the paddy rice, these precisions are higher than that of the PCC results. The overall accuracy is better than 92.0%,and the omission errors of the PCC and MV are reduced by 14.0% and 7.6%, respectively, in the area of cloud contamination. The results of classification using TSFM with different window sizes in the regular and fragmented regions varied, providing a foundation for the use of TSFM in different landscapes.Experimental results showed that the TSFM effectively solved the problem of errors from "cloud contamination" and "salt and pepper."The TSFM provides a new and potentially effective method for paddy rice mapping based on change detection. In the future, we will attempt to apply this method to a large area in China with fragmented and complex landscape.

identification;temporal-spatial-belonging degree;change detection;landscape features;classification accuracy;paddy rice