## 3 Results and Validation
### Validation  
The procedure for validating the classification results is as follows:  
1. Randomly sample from the classification map by land cover types. The sample number for each class was determined by the area ratio of the class. The sampling details are shown in Table 4.
2. Locate the samples precisely on the remotely sensed images, including seasonal composites of Landsat-OLI/TM and VHSR images from Google Earth. 3. Manually interpret the land cover types of the samples by carefully inspecting the remotely sensed images and VHSR images from Google Earth. 4. Make a confusion matrix for each year. Table 4 gives an example of the confusion matrix of 2014. The overall accuracy of classification in 2014 is 93.68%, and the kappa coefficient is 0.92. Because of its easy confusion with forests and grassland, shrubland had the lowest accuracy, whose producer's accuracy (PA) was only 77.78%. Except that, the user's accuracy (UA) of grassland and bare land is a little bit lower than 90%, the PAs and UAs of the other classes are all over 90%. Figure 6: The long time series of land cover maps at HRB based on the proposed method. The OAs and Kappa coefficients for each year are listed in Table 5 and it shows that almost all of OAs are over or close to 90%. An average accuracy of 90.32% and an average kappa coefficient of 0.88 were achieved. The OA in 1986 is 85.2% and the result is degraded by the lack of data. Therefore, the proposed method is effective by combining high-precision samples with historical data to produce a land cover dataset. In order to further verify the land cover dataset in this study, the land cover maps at HRB from both GlobeLand30 and the proposed method were compared at close views in 2000 and 2010 at the first place, which is shown in Figure 7. Based on the visual comparison, the advantages of the proposed method can be concluded as follows:  
1. The classification accuracy of the proposed method is much higher than that of the GlobeLand30. For example, the small villages on A and C are accurately classified in our map and they are completely missed in the GlobeLand30 one. 2. The temporal consistency is better than that of the GlobeLand30. B1 (2000) and D1 (2010) from the GlobeLand30 were very different; B1 had a large area of shrub and water, but D1 did not. In contrast, B3 (2000) and D3 (2010) from the proposed method are very consistent. 3. Waterbodies can be better classified with seasonal composites in our method. Only limited data used in GlobeLand30 caused the waterbodies to not be discerned. In order to better illustrate the temporal consistency of our land cover maps at longer time series, the time series of major land covers, such as cropland, built-ups, and waterbodies for every five years from 1986-2015 were plotted. Figure 8 shows two close-view examples of time series land covers from 1986-2015. The cropland and built-ups have been continuously increasing. Furthermore, the total areas at HRB for every five years since 1986 for the major land covers, such as built-ups and urban, snow and ice, forest, and cropland, were calculated and plotted in Figure 9. The increasing cropland and built-ups are consistent with the increasing human activities. The decreasing of snow and ice is strongly related to the global warming trend. The time series analysis of land covers further supports the effectiveness of the proposed method for long time series of land cover mapping.