## 5 Conclusions  
The temporal-spatial fusion of remote sensing data is an effective approach to solve the dilemma of the simultaneous attainment of high temporal and spatial resolution remote sensing images. In this study, an USTARFM algorithm based on improved STARFM supported by an unmixing-based method was developed to generate high-temporal-spatial remote sensing data. The USTARFM algorithm was tested by the experimental data from Landsat 8/ Landsat 7 and MODIS images in two study areas. USTARFM adopts the unmixed fraction from the unmixing data, which is able to reflect surface information in heterogeneous areas, to take place of the directed resampled data used for STARFM, which can increase the probability of searching the \"pure pixels\" and guarantee the fusion accuracy. This enables the USTARFM to achieve better performance than STARFM at the same window size, even smaller (_w_, 11 \(\times\) 11 _vs_. 31 \(\times\) 31). In the Hengshui study area, taking the predicted NIR band reflectance as an example, with the best combination of window size, class number and similar pixels searching window, \(\gamma\) of the USTARFM, STARFM and unmixing were 0.96, 0.95, 0.90, respectively (_p_-value \(<\) 0.001); RMSE were 0.0245, 0.0300, 0.0401, respectively; and ERGAS were 0.5416, 0.6507, 0.8737, respectively. However, the scatterplots of synthetic data produced by USTARFM are more close to 1:1. The synthetic images produced by the USTARFM look more similar to the reference images in RGB combination manner (Figure 7). Especially in the broken area, USTARFM is skilled in reducing the homogeneous \"plot\" generated by the unmixing method and maintains the fidelity of the spectrum. In addition, the similar conclusions for USTARFM were also obtained for a second study area in Virginia which is a relatively heterogeneous area. The influence of heterogeneity on the three methods shows the certain regularity (Figure 5). The USTARM showed consistent higher performance than those of STARM, except for the Red and Green bands when heterogeneity degree as 1, highlighting that this method is capable of solving the problem of fused data that is present in STARM. It is an exciting result that, for the NIR band, the USTARFM is  
Figure 8: Landsat images collected 26 February 2002 (1) and May 17 (2) and their enlarged sub-images (**a1**) and (**a2**); and the corresponding prediction sub-images by USTARFM (**b1, b2**), STARFM (**c1, c2**), and unmixing-based method (**d1, d2**). performs consistently better than STARFM when heterogeneity degree ranges from 1 to 10. Compared to the short-wave band as Red and Blue, the long-wave band, NIR, it is insensitive to the atmospheric effect to ensure the unmixing result, even in the area when heterogeneous degree is 1. On the whole, USTARFM works better than STARFM and the unmixing based method. As the heterogeneity increased, the USTARFM was better than the STARFM and unmixing method. But if the land-cover patches are \"extremely\" broken and the high-spatial remote sensing images are unable to capture the texture details, it is still difficult to achieve an acceptable result. There are some issues that need to be addressed in further study. First, the accuracy of the USTARFM depends on the performance of the unmixing data generated from Equation (1). The USTARFM method could get better results depending on the higher precision unmixing data. How to achieve better unmixed data from development of unmixing model or finer remote sensing is still an important research topic. Next, the land-cover types changed greatly over the long time span of this research, the performances of USTARFM and STARFM are degraded, such as 0.12 and 0.16 of correlation coefficient in green band lower than usual analyzed in Virginia area in this study. This problem urgently needs to be addressed because this affects the ability of USTARFM to detect cultivated land where usually had abrupt change at short temporal scale.