## 4 Results and Discussion
### Algorithm Applicability Analysis  
We analyzed another series of study dates to test the applicability of USTARFM. This study area is situated in a region of Virginia, in the Eastern United States (about 37.51\({}^{\circ}\)-37.72\({}^{\circ}\) N and 76.99\({}^{\circ}\)-77.26\({}^{\circ}\) W) where there are many small patches of different land types including forest, bare soil, and residential patches. The dataset includes Landsat 7 ETM+ and MOD09 GA images acquired for 25 January, 26 February, and 17 May, in 2002. In the study, the Landsat 7 ETM+ and MOD09 GA for 25 January is the base data for predicting the Landsat-like data collected 26 February and 17 May 2002. The dataset has been applied for data fused study, and the detailed description about study area and dataset can be found in publication [30].  
The series parameters for the USTARFM were the same as for the previous experiment. Through analyzing the unmixed results of MODIS data for data acquired 25 January 2002, the optimal combination of window size and the class number (\(W\) = 31, \(k\) = 10) were also determined for unmixing the MODIS data acquired for 26 February, and 17 May 2002. Then these unmixed data are input to USTARFM for predicting the surface reflectance for data collected 26 February and 17 May 2002. The result for 26 February 2002 shows the accuracy of USTARFM is better than STARFM (_i.e._, for green band, the \(\gamma\): 0.92 _vs._ 0.90; RMSE: 0.0097 _vs._ 0.0116; ERGAS: 0.9058 _vs._ 1.0797), which achieved higher accuracy at a smaller window size compared to STARFM (_w_ = 15 \(\times\) 15 _vs._ 45 \(\times\) 45). The predicted reflectance of 17 May 2002 by USTARFM are nearly the same for the unmixing method, and are still better than STARFM (_i.e._, for green band, the \(\gamma\): 0.80 _vs._ 0.83 _vs._ 0.74; RMSE: 0.0159 _vs._ 0.0148 _vs._ 0.0178; ERGAS: 1.4529 _vs._ 1.3487 _vs._ 1.6289). However, the performance of the predicted result is lower than those acquired on 26 February 2002, because some changes of land-cover types from the larger time span occurred, which is also consistent with [PERSON]'s result [30].  
Figure 8 shows the synthetic images of 26 February and 17 May 2002 for the three methods. The prediction image by USTARFM (Figure 8(b1)) on 26 February 2002 is almost the same as that by STARFM (Figure 8(c1)) visually, but the quantitative evaluation of the former is better than the latter, and the unmixed image is worse for loss of texture details of surface ground in Figure 8(d1) (marked in circle). However, due to land cover changed over the long time span that single date Landsat was unable to record the changes, it was also unfulfillable to reflect the changed information from the fusion data [25]. This is why, in this paper, the predicted images from 17 May 2002 by USTARFM and  
Figure 7: The comparison of real and fusion images produced by the three algorithms (NIR-red-green combination). **(a)** shows the reference Landsat 8 images observed on 4 September 2014; **(b–d)** are the prediction images by USTARFM, STARFM, and unmixing-based method, respectively; **(e–h)** indicate the enlarged subset images of **(a–d)**, respectively.  
STARFM have some variances from the actual image shown in Figure 8(a2) (marked by a rectangle). Therefore, the STAARCH model, proposed by [PERSON] (1998), will offer us some new ideas to solve the problem. Besides, the high temporal frequency MODIS at long time scale can record the land surface changed trace, which may be a good alternative to reflect the crop changes in agricultural landscape.