## 1 Introduction  
Considerable progress has been achieved in biophysical plant properties research using optical sensors [1]. For instance, blending remotely-sensed data from multiple sources may provide more useful information than each single sensor data [2], thereby enhancing the capability of remote sensing for the monitoring of land cover and land use change, vegetation phenology and ecological disturbance [3; 4; 5; 6]. Data fusion may also be applied to fill in \"missing days\" when dealing with satellite imagery with longer revisit periods [7]. The traditional image fusion methods can produce new multispectral high-resolution images with different spatial and spectral characteristics, such as principle component analysis (PCA) [8; 9; 10], intensity-hue-saturation (IHS) [11; 12], Brovey transform [13], synthetic variable ratio (SVR) [13] and wavelet transform [9; 14]. To obtain the reflectance data with high spatiotemporal resolution, a new developed image fusion modeling approach, the spatial and temporal adaptive reflectance fusion model (STARFM), has been proven to have the capacity of blending Landsat Thematic Mapper (TM)/Enhanced TM Plus (ETM+) images (with high spatial resolution) and Moderate-resolution Imaging Spectroradiometer (MODIS) images (with high temporal resolution) to simulate the daily surface reflectance at Landsat spatial resolution and MODIS temporal frequency [1; 5; 15]. The STARFM method has been applied to a confer-dominated area in central British Columbia, Canada, and the landscape-level forest structure and dynamics are characterized [1]. [PERSON] _et al._[16] combined the STARFM method and random forest classification models to produce synthetic images, and their results showed that this method improves the accuracy for conservation tillage classification. A combination of bilateral filtering and STARFM was applied for generating high spatiotemporal resolution land surface temperature for urban heat island monitoring [17]. The STARFM method was also applied to the generation and evaluation of gross primary productivity (GPP) by blending Landsat and MODIS image data [18; 19] and the monitoring of changes in vegetation phenology [4]. Considering sensor observation differences between different cover types, the STARFM was modified when calculating the weight function of the fusion model [20]. The quality of synthetic imagery produce by STARFM depends on the geographic region [6]. To improve upon this limitation, enhanced STARFM (ESTARFM) has developed based on a remotely-sensed reflectance trend between two dates and spectral unmixing theory, and it has a better performance in heterogeneous and changing landscapes [6]. The performance of STARFM and ESTARFM was assessed in two landscapes with contrasting spatial and temporal dynamics [21]. Although ESTARFM improved the capacity of STARFM in the heterogeneous and changing region, it does not address spatial autocorrelation, as pixel simulations rely on the information of the entire image, including the overall standard deviation of reflectance and the estimated number of land cover types. The pixels are selected as the similar pixels in ESTARFM if all fine-resolution images satisfy the following rule:  
\[\mid F(x_{i},y_{j},T_{m},B_{n})-F(x_{w/2},y_{w/2},T_{m},B_{n})\mid\leq\sigma(B_ {n})\,/\,m \tag{1}\]where \(F\) is the fine-resolution reflectance, (\(x_{i}\), \(y_{j}\)) indicates the pixel location, \(w\)/2 is the half size of the local moving window, \(T_{m}\) is the date of acquired image data, \(\sigma(B_{n})\) is the standard deviation of the reflectance of the \(n\)-th band (\(B_{n}\)) and \(m\) is the number of land cover types. However, according to the first law of geography, the autocorrelation decreases with distance [22], the overall image information may not adequately represent the status of a simulated pixel within the local moving window. Furthermore, it is important to select accurate similar neighbor pixels, because it could help to improve the spectral blending at the next steps for the image fusion.