## 5 Discussion
Some researchers attempted to use the average BRDF shape function for the whole study area when conducting topographic correction, and meanwhile, pointed out the necessity of employing different BRDF shape functions for different land cover types [20]. Model improved in this paper divides the land targets into eleven groups with NDVI thresholds, and uses different BRDF model parameters to perform topographic correction for different group of targets, which helps to account for the surface anisotropy more effectively [65]. It can be seen from Table 1 that the BRDF shape function parameters for different NDVI intervals of each band have apparent differences. Since NDVI is the ratio of simple band by algebraic operation, it is capable of restraining topographic effect compared to single band [47]. Moreover, NDVI can approximately indicate land cover types, and is readily available compared to other ancillary land cover data. These merits make model improved in this paper widely effective, practicable and potentially operational. Nevertheless, it is noteworthy that NDVI alone cannot distinguish dense vegetation types or non-vegetated areas. This is a big limitation our model encounters, and further thoughts are required to overcome it. ASTER GDEM (version 2) is employed to depict the rugged land surface and derive terrain factors, like slope and aspect, which serve as crucial input parameters for topographic correction. ASTER GDEM is produced with optical stereo pixel pairs, and it is prone to being impacted by cloud and mist (especially in mountainous areas), though version 2 improves on quality against version 1. [PERSON] _et al._ (2013) concluded that evident noise exists in ASTER GDEM for some local mountainous regions, though it is more capable of revealing details than SRTM [67]. [PERSON] _et al._ (2014) reported that the overall horizontal accuracy of ASTER GDEM is better than that of SRTM in eastern Qinghai-Tibetan Plateau, but the opposite is the case for the overall vertical accuracy [25]. Since accuracy of DEM has a significant influence on topographic correction effect, some researchers have explored and given suggestions on what resolution of DEM is appropriate for topographic correction. [PERSON] _et al._ (2009) claimed that the same resolution is enough to achieve satisfactory correction results [53]. [PERSON] _et al._ (2011) suggested that resolution of DEM used should be one third of that of images to be corrected [14]. [PERSON] _et al._ (2015) even concluded that 30-m DEM can only satisfy the correction of 90- to 500-m resolution remote sensing images [68]; this question still needs further and systematic investigations. Apart from that, local artifacts or noises in DEM also impact the correction effect, and this is expected to be suppressed by filtering of DEM prior to topographic correction. Nevertheless, it should be noted that DEM filtering modifies the original values and may potentially reduce the quantitative accuracy of remote sensing. In the procedure of land surface remote sensing, the signals received by the satellite sensors are coupled and integrated results from atmosphere and land surface [38]. And this is especially true for the mountainous areas where the interactions between signals and land surface-atmosphere system are much more complicated. Therefore, the combined atmospheric and topographic correction methodologies are adopted by many researchers to remove the radiometric distortions of optical satellite images in mountain areas [5, 18-20]. [PERSON] _et al._ (2013, 2014) showed that coupled correction methods showed most efficiency on weakly illuminated slopes [30, 69]. [PERSON] _et al._ (2012) also pointed out the strengths of combined methodology [20]. In this study, the improved model also corrects atmospheric and topographic effects simultaneously. However, we mainly modify the topographic correction module, and aim to see whether it works to improve the overall correction results.