## 5 Discussion  
Fine spatio-temporal resolution images can typically provide detailed land surface dynamics which the coarse spatial resolution images cannot supply. Generating the consistent and comparative fine spatio-temporal imagery product is therefore critical for deepening understanding of earth surface processes. Image compositing techniques that select the most suitable pixel from multi-temporal image acquisitions offer great potential to generate such kinds of products. This paper describes new dense time series products formed by compositing the HJ-1A/B observations. While there have been some studies exploring the use of Landsat TM/ETM+ images for generating the time series products on a 30-m spatial scale [11; 15; 19; 21], the proposed composited product in this paper reflected that a higher temporal resolution composited product can be obtained by using 2-day repeat coverage data. The compositing approach includes some key issues that are worth attention. Figure 11: Scatter plot for the comparable samples in the year 2010 with HJ-1A/B 16-day composited TOA-NDVI as x-axis and MODIS 16-day NDVI (MOD13A1) as y-axis. The compositing approach took advantage of the automatic pre-processing methods for geometric registration and cloud masking [26]. These methods substantially improved the feasibility of producing HJ-1A/B composites (8-day, 16-day and monthly). The pre-processing methods can easily be implemented on a parallel processor. Nevertheless, since there were some error sources in the pre-processing procedures, a fully automated processing scheme that can provide processing and updating composites in near-real time is needed in future studies. Besides, the cloud shadow detection is more difficult than detecting cloud. There might be some uncertainties about cloud shadow accuracy because of the lack of a thermal-infrared band of HJ-1A/B to determine the cloud height [52]. Although the CV-MVC method is helpful to exclude cloud shadow to some extent, the undetected cloud shadow might be a source of error for the composites. In this study, the CV-MVC method was used to select the optimum vegetation pixel from available observations. However, for the none-vegetation pixels such as water, peat lands or sand lands, the NDVI profile of these pixels do not demonstrate obvious phenology variation trends. Therefore, as shown in Figure 6, the DOY of these none-vegetation areas showed a different trend compared with vegetation pixels in both HJ and WELD. Besides, The CV-MVC method was helpful to exclude some undetected optically thick clouds over vegetation areas. However, for the thin cirrus cloud contaminated areas, the rejection of cloud pixels for the compositing method might not work well because NDVI is less sensitive to those thin cirrus clouds [53]. Besides, since the real earth surface is non-Lambertian, the anisotropy of surface reflectance is an important attribute in satellite observations [13]. The CV-MVC method was also helpful to minimize the bi-directional reflectance effects (BRDF) to some extent [42]. Generally, normalizing the directional reflectance to their nadir-equivalent band reflectance values can increase the radiometric consistency of different dates and lead to the sufficient use of all valid observations [15]. However, in the case of image composition with fine or high spatial resolution, it was not practical to carry out directional reflectance normalization because of the relatively long revisit frequency or narrow FOV. In future studies, the BRDF normalization will be further considered in the HJ composites. BRDF calibration using multi-source ancillary information (such as BRDF parameters from MODIS products) [54, 55, 56] or deriving BRDF parameters from monthly HJ-1A/B images may be a feasible development. Two undesired results concerning the residual clouds and the unsmooth transition should be noted in the composites. For the residual clouds, those pixels were mainly dependent on the available valid observations.