## 1 Introduction  
Dense time series (e.g., annual, monthly, weekly) satellite datasets are crucial for monitoring land surface dynamics (e.g., intra-seasonal ecosystem variations). Monthly or sub-monthly satellite datasets have been developed at moderate (>250 m) or coarser spatial resolutions [1; 2]. However, pixels in these coarse resolution images are often a mixture of various land cover types [3; 4]. Finerspatial resolutions (e.g., 30 m) and dense time series satellite datasets are urgently required to provide adequate spatial details for many surface phenomena [5; 6; 7; 8; 9; 10]. Temporal composition methods can select the optimum observation over the same geographical region from multi-temporal images in a certain time window based on different user-defined rules [11; 12; 13; 14]. In recent years, the temporal composition methods are gradually adopted for the composition of fine spatial resolution images to generate cloud-free time series datasets [11; 15; 16; 17; 18; 19]. The Web-Enabled Landsat Data (WELD) project provides a good framework for producing composites at 30-m spatial resolution and monthly, seasonal, annual time intervals using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data [15]. The WELD data have been successfully employed in many studies, such as the crop field extraction and land cover change monitoring for the conterminous United States (CONUS) [20; 21; 22]. Based on the success of the CONUS WELD products, the WELD processing is being expanded to the global scale to provide the monthly and annual Landsat 30 m information for six three-year epochs spaced every five years from 1985-2010 [18]. Similar Landsat-based compositing has also been carried out across the entire Carpathians and Canada [11; 19]. However, because of the frequent cloud contamination and the poor atmospheric conditions, the comparatively infrequent 16-day repeat of the current Landsat system does not provide adequate observations for producing gap free clear view composites at monthly or sub-monthly intervals. Satellites or constellations with short revisit periods (e.g., 2-4 days) and fine spatial resolution sensors, which were already on orbit (e.g., HJ-1A/B, Sentinel-2A) or will be launched (e.g., Sentinel-2B), create new opportunities to generate the composites at fine spatial resolution and dense time series. A pair of Chinese HJ-1 (Huanjing (HJ)), which means environment in Chinese) optical satellites, launched in 2008 and named HJ-1A and HJ-1B, provides 30-m observations every two days [23; 24; 25]. The HJ-1A and HJ-1B are distributed with a 180 degrees phase difference in the same orbital plane to enhance the temporal resolution. The revisit period of a single satellite in the HJ]1-A/B constellation is four days, and therefore two optical satellites can obtain the global observation data within two days [26; 27]. Such a high observation frequency allows generation of 30-m composites at monthly or even weekly intervals. However, compositing using standard HJ imagery is infeasible because they are not orthorectified and hence have considerable geolocation errors. Cloud masking can also be challenging due to lack of thermal band and shortwave infrared band. The authors addressed both issues in previous studies where automated approaches were developed for orthorectifying HJ images to achieve sub-pixel geolocation accuracies and for masking cloud in those images [26; 28]. Based on those approaches, this study demonstrates the feasibility of using HJ images to create composites in the Zoige plateau in China, and provide a comprehensive assessment of the quality of those composites. The rest of the paper is structured as follows: Section 2 describes the study area, HJ-1A/B satellites, sensors and data used for the compositing. Section 3 presents the preprocessing, compositing and quality assessment methods for the original data and composite results. Section 4 shows the results of compositing and evaluation by comparison with other sensors.