## 1 Introduction
Consequently, they acquired a long 5-yr time series at Landsat resolution. They found that this Landsat image series, which had resolutions of 16 d and 30 m, could be effectively used to monitor vegetation phenology in broadleaf forest environments. [PERSON] _et al._[24] performed land cover classifications by detecting the phenology patterns of different vegetation with the synthetic NDVI series and found that synthetic images could be used to compensate for missing Landsat data and increase the classification accuracy compared to that with the use of single-date Landsat data. Based on the design of STARFM, several new methods and modifications have been developed, including the sparse-representational-based spatiotemporal reflectance fusion model (SPSTFM) [25], enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) [15], spatial temporal adaptive algorithm for mapping reflectance change (STAARCH) [26], spatial and temporal adaptive reflectance fusion model (ESTARFM) [26]. temporal reflectance unmixing model (STRUM) [27], and other fusion algorithms [13; 28; 29; 30]. Most of these methods need more than one image pair as inputs. However, it is very hard to acquire many scenes of TM data without cloud contamination. The STARFM is the most widely used method and can work with a single pair of fine and coarse images. Moreover, the STARFM has achieved satisfactory results in many studies [18; 22; 23; 31; 32], which have demonstrated its practicability. Various methods have been applied with remotely sensed data for estimating grassland biomass, and these methods include multiple regression analysis [33], K-nearest neighbor [34], artificial neural network (ANN) [35], and support vector machine (SVM) techniques. Artificial neural network, because of its ability to handle complex nonlinear functions, has been successfully used in various land observation applications including biomass estimations [36; 37; 35], land classifications [38; 39], and land change detection [40; 41]. However, the ANN model would not produce guaranteed accuracy when there are not enough samples and it is easy to over-fit the data when there are excessive numbers of samples [42]. Support vector machine is based on the structural risk minimization principle and isgood at solving practical problems involving small numbers of training samples, nonlinearity, high numbers of dimensions, and local minima; thus, SVM is considered to be a good alternative to ANN. Several studies have demonstrated that biomass estimation based on a SVM model can provide robust results [43; 44; 45]. The objective of this study was to accurately estimate aboveground biomass (AGB) for grasslands in arid and semi-arid regions of Inner Mongolia, China. To achieve this goal, we first derived synthetic NDVI time series with high temporal and spatial resolution by blending the high-temporal MODIS data and the medium-spatial Landsat data with a data-fusion approach. We then developed different biomass models and integrated the synthetic NDVI products to acquire the optimal grassland AGB estimation model for accurate estimates of AGB (8-d intervals and 30-m resolutions). To further explore the practicability of our model, we calculated biomass anomalies with the optimal grassland AGB estimation model and further compared the results between the synthetic NDVI-derived biomass and the MODIS NDVI-derived biomass data. Our approach detailed in this study will be useful for near real-time and accurate (high spatial and temporal resolutions) monitoring of grasslands conditions. Such data have the potential to help grassland managers utilize grassland resources more effectively.