## 5 Conclusions  
Modeling aboveground biomass is of great importance to studies of grassland ecology and socio-economic environments in grassland regions. In order to predict biomass for grasslands with high spatial resolutions and temporal frequencies, we developed an aboveground biomass estimation model integrating synthetic NDVI data and the support vector machine technique (SVM-AGB). Our modeling method showed that the synthetic images can guarantee the accuracy of AGB estimations compared with AGB estimates from MODIS data (MODIS data: \(R^{2}\) = 0.77 and RMSE = 17.22 g/m\({}^{2}\); synthetic imagery: \(R^{2}\) = 0.73 and RMSE = 30.61 g/m\({}^{2}\)). Importantly, the spatial-temporal resolution was improved (8 d, 30 m), which indicates that integrating the synthetic NDVI data and SVM model can produce accurate grassland AGB estimates for natural resource applications.  
Based on the improved grassland AGB estimation model and synthetic NDVI, we mapped time-series AGB for grasslands of Xilinhot, Inner Mongolia, at 8-d intervals and 30-m resolutions. We successfully combined the STARFM and SVM into grassland biomass estimations. Compared with the biomass model using single data sources, our SVM-AGB model not only guarantees the accuracy of biomass estimations and achieves higher temporal frequencies, but it also improves the spatial resolution (_i.e._, from 250 to 30 m). Thus, it can capture biomass anomalies at finer scales, which is very useful for monitoring grassland conditions and conducting disaster assessments (such as during droughts). The high temporal (8 d) and medium spatial resolution (30 m) AGB estimations should be particularly useful for dynamic monitoring of grassland AGB, animal  
Figure 9: The Standardized Anomalies Index (SAI) indicating biomass anomalies in Xilinhot during July 2007. The land cover map in Xilinhot in the left panel was adapted from the land cover data set provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn))). The unutilized lands represent lands that have not been used (including desert, Gobi region, saline, wetlands, bare soil).  
husbandry management, and general grassland management. Our approach can also be applied to grasslands in other geographical regions.  
This study was funded by the National Natural Science Foundation of China (NSFC) (Grant No. 41271372) and the National Key Technology R&D Program (Grant No. 2012 BAC16B01). The authors thank STARFM developers for sharing the code, [PERSON] for sharing field data for this study, and [PERSON] for giving important suggestion for this study. We thank the helpful comments on our manuscript from the anonymous reviewers and associate editors.  
[PERSON] contributed to the experiments of the research, analyzed the data, and wrote the majority of the paper. [PERSON] designed the original idea of the study, supervised and designed the research work, and contributed to the construction of the manuscript structure. [PERSON] made comparison of different biomass estimation models. [PERSON] contributed to data processing. [PERSON] and [PERSON] helped with discussion and revisions.  
The authors declare no conflict of interest.