## 4 Discussions  
### Potentials Analysis of Model  
A significant empirical relationship was found between the accumulated PSNnet data and the aboveground biomass data, showing good prospects for the application of MODIS productivity data in combination with ground sampling data to establish models for biomass inversion. There are three reasons to support this conclusion, as follows:  
(1) Through the accumulation of PSNnet data every eight days from the beginning of the growing season to the peak of the growing season, a good correlation was achieved between the obtained accumulated PSNnet data and the peak growing season NDVI data for the corresponding spatial point, showing coefficients of determination (\(R^{2}\)) up to 0.75 (Figure 7). Because there have been many previous studies on statistical models of the NDVI and aboveground biomass [35; 36; 37; 38], we can assume a good correlation between the PSNnet data and the aboveground biomass data. Furthermore, we can assume that using the PSNnet data and the aboveground biomass data to build the model and then retrieve the aboveground biomass data is a practical method. In addition, the MODIS productivity data used in this study fully considered the effects of temperature, precipitation and other climatic factors during the process of estimating vegetation productivity. Compared with the NDVI data employed in  
Figure 6: Interannual variations in the aboveground biomass in different grassland types.  
traditional methods of biomass estimation, MODIS productivity data can better reflect the effects of environmental stresses.  
(2) The temporal matching between remotely sensed images and ground survey greatly affects the accuracy of remote sensing based models for grassland biomass estimation. The database of the extensive filed samples and their matching remotely sensed data is the basis of improving the model precision and stability. In this study, a sound database combining multi-year accumulated PSNnet data and ground survey biomass data with strict temporal matching was developed, which was further applied to biomass estimation models.  
(3) Two methods are often used to evaluate model performances. One is based on the coefficient of determination (\(R^{2}\)), another way is to assess model error. In general, a high \(R^{2}\) or a low error value often indicates a good fit between the model developed and the ground survey data [39]. In this study, we compared the correlation between NDVI data and biomass, as well as accumulated PSNnet data and biomass. The result showed that \(R^{2}\) between calculated PSNnet data and biomass was a little higher than \(R^{2}\) between NDVI data and biomass, as shown in Figure 8. We used ground survey data from the Xilingol grassland for the years 2005-2012 and MODIS productivity data for the same time period to establish statistics-based models for biomass estimation, with an overall accuracy of 69%, which is close to highest accuracy (74%) by [PERSON], _et al_. [38]. In addition, NDVI data are prone to an \"oversaturation\" phenomenon if the vegetation coverage is higher, which decreases the sensitivity of biomass estimation, whereas MODIS productivity data can overcome this oversaturation problem. Therefore, in high vegetation cover conditions, the biomass estimation accuracy by MODIS productivity data would be higher than the biomass estimation accuracy by NDVI data.  
Figure 7: Relationship between the accumulated PSNnet data and NDVI data.