## 1 Introduction  
Grassland ecosystems are one of the most important types of terrestrial ecosystems on the planet. They provide the ecosystem functions of soil and water conservation, wind erosion prevention, sand fixation and air purification. They also play an important role in the global terrestrial circulation among ecosystems. In addition, grassland ecosystems are important for \"livestock production\". China's grasslands encompass an area of approximately 400 million hectares, accounting for 41.7% of the country's total land area [1], and constituting the single largest type of terrestrial ecosystem in China. Grassland biomass is the most direct indicator of grassland's ecological status [2]. Thus, a precise and rapid method for the estimation of grassland biomass is of great importance for both basic science and the management and protection of grasslands. Grassland biomass directly reflects the level of grassland productivity. Productivity refers to the amount of organic matter produced by autotrophic organisms in a given unit of area over a given unit of time. Grassland net primary productivity (NPP) is obtained by subtracting autotrophic respiration [3] from the total amount of organic matter fixed by grassland vegetation. Grassland biomass is defined as the yield of fresh grass or hay that can be harvested at a certain time, and is close to the net primary productivity without regard to disturbances. Productivity is the basis for the formation of biomass, and biomass is the manifestation of productivity. The currently available methods for the estimation of grassland productivity and biomass primarily include field surveys, statistical modeling [4, 5, 6, 7], process modeling [8, 9, 10, 11] and parameter modeling [12, 13, 14, 15, 16]. With the development of remote sensing technology, more and more fields have begun to involve remote sensing technology [17, 18], and the development of remote sensing technology has stimulated studies on vegetation productivity and biomass. Due to the simple calculations involved and the high accuracy of the approach, statistical regression models using remote sensing data have been widely applied for the estimation of grassland biomass. The essence of the method is the establishment of a regression model between biomass measurements and either single-band remote sensing or vegetation index data, using satellite remote sensing data as the input parameters to estimate biomass. Initially, as the use of single-band information was simple, data of this type were used for estimating grassland biomass. However, due to strong interference from a number of factors (e.g., air, soil, sensor performance and the angle of the sun), the resulting estimation accuracy was found to be poor [19]. As such, the vegetation index is now commonly used as the form of input data for building empirical regression models of vegetation biomass. [PERSON], _et al._[20] used the normalized difference vegetation index (NDVI) calculated by NOAA/AVHRR to establish a grassland vegetation biomass estimation model for China, and used the model to study the spatial distribution characteristics of China's grassland vegetation biomass. Advances in remote sensing technology have generated higher-resolution images that are now used to estimate grassland productivity and biomass through methods, such as the application of MODIS data. [PERSON], _et al._[21] performed a systematic estimation of China's grassland productivity by region using a combination of MODIS data and ground survey data for the same time period. For different types of grassland areas, they established relational models between NDVI and field survey biomass data that allowed them to estimate the distribution of grass production in China. [PERSON], _et al._[22] employed enhanced vegetation index (EVI) data from MODIS to estimate aboveground biomass in Tibet and analyze the relationship between the grassland's aboveground biomass and climatic factors.