## 1 Introduction  
Grasslands are of considerable global importance because they are important sites for biodiversity and serve as energy suppliers for mankind (_i.e._, production of agricultural commoditiessuch as hay and livestock); grassland conservation is a particular concern in China because of land use changes due to climate variability and the country's continued population growth [1]. Dynamic pasture growth information is of great significance for pasture capacity enhancements, the development of appropriate management strategies and rotational grazing plans, regional livestock production, and scientific evaluations of ecological benefits. In grassland ecosystems, the amount of biomass represents primary production and determines the herbivore carrying capacity [2]. It can also reflect the health status of grassland ecosystems. Therefore, accurate estimates of grassland biomass are critical for their management. Field surveys are the most reliable method to obtain accurate grassland biomass data, but these surveys are extremely time-consuming and labor-intensive over large areas, especially in remote regions. Satellite platforms offer an effective way to collect data over large areas [3]. The normalized difference vegetation index (NDVI) is the most widely known vegetation index that can be computed from remotely sensed data (e.g., Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) data), and the NDVI has been shown to be a sufficiently stable indicator for monitoring the intra- and inter-annual variations of vegetation greenness [4]. However, technical and financial constraints often limit the remote sensing instruments' ability to acquire data with high spatial and temporal resolutions simultaneously [5]. For example, remotely sensed images acquired from Landsat satellites have a spatial resolution of 30 m for multispectral bands. However, its long revisit cycle of 16 d, frequent cloud contamination, and other poor atmospheric conditions that are often encountered [6; 7; 8] limit the uses of Landsat data. In contrast, MODIS data can provide highly frequent (daily) observations. However, their coarse spatial resolutions ranging from 250 to 1000 m restrict their applications in relatively small and heterogeneous landscapes. Thus, integrating information from different remote sensing sensors with image fusion techniques could be a feasible and inexpensive way to better capture land surface characteristics [9]. Traditional image fusion methods such as intensity-hue-saturation (IHS) transformation [10], principal component analysis [11], and wavelet decomposition [12] are not effective for generating synthetic observations with enhanced spatiotemporal resolution [13; 12]. While these traditional methods can be used to combine high spatial resolution panchromatic images and spectral information for coarse pixel images, they cannot generate time series of reflectance with both high spatial and temporal information [14]. Thus, the panchromatic images can only help to enhance the spatial resolution [15]. In order to generate reflectance data with both high spatial and temporal resolution, [PERSON] _et al._[14] developed a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) for the prediction of daily surface reflectance through blending Landsat and MODIS data. The STARFM algorithm has been applied successfully in several studies involved with the monitoring of seasonal changes in surface features [16; 17; 18; 19], improving the accuracy of classifications [20], and evaluating gross primary productivity (GPP) [21]. Through adopting the synthetic reflectance series generated by STARFM, [PERSON] [22] found that the GPP derived from synthetic reflectance data could effectively capture the green-up and leaf down dates of croplands over the growing season. [PERSON] _et al._[23] fused MODIS and Landsat data through the use of STARFM and generated synthetic surface reflectance data to replace low-quality Landsat thematic mapper (TM) images contaminated by clouds.