## 1 Introduction  
Grassland, defined as permanent vegetation of herbaceous plant communities, provides significant ecosystem services, carbon pooling, and forage production [1; 2; 3]. Aboveground biomass (AGB), which is defined as the total mass of plant material per unit area, is an important indicator of vegetation production. The estimation of grassland AGB is therefore crucial for evaluating livestock carrying capacity, understanding the response and feedback to climate change, and achieving sustainable development [4; 5]. The traditional method to estimate grassland AGB is based on field measurement, consisting of field clippings, laboratory drying, and weighing [4]. Although the field measurement methods are accurate, they are time-consuming and labor-intensive. In addition, their spatially sparse nature makes them unfeasible to give a comprehensive understanding at regional or larger scales [6]. Optical remote sensing data contain valuable information on vegetation parameters, and therefore provide an alternative to monitor grassland AGB more handily and in a seamless manner [7]. Various methods have been developed for estimating grassland AGB from optical remote sensing data, and most of them are by nature empirical, and based on transfer functions between AGB and remote sensing observation [1; 8; 9; 10; 11]. Transfer functions can be parametric (e.g., linear [2; 12], exponential [8], and power fitting [5]) or non-parametric (e.g., support vector machine [13] and artificial neural network [14]). Field measurements are also indispensable in these empirical methods to calibrate the transfer functions. Limited by the representativeness of the field measurements, the resulting transfer function is often time-, site-, and sensor-specific. Therefore, corresponding field measurements should be implemented beforehand, and these empirical methods are also time consuming and labor intensive. Canopy radiative transfer models summarize our understanding regarding the photon-canopy interaction and establish the explicit physical relation between vegetation parameters and remote sensing observations [15; 16; 17]. The robustness and transferability of radiative transfer models makes them a preferable solution to the abovementioned condition-specific problem. Physically-based methods by inverting canopy radiative transfer models have been extensively used to retrieve leaf area index, fraction of absorbed photosynthetic active radiation, fraction of green vegetation cover, and many other parameters [18; 19]. However, the physically-based method to retrieve AGB is still at its infancy. One major reason is the lack of a proper radiative transfer model to account for the pool of canopy dry matter constituting the AGB. For example, the primary pool of forest AGB is the tree trunk, yet there is no single radiative transfer model accounting for its dry weight. However, there is no such predicament for grassland considering its herbaceous nature. The dry matter of grassland is mostly from the leaves (i.e., leaf dry matter content, Cm), and significantly determining the leaf spectrum, which can be described by leaf optical models (e.g., the PROSPECT model [20]). In addition, the amount of the leaves can also be depicted by leaf area index, which is an important input of many canopy scale models, such as SAILH [21]. Therefore, a physically-based model to retrieve grassland AGB is possible, given the fact that grassland AGB is the product of leaf dry matter content and leaf area index (AGB = Cm \(\times\) LAI). Very few pioneers of physically-based methods for grassland AGB found are in literature, including [PERSON]'s [22] and [PERSON]'s [23] study. They were both based on PROSAIL model [20; 21], which achieved an appropriate compromise between simulating accuracy and inverting simplification. [PERSON] et al. [22] firstly retrieved LAI by inverting PROSAIL model, then converted the LAI into AGB through a constant value of Cm estimated through field measurement.