###### Abstract  
Aboveground biomass (AGB) is a key indicator of the physiological status and productivity of grasslands, and its accurate estimation is essential for understanding regional carbon cycles. In this study, we developed a suitable AGB model for grasslands in Xinjiang based on the random forest algorithm, using AGB observation data, remote sensing vegetation indices, and meteorological data. We estimated the grassland AGB from 2000 to 2022, analyzed its spatiotemporal changes, and explored its response to climatic factors. The results showed that (1) the model was reliable (R\({}^{2}\) = 0.55, RMSE = 64.33 g\(\cdot\)m\({}^{-2}\)) and accurately estimated the AGB of grassland in Xinjiang; (2) the spatial distribution of grassland AGB in Xinjiang showed high levels in the northwest and low values in the southeast. AGB showed a growing trend in most areas, with a share of 61.19%. Among these areas, lowland meadows showed the fastest growth, with an average annual increment of 0.65 g\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\); and (3) Xinjiang's climate exhibited characteristics of warm humidification, and grassland AGB showed a higher correlation with precipitation than temperature. Developing remote sensing models based on random forest algorithms proves an effective approach for estimating AGB, providing fundamental data for maintaining the balance between grass and livestock and for the sustainable use and conservation of grassland resources in Xinjiang, China. random forest; grassland type; aboveground biomass; climatic factors +
Footnote †: journal: plants  
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warming and environmental degradation is crucial for protecting and improving natural grasslands in Xinjiang [10; 11]. The estimation of grassland biomass can be improved using various methods. Traditional harvesting methods with low estimation accuracy are time-consuming, making it challenging to predict biomass on a large scale. Therefore, remote sensing data with high spatial and temporal resolutions can improve the inversion accuracy of models [12; 13]. High-resolution SPOT and Landsat satellite data are suitable for small-scale estimations [14; 15]. There are many options for the spatiotemporal resolution of MODIS remote sensing data that can be used to estimate grassland AGB in different regions [16; 17; 18]. Additionally, AGB estimation is mostly based on the linear regression of univariate vegetation indices [19; 20], and there is some level of uncertainty in the estimation using a single factor. The estimation of AGB is also influenced by various factors such as climate change, human activities, and geography. However, most studies have not fully considered the effects of different variables [21]. Studies have shown that the warming and humidification trends caused by climate change make the ecological environment more suitable for promoting the growth of local grassland biomass [22]. Temperature changes affect various aspects, such as the start date and duration of grass-growing seasons, whereas precipitation impacts vegetation cover and grassland biomass [23]. In recent years, the use of remote sensing data to upgrade measurements to the regional scale and apply machine learning methods for AGB estimation has become popular [24]. Machine learning algorithms can handle nonlinear relationships and complex data structures and can automatically select feature variables and optimize parameters to improve the accuracy and generalization of grassland biomass prediction [25]. The commonly used machine learning algorithms for estimating aboveground biomass include random forest, support vector machine, neural networks, and regression analysis, among others. These algorithms are robust and enhance the generalizability of the grassland AGB model compared to traditional linear regression. For instance, the AGB model based on the random forest algorithm developed by [PERSON] et al. using MODIS VIs, topographic, and climatic factors outperformed the single NDVI regression model for grassland AGB estimation on the Qinghai-Tibet Plateau [26].