###### Abstract
australis_ in different case studies [7; 16]. For instance, [PERSON] et al. [7] adopted the model to examine the meteorological effects on the growth trend of _P. australis_ in the Scheldt estuary, and [PERSON] et al. [16] applied the model to simulate the biomass and carbon sink of _P. australis_ in a Mediterranean eutrophic lake. In addition, as the impact of environmental factors (e.g., latitude and nutrient availability) on the plant growth traits, including phenological points and biomass, was parameterized in the growth dynamics models [13; 17; 18], the response of plant biomass and carbon sequestration capacity to the variation in environmental factors can be further simulated, which is beneficial for carbon management under environmental changes [17]. It is worth noting that, despite the versatility and flexibility of the plant growth dynamics models, their development and application still rely on the availability of field biomass data for the necessary model calibration and validation [19]. Remote sensing data, such as optical images, light detection and ranging (LiDAR) point cloud data, and synthetic aperture radar (SAR) data, have been widely used to quantify plant above-ground biomass (AGB) [20; 21; 22; 23]. Based on the relationship between remote sensing data (e.g., reflectance, vegetation indices) and the field-collected actual biomass, remote sensing technology can help estimate AGB rapidly and accurately [23; 24]. It is particularly helpful in the context of wetlands due to their typically remote and inaccessible locations and expansive area [25]. For example, [PERSON] et al. [26] proved the ability of hyperspectral compact airborne spectrographic imager (CASI) data for retrieving the AGB of _P. australis_ in the Zhangye National Wetland Park, China, and [PERSON] et al. [27] established the empirical model for retrieving the AGB of _P. australis_ in Dafeng, China. Therefore, plant biomass retrieved by remote sensing can be used as a potential data source for calibrating and validating plant growth dynamics models. Located in the northeast of Beijing Municipality, the Hanshiqiao Wetland Nature Reserve (HWNR) is a typical marsh and the largest _P. australis_ wetland in the city. With a core area mainly occupied by _P. australis_[28], the HWNR provides important ecological services for the city, such as biodiversity conservation [29], eutrophication regulation [30], and air purification [31]. As the capital and a major metropolitan city in China, Beijing is facing growing pressure for mitigating carbon emission, and how to achieve its goal of carbon neutrality has become a critical issue [32]. Therefore, an understanding of the growth trend and carbon sequestration capacity of _P. australis_ in the HWNR to help assess its potential use as a major carbon sink for the city can effectively support the carbon management of the city. To this end, we adapted the growth dynamics model of _P. australis_ originally developed by [PERSON] and [PERSON] [14] to estimate the biomass (including the AGB and below-ground biomass (BGB)) and carbon sequestration capacity of _P. australis_ in the core area of the HWNR in this study. Hyperspectral remote sensing images, together with an empirical retrieval equation proposed by [PERSON] et al. [33] for the HWNR, were used to obtain the plant biomass data for model calibration. The temporal variation in biomass and carbon sequestration capacity of _P. australis_ throughout its growth cycle in the HWNR were simulated and analyzed, and the effects of the key environmental factors on the phenological points and maximum AGB were further discussed. The specific objectives of this study were as follows: (1) to establish a growth dynamics model that is calibrated against the hyperspectral image data to estimate the AGB and BGB of _P. australis_ in the HWNR; (2) to illustrate the variation trend throughout the growth cycle of _P. australis_ in the HWNR; and (3) to analyze the influence of environmental factors on the biomass and carbon sequestration capacity of _P.