Spatiotemporal Dynamics of Aboveground Biomass and Its Influencing Factors in Xinjiang’s Desert Grasslands

Abstract

Grassland biomass is a significant parameter for measuring grassland productivity and the ability to sequester carbon. Estimating desert grassland biomass using the best remote sensing inversion model is essential for understanding grassland carbon stocks in arid and semi-arid regions. The present study constructed an optimal inversion model of desert grassland biomass based on actual biomass measurement data and various remote-sensing product data. This model was used to analyze the spatiotemporal variation in desert grassland biomass and climate factor correlation in Xinjiang from 2000 to 2019. The results showed that (1) among the established inversion models of desert grasslands aboveground biomass (AGB), the exponential function model with the normalized differential vegetation index (NDVI) as the independent variable was the best. Furthermore, (2) the NDVI of desert grasslands in Xinjiang showed a highly significant increasing trend from 2000 to 2019 with a spatially concentrated distribution in the north and a more dispersed distribution in the south. In addition, (3) the average AGB value was 52.35 g·m−2 in Xinjiang from 2000 to 2019 and showed a spatial distribution with low values in the southeast and high values in the northwest. Moreover, (4) the low fluctuation in the coefficient of desert grassland variation accounted for 65.26% of overall AGB fluctuation (<0.10) from 2000 to 2019. Desert grassland AGB in most areas (88.65%) showed a significant increase over the last 20 years. Lastly, (5) the correlation between desert grassland precipitation and AGB was stronger than that between temperature and AGB from 2000 to 2019. This study provides a scientific basis and technical support for grassland livestock management and carbon storage assessments in Xinjiang.