Grassland canopy cover and aboveground biomass in Mongolia and Inner Mongolia: Spatiotemporal estimates and controlling factors

Abstract Temperate and semiarid grasslands comprise 80% of the land area on the Mongolian Plateau and environs, which includes Mongolia (MG), and the province of Inner Mongolia (IM), China. Substantial land cover/use change in the last few decades, driven by a combination of post-liberalization socioeconomic changes and extreme climatic events, has degraded these water-limited grassland's structure and function. Hence, a precise estimation of canopy cover (CC, %) and aboveground biomass (AGB, g m−2) is needed. In this study, we analyzed >1000 field observations with sampling during June, July and August (JJA) in 2006, 2007, 2010 and 2016 in IM and 2010–2012 and 2014–2016 in MG. The field sampling was stratified by the dominant vegetation types on the plateau, including the meadow steppe, the typical steppe, and the desert steppe. Here we used Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance and vegetation indices optimized for low cover conditions to develop and test predictive models of CC and AGB using observed samples as training and validation data through rule-based regression tree models. We then used the predictive models to estimate spatially-explicit CC and AGB for the plateau over the last decade (2000–2016). Our study demonstrated the effectiveness of our predictive models in up-scaling ground observations to the regional scale across steppe types. Our results showed that model R2 and RMSE for CC and AGB were 0.74 (13.1%) and 0.62 (85.9 g m−2), respectively. The validation R2 and RMSE for CC and AGB were 0.67 (14.4%) and 0.68 (76.9 g m−2), respectively. The mean ± SD for CC and AGB were 24.9 ± 23.4% and 155.2 ± 115.2 g m−2, respectively. We also found that our scaled up estimates were significantly related to inter-annual climatic variability and anthropogenic drivers especially distance to urban/built-up areas and livestock density. In addition to their direct use in quantifying the spatiotemporal changes in the terrestrial carbon budget, results from these predictive models can help decision makers and rangeland managers plan sustainable livestock practices in the future.