The Hindu Kush Himalayan (HKH) region is one of the most ecologically vulnerable regions in the world. Several studies have been conducted on the dynamic changes of grassland in the HKH region, but few have considered grassland net ecosystem productivity (NEP). In this study, we quantitatively analyzed the temporal and spatial changes of NEP magnitude and the influence of climate factors on the HKH region from 2001 to 2018. The NEP magnitude was obtained by calculating the difference between the net primary production (NPP) estimated by the Carnegie–Ames Stanford Approach (CASA) model and the heterotrophic respiration (Rh) estimated by the geostatistical model. The results showed that the grassland ecosystem in the HKH region exhibited weak net carbon uptake with NEP values of 42.03 gC∙m⁻²∙yr⁻¹, and the total net carbon sequestration was 0.077 Pg C. The distribution of NEP gradually increased from west to east, and in the Qinghai–Tibet Plateau, it gradually increased from northwest to southeast. The grassland carbon sources and sinks differed at different altitudes. The grassland was a carbon sink at 3000–5000 m, while grasslands below 3000 m and above 5000 m were carbon sources. Grassland NEP exhibited the strongest correlation with precipitation, and it had a lagging effect on precipitation. The correlation between NEP and the precipitation of the previous year was stronger than that of the current year. NEP was negatively correlated with temperature but not with solar radiation. The study of the temporal and spatial dynamics of NEP in the HKH region can provide a theoretical basis to help herders balance grazing and forage.

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors.