Hyperspectral technology has received great attention for estimating aboveground plant biomass. Corrections using ground spectrometer data have been proposed for more accurate integrated assessments of aboveground plant biomass. In this study, we sampled the aboveground plant biomass and the corresponding hyperspectral data from three types of alpine grassland ecosystems (alpine desert, alpine steppe, and alpine meadow) from the Qinghai-Tibetan Plateau to improve the biomass estimation accuracy. We used partial least squares regression and the vegetation indices (VIs) (using all two-band combinations (601*601combinations in all) involving 601 narrow bands between 400nm and 1000nm), and the optimal band combinations were used to calculate four most commonly used VIs. We found that the VIs are better than the least square method for alpine meadow, and that the most effective combinations were shown in the combinations of reflectance at the red edge (rather than red) and near-infrared bands(728nm,764nm). For alpine steppe and alpine desert, however, partial least squares regression is better than VIs. While the most important band for alpine steppe is between 600nm and 700nm in the red spectrum, the most important band for alpine desert is around 400nm. Our results can provide a theoretical basis for accurately estimating the aboveground plant biomass of different grassland types in the Qinghai Tibet Plateau.

Understanding the effects of climate change on plant phenological dynamics and growth patterns is critical for predicting climatic changes on the Qinghai-Tibetan Plateau (QTP). We used data over 21 years (1997 to 2017) for four dominant species on the QTP, namely Astragalus laxmannii (legume), Artemisia scoparia (forb), Kobresia humilis (sedge), Stipa purpurea (grass), and examined the relationships among climatic changes, plant phenology, growth pattern, and biomass. Most phenological periods in Stipa purpurea and Artemisia scoparia were delayed, whereas in Astragalus laxmannii, they were advanced. Soil temperature and maximum air temperature were the most important drivers. There were trade-offs between reproductive phenology and vegetative phenology, as well as between the length of the rapid growth period and the intrinsic growth rate. The impacts of the phenological or growth processes were species-specific. Our findings provide evidence of long-term changes and are of great significance for improving the accuracy of models.