Determining the driving climatic factors at critical periods and potential legacy effects is crucial for grassland productivity predictions on the Qinghai–Tibet Plateau (QTP). However, studies with limited and ex situ ground samples from highly heterogeneous alpine meadows brought great uncertainties. This study determined the key climatic factors at critical plant developmental stages and the impact of previous plant growth status for interannual aboveground net primary productivity (ANPP) variations in different QTP grassland types. We hypothesize that the impact of climatic factors on grassland productivity varies in different periods and different vegetation types, while its legacy effects are not great. Pixel-based partial least squares regression was used to associate interannual ANPP with precipitation and air temperature at different developmental stages and prior-year ANPP from 2000 to 2019 using remote sensing techniques. Results indicated different findings from previous studies. Precipitation at the reproductive stage (July–August) was the most prominent controlling factor for ANPP which was also significantly affected by precipitation and temperature at the withering (September–October) and dormant stage (November–February), respectively. The influence of precipitation was more significant in alpine meadows than in alpine steppes, while the differentiated responses to climatic factors were attributed to differences in water consumption at different developmental stages induced by leaf area changes, bud sprouting, growth, and protection from frost damage. The prior-year ANPP showed a non-significant impact on ANPP of current year, except for alpine steppes, and this impact was much less than that of current-year climatic factors, which may be attributed to the reduced annual ANPP variations related to the inter-annual carbon circulation of alpine perennial herbaceous plants and diverse root/shoot ratios in different vegetation types. These findings can assist in improving the interannual ANPP predictions on the QTP under global climate change.

Based on limited controlled experiments, both advanced and delayed shifts in flowering phenology induced by precipitation and snow cover have been reported on the Qinghai–Tibetan Plateau (QTP). To clarify the impact of precipitation and snow cover on flowering phenology, we conducted a comprehensive statistical analysis of the temporal change in flowering phenology and its responses to precipitation and snow cover changes using regression models built on the largest collection of ground phenological observation data on the QTP. We found that first flowering date (FFD) for the early-flowering time series significantly advanced at the rate of −0.371 ± 0.149 days/year (p < 0.001), whereas FFD mid-to-late-flowering time series showed no trend at the rate of 0.158 ± 0.193 days/year (p = 0.108). Cumulative pre-season precipitation regressed with FFD positively for early-flowering time series, with the explained variation ranging from 11.7 to 49.4% over different pre-season periods. The negative impact of precipitation on flowering phenology is unexpected, because an increase in precipitation should not hamper plant growth in the semi-arid and arid environments on the QTP. However, precipitation was found to be inversely correlated with temperature. Thus, it is likely that temperature, and not precipitation, regulated flowering phenology over the study period. No relationship was found between FFD and snow-cover melt date or duration. This result indicated that snow cover may not affect flowering phenology significantly, which may be because plant flowering time was much later than the snow-cover melt date on the QTP. These findings contrast the results of controlled experiments on the QTP, which showed that precipitation regulated flowering phenology, and with other studies that showed that snow-cover melting time determined flowering dates of early-flowering species in high latitude and Arctic zones in Europe and North America, where the low-temperature environment is similar to the QTP. These findings can improve flowering phenology models, assist in the prediction of phenological responses of herbaceous plants to climate change, and forecast changes in the structure and function of the grassland ecosystem on the QTP.

Remote sensing (RS) models can easily estimate the net primary productivity (NPP) on a large scale. The majority of RS models try to couple the effects of temperature, water, stand age, and CO₂ concentration to attenuate the maximum light use efficiency (LUE) in the NPP models. The water effect is considered the most unpredictable, significant, and challenging. Because the stomata of alpine plants are less sensitive to limiting water vapor loss, the typically employed atmospheric moisture deficit or canopy water content may be less sensitive in signaling water stress on plant photosynthesis. This study introduces a soil moisture (SM) content index and an alpine vegetation photosynthesis model (AVPM) to quantify the RS NPP for the alpine ecosystem over the Three-River Headwaters (TRH) region. The SM content index was based on the minimum relative humidity and maximum vapor pressure deficit during the noon, and the AVPM model was based on the framework of a moderate resolution imaging spectroradiometer NPP (MOD17) model. A case study was conducted in the TRH region, covering an area of approximately 36.3 × 10⁴ km². The results demonstrated that the AVPM NPP greatly outperformed the MOD17 and had superior accuracy. Compared with the MOD17, the average bias of the AVPM was −9.8 gCm⁻²yr⁻¹, which was reduced by 91.8%. The average mean absolute percent error was 57.0%, which was reduced by 68.2%. The average Pearson’s correlation coefficient was 0.4809, which was improved by 30.0%. The improvements in the NPP estimation were mainly attributed to the decreasing estimation of the water stress coefficient on the NPP, which was considered the higher constraint of water impact on plant photosynthesis. Therefore, the AVPM model is more accurate in estimating the NPP for the alpine ecosystem. This is of great significance for accurately assessing the vegetation growth of alpine ecosystems across the entire Qinghai–Tibet Plateau in the context of grassland degradation and black soil beach management.

The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPAR_FVC) based on Beer-Lambert’s law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert’s law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPAR_FVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPAR_LAI) and MODIS FPAR products (FAPAR_MOD). The mean absolute percentage error of FAPAR_FVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPAR_LAI and FAPAR_MOD, respectively. The mean percentage error of FAPAR_FVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPAR_LAI and FAPAR_MOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPAR_FVC became more pronounced with increasing FVC compared to FAPAR_LAI. The findings suggested that the FAPAR_FVC method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers.