###### Abstract  
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 CO\({}_{2}\) 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 \(36.3\times 10^{4}\) km\({}^{2}\). 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 \(-9.8\,\mathrm{gCm^{-2}yr^{-1}}\), 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. remote sensing model; net primary productivity; maximum light use efficiency; soil moisture limitation; Three-River Headwaters region +
Footnote †: journal: _agronomy_  
Satellite data provide remote sensing (RS) models with abundant information on vegetation patterns and activities at high resolution, enabling easy NPP estimation on a large scale [8; 9; 10; 11; 12; 13]. Most RS models attempt to couple various effects (temperature, water, stand age, CO\({}_{2}\) concentration, etc.) as constraints to attenuate the potential light use efficiency (LUE) to the actual LUE [10; 14; 15; 16]. Meanwhile, water stress is considered the most uncertain, important, and challenging [17; 18; 19], and numerous methods have been defined to describe it. According to the pathway of water effect on vegetation photosynthesis, these water stress methods can be grouped into the following three classes: (1) on the stomatal conductance, e.g., the vapor pressure deficit (VPD) in the two-leaf light use efficiency model and the MODerate resolution imaging spectroradiometer NPP (MOD17) model [10; 20]; (2) on the canopy water content, e.g., the land surface water index (LSWI) in the vegetation photosynthesis model, the vegetation photosynthesis and respiration model, and the photosynthetic capacity model [15; 21; 22]; (3) on the soil moisture (SM) content, e.g., the RS soil moisture in the Carnegie-Ames-Stanford approach and the physiological processes predicting growth model [23; 24]. The VPD indicates the impact of stomatal conductance on plant photosynthesis. However, in alpine ecosystems, stomata are less sensitive to limiting water vapor loss [25; 26; 27]. Water deficiency at low altitudes causes plants to close their stomata, while at high altitudes this phenomenon is not observed due to a greater relationship with net radiation [25; 26; 27].