Temperature and precipitation are important abiotic factors affecting net primary productivity (NPP) in grassland ecosystems. However, findings on how elevation influences the effects of these factors on NPP in alpine grasslands are not yet consistent. In addition, the impact of varied patterns of climate change on NPP sensitivity with elevation remain unclear. Therefore, alpine grassland on the Tibetan Plateau (TP) was selected to profile the spatial and temporal patterns of NPP from 2001 to 2022, and subsequently to reveal the effects of temperature and precipitation on the sensitivity of NPP with altitudinal gradient. The results showed that (1) 91% of the TP grassland experienced positive NPP trends, and the NPP trends followed a unimodal curve with elevation, with the largest mean value at 2500 m; (2) a positive correlation between precipitation and NPP dominated the grassland NPP up to an elevation of 3400 m, and a positive correlation between temperature and NPP dominated the grassland NPP above an elevation of 3400 m; (3) temperature, precipitation, and their interaction explained, on average, 21% of the temporal variation in the NPP of TP grassland, and the explanatory capacity decreased significantly with elevation; and (4) elevation, temperature, and precipitation variations together explained 35% of the NPP sensitivity of the TP grasslands. This study reveals the altitudinal characteristics of NPP in grasslands affected by climate, and reminds us to take elevation into account when carrying out grassland management.

Ecosystem trait is a standardized description of biological features of a community, and it bridges individual plants and ecosystem. Conventionally most ecosystem trait data are collected from field survey and the generated data is hard to meet the requirements as set in the concept of ecosystem trait. To a great extent, remotely piloted aircraft systems (RPAS) remote sensing, which is capable of retrieving ecosystem traits across multiple scales, can overcome constraints in field plot survey. In this study, we selected alpine grassland ecosystem on the Tibetan Plateau (TP), which is under‐studied due to scarcity of field monitoring data, as the research target. A new data framework was proposed by integrating field plot and RPAS remote sensing data to map spatial patterns of ecosystem traits for the alpine grasslands. Across four landscapes on the TP, ecosystem traits of vegetation coverage (CVC), species number (CSN), individual number (CIN), above ground biomass (AGB), organic carbon content (OC%) and total nitrogen content (TN%) were retrieved. We also calculated Shannon's Diversity Index and Shannon's Evenness Index for each plot. The results showed that RPAS‐based high spatial resolution RGB image is capable of predicting both physical and chemical ecosystem traits for alpine grasslands on the TP. Remote sensing on physical traits are overall more efficient than on chemical traits, with the highest R² of 0.86 and 0.48 for physical trait and chemical one, respectively. The bands of Red and Green contributed more to the prediction model than band of Blue did, and the spectral mean value played a greater role than the spectral standard deviation. Based on the retrieved results, a set of spatial patterns on ecosystem traits can be revealed. This study represents an advance on ecosystem trait study and can significantly improve our understanding on ecosystem functions of the alpine ecosystem on the TP.