森林是陆地生态系统的核心部分，其生物量约占陆地生态系统总生物量90%，承担着65%以上的年碳固定量，在维持全球碳平衡、减缓温室气体增加等方面具有重要作用。天山云杉是新疆地区重要的林木资源，其种植覆盖面积为75 8600 hm²，占新疆维吾尔自治区天山区域总面积的42.33%，蓄积量为0.17 km³，占总蓄积量的50.66%，构建其生物量时空数据集，可为新疆维吾尔自治区天山区域碳固存潜力评估和天山云杉林保护与可持续经营实践提供科学依据。数据集以2002年、2007年、2012年和2017年天山云杉密集区野外样地调查和相关多源遥感影像资料为基础，利用时空地理加权回归(GTWR)模型拟合生成各时期生物量分布图。该数据集有助于探索天山云杉生长趋势和生物量变化，同时在新疆天山地区生态保护和气候变化研究领域具有科学价值，对区域生态系统管理具有实践意义。

Forests constitute the central part of terrestrial ecosystems, encompassing approximately 90% of the total biomass. They are responsible for over 65% of the annual carbon sequestration, playing a crucial role in maintaining global carbon balance and mitigating the increase of greenhouse gases. Tianshan Spruce, a vital forest resource in the Xinjiang region, covers an area of 758,600 hectares, accounting for 42.33% of the total area, with a volume of 0.17 cubic kilometers, representing 50.66% of the total volume. Constructing a spatiotemporal biomass dataset of Tianshan Spruce provides a scientific basis for assessing the carbon sequestration potential and for the protection and sustainable management of these forests in the region. In recent years, biomass estimation models integrating multisource data like remote sensing, meteorological, topographic, and soil data have been widely adopted. The geographically weighted regression (GWR) method is particularly effective in addressing spatial heterogeneity issues in forestry and ecological applications. This study's dataset is grounded on field survey data from dense Tianshan Spruce areas collected in 2002, 2007, 2012, and 2017 by the project team, adhering to the forest survey technical procedures of the National Forestry and Grassland Administration, and on multisource remote sensing images from the Google Earth Engine. The geographically and temporally weighted regression (GTWR) model was employed to generate biomass distribution maps for each period. This dataset is instrumental in exploring the growth trends and biomass variations of Tianshan Spruce, holding scientific value for ecological protection and climate change research in the Tianshan area of Xinjiang. It has practical significance for regional ecosystem management. Additionally, it offers data support for identifying and protecting key ecological areas, assessing the impact of climate change on ecosystems, monitoring and simulating the carbon cycle processes in ecosystems, and contributing to global carbon budget research. Furthermore, it provides valuable data resources for researchers in ecosystem management and related fields.