陆地生态系统的物质和能量循环离不开植被，植被覆盖变化对生态环境有深远影响。当前研究多聚焦于NDVI与气候因子、人为因素的响应分析，未考虑地形地貌、土壤特征等非线性因素的影响。因此，植被覆盖变化的监测及其影响因子的响应研究对陆地生态系统研究中具有重要意义。2000-2021年新疆NDVI空间分布格局及其影响因子数据集基于GEE平台获取MODIS遥感数据和ERA5-Land Monthly Averaged数据集，采用ArcGIS软件将TIF型数据处理为数值型数据，借助Python工具包对其进行清洗、归一化等预处理，最终形成包含14个字段，2756条数据数值型数据；采用克里金插值法，对获取的数据进行空间插值，得到13张影响因子空间插值图。本数据集可以用作构建新疆植被覆盖变化及其影响因子关系研究建模，为不同影响因子对NDVI变化提供基础数据，进一步为该地区的农业技术与环境开发管理等领域提供科学依据。
", "ds_source": "本数据集的矢量边界数据来自中国科学院资源环境科学与数据中心（www.resdc.cn）;植被类型数据来源于中国科学院资源环境科学与数据中心的中国100 万植被类型空间分布数据，植被类型分为12大类，研究区仅有9大类植被类型；研究区高程数据来源于中国科学院资源环境科学与数据中心，数据空间分辨率为 1km。\n
\n基于已有研究，选定13个NDVI变化潜在影响因子，其中，辐射量、潜在蒸发、径流、根部土壤湿度来源于GEE平台所提供的ERA5-Land Monthly Averaged数据集，其分辨率为11132m；地貌类型、土壤类型均来自中国科学院资源环境科学数据中心发布的1km栅格数据；经度、纬度、坡度、坡向均基于 DEM 数据由 ArcGIS 软件计算生成；人类影响指数由社会经济数据和应用中心（SEDAC）发布的全球数据。
", "ds_process_way": "将所有获取的数据按照研究区矢量文件裁剪完成以后，依据掩膜文件提取研究区的植被类型数据，借助 ArcGIS 软件将研究区植被类型文件进行矢量转点操作。通过计算每个点的经度和纬度信息，将 14 个属性依次根据植被类型矢量转点的文件进行值提取至点的操作，使得每个点可对应 14 个字段。然后去除异常值得到 2756 条完整有效数据。再采用克里金插值法，分别对影响因子做空间插值计算，得到各个影响因子的空间插值分布图。最后借助Python 工具包对 2756 条数值型数据做归一化处理。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "新疆", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 2348201, "ds_files_count": 2, "ds_format": "", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "9bdd5cb6-198c-4470-bae9-070115e9a73b.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-03-28 19:01:02", "last_updated": "2025-05-29 11:06:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/17058.11.sciencedb.agriculture.00139", "i18n": { "en": { "title": "Spatial Distribution Pattern of NDVI and Its Influencing Factors in Xinjiang from 2000 to 2021", "ds_format": "", "ds_source": "The vector boundary data of this dataset is from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (www.resdc. cn); Vegetation type data is from the spatial distribution data of 1 million vegetation types in China from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences. Vegetation types are divided into 12 categories, and the study area has only 9 categories of vegetation types; The elevation data of the study area is from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences, with a spatial resolution of 1km.\n
\nBased on the existing research, 13 potential impact factors of NDVI changes are selected. Among them, radiation, potential evaporation, runoff and root soil moisture are derived from the ERA5 Land Monthly Average dataset provided by the GEE platform, with a resolution of 11132m; geomorphic types and soil types are from the 1km grid data released by the Resource and Environmental Science Data Center of the Chinese Academy of Sciences; Longitude, latitude, slope, and aspect are all calculated and generated using ArcGIS software based on DEM data; The Human Impact Index is a global data released by the Center for Socio Economic Data and Applications (SEDAC).
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "The material and energy cycle of terrestrial ecosystems cannot be separated from vegetation, and changes in vegetation cover have a profound impact on the ecological environment. Current research mostly focuses on the response analysis of NDVI to climate factors and human factors, without considering the influence of nonlinear factors such as topography, soil characteristics, etc. Therefore, the monitoring of vegetation cover changes and the response of their influencing factors are of great significance in the study of terrestrial ecosystems. The Xinjiang NDVI spatial distribution pattern and its influencing factors dataset from 2000 to 2021 was obtained based on the GEE platform using MODIS remote sensing data and ERA5 Land Monthly Average dataset. The TIF data was processed into numerical data using ArcGIS software, and preprocessed using Python toolkit for cleaning and normalization, resulting in a numerical data set consisting of 14 fields and 2756 data points; Using Kriging interpolation method, the obtained data was spatially interpolated to obtain 13 spatial interpolation maps of influencing factors. This dataset can be used to construct a research model for the relationship between vegetation cover change and its influencing factors in Xinjiang, providing basic data for the impact of different influencing factors on NDVI changes, and further providing scientific basis for agricultural technology and environmental development management in the region.
", "ds_time_res": "", "ds_acq_place": "Xinjiang", "ds_space_res": "", "ds_projection": "", "ds_process_way": "After cropping all the obtained data according to the vector file of the study area, the vegetation type data of the study area is extracted based on the mask file, and the vegetation type file of the study area is transformed into vector points using ArcGIS software. By calculating the longitude and latitude information of each point, the 14 attributes are sequentially transformed into point files based on vegetation type vectors for value extraction to the point, so that each point can correspond to 14 fields. Then, removing outliers resulted in 2756 complete and valid data points. Then, using the Kriging interpolation method, perform spatial interpolation calculations on the influencing factors separately to obtain spatial interpolation distribution maps for each influencing factor. Finally, 2756 numerical data were normalized using the Python toolkit.
", "ds_ref_instruction": "When using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "植被覆盖", "新疆", "NDVI", "2000-2021" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "孙伟", "email": "maplesunw@163.com", "work_for": "中国农业科学院农业信息研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "孙伟", "email": "maplesunw@163.com", "work_for": "中国农业科学院农业信息研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "孙伟", "email": "maplesunw@163.com", "work_for": "中国农业科学院农业信息研究所", "country": "中国" } ], "category": "生态" }