本数据集是针对塔吉克斯坦全境构建的多要素生态环境基础数据集，由动态时序监测产品和静态本底属性产品两部分组成。数据集空间分辨率统一为1 km，动态时序产品覆盖2001—2022年每年2—9月、旬尺度，静态本底属性产品为同期空间基础数据，不具有时间序列属性。动态产品以植被生长状态和地表水分环境为核心，静态产品刻画地形与土壤质地本底特征，可服务于塔吉克斯坦干旱区生态环境演变、草地退化评估、农业干旱监测、蝗虫等生物灾害早期预警及生态灾害评估。\n
本数据集包含动态时序栅格与静态本底栅格两类产品：（一）动态时序产品共7个波段（图层）：Band1（NDVI）为当前旬归一化差值植被指数；Band2（NDVI_PREV）为前一旬NDVI；Band3（NDVI_PREV2）为前两旬NDVI；Band4（NDVI_HIST）为基于2001-2022年历史同期旬数据计算的中值NDVI基准；Band5（NDVI_ANOMALY）为当前NDVI与历史同期中值的差值异常指数；Band6（P30）为旬结束前溯30天的累积降水量（mm）；Band7（SM30）为同期30天表层0-7cm土壤体积含水量均值（m³/m³）。（二）静态本底产品共7个独立栅格层：① 高程（NASADEM，单位m）；② 坡度（度°）；③ 坡向（度°）；④ 5-15cm深度土壤沙粒含量（Sand，重量百分比%）；⑤ 5-15cm深度土壤粘粒含量（Clay，重量百分比%）；⑥ 5-15cm深度土壤粉粒含量（Silt，重量百分比%）；⑦ 5-15cm深度土壤粗碎屑含量（Coarse Fragments，体积百分比%）。所有栅格空间范围均裁剪至塔吉克斯坦国境边界，空间分辨率为1km，坐标系统一为WGS84地理坐标系（EPSG:4326）。", "ds_source": "
本数据集的原始数据分为动态时序源和静态本底源两类，均通过Google Earth Engine（GEE）数据目录在线调用获取。动态时序源包括：① NASA MODIS MOD09A1 V061，按研究区及2001—2022年2—9月时间范围筛选获取红光、近红外及质量控制信息；② UCSB-CHG CHIRPS Daily v2.0，按研究区和日期范围获取日降水数据；③ ECMWF ERA5-Land Daily Aggregated，按研究区和日期范围获取0—7 cm表层土壤体积含水量。静态本底源包括：④ NASADEM，调用塔吉克斯坦区域DEM数据；⑤ ISRIC SoilGrids v2.0，调用塔吉克土壤属性栅格获取5—15 cm深度层的Sand、Clay、Silt及Coarse Fragments数据。", "ds_process_way": "
数据加工基于Google Earth Engine云平台完成，分为动态时序处理和静态本底处理两条主线。\n
首先，动态时序数据利用MOD09A1计算NDVI，以旬为窗口合成当前旬、前一旬及前两旬NDVI；剔除目标年份后提取历年同期旬NDVI集合逐像元取中值构建历史基准，当前NDVI减去基准得到NDVI_ANOMALY；以旬结束日为终点回溯30天，分别统计CHIRPS累积降水量和ERA5-Land土壤湿度均值，生成P30和SM30，并将连续变量聚合至1 km。\n
其次，静态数据提取NASADEM高程，并利用GEE地形算法计算坡度和坡向；提取ISRIC SoilGrids 5—15 cm深度层的Sand、Clay、Silt及Coarse Fragments。\n
最后，对连续型动态和静态栅格统一采用双线性重采样至1 km；国境边界作为空间掩膜使用，不采用最近邻对连续变量进行重采样。所有数据统一处理到WGS84（EPSG:4326）， 裁剪至塔吉克斯坦国境范围并输出。", "ds_quality": "
本数据集质量由原始数据质量、处理流程、产品一致性及产出后检查共同保障。动态时序方面，MOD09A1采用原始质量控制信息进行云、阴影及卷云等低质量观测剔除，旬内采用中值合成降低观测噪声；CHIRPS在高海拔及地面站稀少区域存在空间插值不确定性，ERA5-Land土壤湿度在复杂地形下存在再分析模型偏差，因此P30和SM30主要用于区域相对丰枯趋势分析。静态本底方面，NASADEM和SoilGrids均为全球标准化数据产品，重采样至1 km会产生一定尺度转换误差，SoilGrids土壤属性属于模型预测数据，在塔吉克斯坦局地验证点有限，不宜用于精准点位定量分析。产品处理过程中采用统一空间范围、坐标系和1 km网格，并检查动态产品时间完整性、波段完整性、空间范围、空间分辨率及数值范围，同时检查静态图层的空间覆盖和有效值情况。当前数据材料未提供独立地面样本的系统精度验证结果，因此不对各生态环境指标给出未经验证的定量精度指标，建议用于国家级和区域级宏观生态环境监测与评估。", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "塔吉克斯坦", "ds_acq_lon_east": 75.23333333333333, "ds_acq_lat_south": 36.65, "ds_acq_lon_west": 67.5, "ds_acq_lat_north": 41.083333333333336, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 3752455433, "ds_files_count": 532, "ds_format": "*.tif", "ds_space_res": "", "ds_time_res": "旬", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "aafbf93d-721f-42e1-89db-e6d7ed5e46bd.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "b6ee8072-db07-4053-bcbc-78e22d72c5ec", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 0, "publish_time": "2026-10-14 15:20:00", "last_updated": "2026-10-14 15:20:00", "protected": false, "protected_to": "2027-09-05 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.db7834.2026", "i18n": { "en": { "title": "Tajikistan 1 km Ecological and Environmental Elements Dataset (2001-2022)", "ds_format": "*.tif", "ds_source": "
The raw data of this dataset is divided into two types: dynamic time-series sources and static background sources, both of which are obtained through online call from the Google Earth Engine (GEE) data catalog. Dynamic timing sources include: ① NASA MODIS MOD09A1V061, filtered to obtain red light, near-infrared and quality control information according to the study area and the time range from February to September 2001 to 2022;② UCSB-CHG CHIRPS Daily v2.0, obtain daily precipitation data according to the study area and date range;③ ECMWF ERA5-Land Daily Aggregated, obtain the volume water content of 0-7 cm surface soil according to the study area and date range. Static background sources include: ④ NASADEM, which calls Tajikistan regional DEM data; ④ ISRIC SoilGridds v2.0, which calls Tajikistan soil attribute grids to obtain Sand, Clay, Silt and Coarse Fragments data at a depth of 5-15 cm.", "ds_quality": "
The quality of this dataset is guaranteed by the quality of raw data, processing procedures, product consistency and post-output inspection. In terms of dynamic time series, MOD09A1 uses original quality control information to eliminate low-quality observations such as clouds, shadows and cirrus, and uses median synthesis to reduce observation noise within ten days;CHIRPS has spatial interpolation uncertainties in areas with high altitudes and few ground stations., ERA5-Land soil moisture has reanalysis model deviations under complex terrain, so P30 and SM30 are mainly used for regional relative abundance and drought trend analysis. In terms of static background, NASADEM and SoilGridds are both global standardized data products. Resampling to 1 km will produce a certain scale conversion error. SoilGridds soil attributes are model prediction data and have limited local verification points in Tajikistan and should not be used for precise point-point quantitative analysis. A unified spatial range, coordinate system and 1-kilometer grid are used during product processing, and the temporal integrity, band integrity, spatial range, spatial resolution and numerical range of the dynamic product are checked, and the spatial coverage and effective value of the static layer are also checked. Current data materials do not provide systematic accuracy verification results of independent ground samples, so unverified quantitative accuracy indicators are not given for each ecological environment indicator. They are recommended for national and regional macro-ecological environment monitoring and evaluation.", "ds_ref_way": "", "ds_abstract": "
This dataset is a multi-factor ecological environment basic dataset built throughout Tajikistan. It consists of two parts: dynamic time-series monitoring products and static background attribute products. The spatial resolution of the data set is unified to 1 km. The dynamic time series products cover the February to September and ten-day scales of each year from 2001 to 2022. The static background attribute products are spatial basic data for the same period and do not have time series attributes. Dynamic products focus on vegetation growth status and surface moisture environment. Static products depict the background characteristics of terrain and soil texture. They can serve the evolution of ecological environment in arid areas of Tajikistan, grassland degradation assessment, agricultural drought monitoring, early warning of biological disasters such as grasshoppers and ecological disaster assessment.", "ds_time_res": "", "ds_acq_place": "Tajikistan", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Data processing is completed based on the Google Earth Engine cloud platform and is divided into two main lines: dynamic timing processing and static background processing.\r\n
First, NDVI is calculated using MOD09A1 for dynamic time series data, and the current ten days, the previous ten days and the two ten days are synthesized using ten days as a window; After eliminating the target year, the ten days NDVI set in the same period of the calendar year is extracted and the median value is taken one by one to build a historical benchmark. The current NDVI is subtracted from the benchmark to obtain NDVI_ANOMALY; Looking back for 30 days with the end date of ten days, the cumulative CHIRPS precipitation and the average soil moisture of ERA5-Land are counted respectively to generate P30 and SM30, and the continuous variables are aggregated to 1 km.\r\n
Secondly, the NASADEM elevation is extracted from static data, and the GEE terrain algorithm is used to calculate slope and aspect; Sand, Clay, Silk and Coarse Fragments of ISRIC SoilGrids5 -15 cm depth layer are extracted.\r\n
Finally, bilinear resampling to 1 km is uniformly used for continuous dynamic and static grids; national borders are used as spatial masks, and continuous variables are not resampled by nearest neighbors. All data is uniformly processed into WGS84 (EPSG:4326), clipped to the border of Tajikistan and exported.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "allow_update_data": false, "created_from": "admin", "ds_topic_tags": [ "黏土含量", "土壤", "坡度", "坡向", "高程", "静态", "动态" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "塔吉克斯坦" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "钱静", "email": "jing.qian@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" }, { "true_name": "孙博", "email": "sunbo@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" }, { "true_name": "魏树杰", "email": "sj.wei@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" }, { "true_name": "邢秀为", "email": "xw.xing@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "魏树杰", "email": "sj.wei@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "魏树杰", "email": "sj.wei@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "category": "水文" }