A multi-source eco-hydrological dataset for Northern, Southern, and Eastern Xinjiang (2000–2023)

This dataset provides a spatially explicit eco-hydrological data collection for Xinjiang, China, covering the years 2000–2023. It integrates long-term remote sensing and climate records to support studies of vegetation–water interactions, land cover dynamics, and environmental attribution across arid landscapes. All data are resampled to a unified 825 m resolution and georeferenced in WGS84 coordinate system. **1. Original Remote Sensing and Climate Data** This section includes year-by-year observations from publicly available Earth observation sources:- MODIS-derived indicators: Gross Primary Productivity (GPP), Evapotranspiration (ET), NDVI, and LAI - TerraClimate variables: Precipitation (PR), Soil Moisture (SOIL), and Average Temperature (TEMP) - Land cover: Annual maps from the China Land Cover Dataset (CLCD) - Anthropogenic proxy: Night-time light intensity (DMSP-OLS) - Shapefiles: Ecohydrological zoning of Northern, Southern, and Eastern Xinjiang **2. EcoIndex and Coupling Indicators** This section contains composite indicators and diagnostic layers derived from the original variables:- EcoIndex: A dimension-reduced composite index integrating water use efficiency (WUE = GPP/ET) and NDVI, using a maximum variance projection method - ESI (Ecohydrological Synchrony Index): Cosine similarity metric between NDVI and WUE, reflecting vegetation–water coordination strength - Quadrant classification maps: Annual categorical layers representing four vegetation–water functional regimes (co-enhancement, co-degradation, stress release, emerging stress) **3. Attribution of Climatic and Anthropogenic Drivers** This section includes pixel-wise attribution products generated from machine learning models:- Temporal trend layers: Sen’s slope and Mann–Kendall trend significance for EcoIndex and ESI - Driver importance maps: Random forest–based variable importance for climatic (PR, TEMP, SOIL) and anthropogenic (night-time lights, land cover) factors - Tabular summaries: - `trend_statistics.csv`: Regional statistics of upward/downward/stable trends and average slope magnitudes - `Regional Statistics of Driver Importance Means and Medians in Xinjiang.csv`: Regional averages and medians of driver importance scores All layers are stored in GeoTIFF, CSV, or ESRI Shapefile formats and organized into subfolders by category and year. The dataset supports eco-hydrological research, spatiotemporal modeling, and environmental attribution analyses across dryland regions.本数据集为中国新疆维吾尔自治区提供了一套空间显式的生态水文数据集合，时间跨度为2000年至2023年。本数据集整合了长期遥感与气候观测记录，可支撑干旱景观下的植被-水相互作用、土地覆盖动态变化及环境归因研究。所有数据均重采样至统一的825米空间分辨率，并采用WGS84坐标系进行地理配准。 **1. 原始遥感与气候数据** 本部分包含来自公开地球观测源的逐年观测数据： - MODIS（中分辨率成像光谱仪，Moderate Resolution Imaging Spectroradiometer）衍生指标：总初级生产力（Gross Primary Productivity, GPP）、蒸散发（Evapotranspiration, ET）、归一化植被指数（Normalized Difference Vegetation Index, NDVI）以及叶面积指数（Leaf Area Index, LAI） - TerraClimate 数据集变量：降水量（Precipitation, PR）、土壤湿度（Soil Moisture, SOIL）与平均气温（Average Temperature, TEMP） - 土地覆盖数据：来自中国土地覆盖数据集（China Land Cover Dataset, CLCD）的逐年土地覆盖图 - 人为活动代理指标：夜间灯光强度（DMSP-OLS） - 矢量文件：新疆北部、南部与东部的生态水文分区Shapefile文件 **2. 生态指数与耦合指标** 本部分包含由原始变量衍生的复合指标与诊断图层： - 生态指数（EcoIndex）：采用最大方差投影法，整合水分利用效率（Water Use Efficiency, WUE = GPP/ET）与归一化植被指数得到的降维复合指数 - 生态水文同步指数（Ecohydrological Synchrony Index, ESI）：归一化植被指数与水分利用效率之间的余弦相似度指标，用于反映植被-水协同强度 - 象限分类图：逐年分类图层，表征四类植被-水功能状态（协同增强、协同退化、胁迫缓解与新发胁迫） **3. 气候与人为驱动因子归因** 本部分包含由机器学习模型生成的逐像元归因产品： - 时间趋势图层：针对生态指数与生态水文同步指数的森斜率（Sen’s slope）与曼-肯德尔（Mann-Kendall）趋势显著性检验结果 - 驱动因子重要性图：基于随机森林算法，针对气候因子（PR、TEMP、SOIL）与人为因子（夜间灯光、土地覆盖）的变量重要性分布图 - 表格汇总文件： - `trend_statistics.csv`：包含区域上升/下降/稳定趋势统计及平均斜率幅值的统计数据 - `Regional Statistics of Driver Importance Means and Medians in Xinjiang.csv`：包含新疆地区驱动因子重要性得分的区域平均值与中位数的统计数据 所有图层均以GeoTIFF、CSV或ESRI Shapefile格式存储，并按类别与年份分文件夹组织。本数据集可支撑干旱区的生态水文研究、时空建模及环境归因分析。