As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

由于气候变化，泛北极地区的气温上升幅度高于地球表面其他地理区域。这导致陆地生态系统和水文循环发生重大变化，影响了植被的分布以及水流和积累的模式。各种遥感技术，包括光学和微波卫星观测，可用于监测这些陆地水和植被动态。在本研究中，卫星和再分析数据集用于制作 2003 年至 2017 年期间西伯利亚上空大陆尺度的具有高时间分辨率（每日）和中等空间分辨率（500 m）的水和植被地图。通过基于像素的机器学习（随机森林）整合多个数据源，生成归一化差异水指数（NDWI）、归一化差异植被指数（NDVI）和没有任何间隙的水分数，即使对于光学数据存在的区域也是如此。缺失（例如，云层）。为了方便用户处理数据，提供了聚合产品，其格式为经纬度投影中的0.1°网格。当使用原始光学图像进行验证时，NDWI 和 NDVI 图像显示出较小的系统偏差，研究区域的均方根误差约为 0.1。该产品既用于2003年至2017年各指数的时间序列趋势分析，又用于基于季节NDVI模式的物候特征提取。前一分析用于识别NDVI 减少而NDWI 增加的区域，以及湖滨和沿海地区NDWI 减少的热点区域。后者的分析采用双 sigmoid 拟合来评估两个落叶松林点的五个物候参数（即春季和秋季的开始和结束以及 NDVI 峰值）的变化，突显了近期生长期延长的趋势。未来将开发更多应用，包括模型集成和对土地覆盖测绘的贡献。