ForestTemp: sub-canopy microclimate temperatures of European forests

Combining more than 1,200 time series of <em>in situ</em> near-surface forest temperatures with topographical, biological and macroclimatic variables in a machine learning model, we predicted the monthly offset for minimum, mean and maximum temperature between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 x 25 m² across Europe. When using any of these layers, please cite: Haesen et al. (2021). ForestTemp –Sub-canopy microclimate temperatures of European forests. Global Change Biology, 00, 1–13. https://doi.org/10.1111/gcb.15892 To mask pixels by the proportion of extrapolation, the file 'extrapolation.tif' can be used. All layers are projected in epsg:3035. <br> Version history: v1: original layers (DO NOT USE) v2: corrigendum layers v3: added layers for maximum temperature offset

本研究将1200余条原位（in situ）森林近地表温度时间序列，与地形、生物及大气候变量整合至机器学习模型中，针对2000—2020年欧洲全域25×25平方米的空间分辨率下，预测了地表以上15厘米处林冠下温度与自由大气温度之间的月度最低、平均及最高温度偏差。若使用本数据集的任意图层，请引用如下文献：Haesen等（2021）。ForestTemp –Sub-canopy microclimate temperatures of European forests. Global Change Biology, 00, 1–13. https://doi.org/10.1111/gcb.15892。若需基于外推比例对像素进行掩膜处理，可使用'extrapolation.tif'文件。所有图层均采用EPSG:3035坐标系投影。版本更新历史：v1：初始图层（请勿使用）；v2：勘误版图层；v3：新增最高温度偏差图层。