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, <em>27</em>(23), 6307–6319. 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 offsets
v4: added layers for minimum temperature offsets

本研究将1200余条**原位（in situ）**近地表森林温度时间序列数据，与地形、生物及大气候变量整合至机器学习模型中，针对欧洲全域2000年至2020年、空间分辨率为25×25平方米的区域，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、平均及最高温度差值。

若使用本数据集的任一图层，请引用如下文献：
Haesen 等（2021）。《ForestTemp——欧洲森林林冠下微气候温度》，《全球变化生物学》（*Global Change Biology*），27(23)，6307–6319。https://doi.org/10.1111/gcb.15892

可通过文件'extrapolation.tif'基于外推比例对像素进行掩膜处理。所有图层均采用**EPSG:3035**坐标系进行投影。

版本更新记录：
v1：初始发布图层（请勿使用）
v2：勘误修正版图层
v3：新增最高温度差值图层
v4：新增最低温度差值图层