State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China.
Sci Data. 2020 Sep 23;7(1):311. doi: 10.1038/s41597-020-00654-4.
Surface solar radiation is an indispensable parameter for numerical models, and the diffuse component contributes to the carbon uptake in ecosystems. We generated a 12-year (2007-2018) hourly dataset from Multi-functional Transport Satellite (MTSAT) satellite observations, including surface total solar radiation (R) and diffuse radiation (R), with 5-km spatial resolution through deep learning techniques. The used deep network tacks the integration of spatial pattern and the simulation of complex radiation transfer by combining convolutional neural network and multi-layer perceptron. Validation against ground measurements shows the correlation coefficient, mean bias error and root mean square error are 0.94, 2.48 W/m and 89.75 W/m for hourly R and 0.85, 8.63 W/m and 66.14 W/m for hourly R, respectively. The correlation coefficient of R and R increases to 0.94 (0.96) and 0.89 (0.92) at daily (monthly) scales, respectively. The spatially continuous hourly maps accurately reflect regional differences and restore the diurnal cycles of solar radiation at fine resolution. This dataset can be valuable for studies on regional climate changes, terrestrial ecosystem simulations and photovoltaic applications.
地表太阳辐射是数值模型不可或缺的参数，其中散射分量有助于生态系统对碳的吸收。我们利用深度学习技术，从多用途运输卫星（MTSAT）卫星观测中生成了一个 12 年（2007-2018 年）的逐时数据集，包括地表总太阳辐射（R）和散射辐射（R），空间分辨率为 5 公里。所使用的深度网络通过结合卷积神经网络和多层感知机来解决空间模式的整合和复杂辐射传输的模拟问题。与地面测量值的验证表明，逐时值 R 的相关系数、平均偏差误差和均方根误差分别为 0.94、2.48 W/m 和 89.75 W/m，逐时值 R 的相关系数、平均偏差误差和均方根误差分别为 0.85、8.63 W/m 和 66.14 W/m。R 和 R 的相关系数分别在日（月）尺度上增加到 0.94（0.96）和 0.89（0.92）。逐时的空间连续地图准确地反映了区域差异，并以精细的分辨率恢复了太阳辐射的日变化周期。该数据集可用于研究区域气候变化、陆地生态系统模拟和光伏应用。
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