Daily surface all-wave net radiation over global land (1995-2006) from AVHRR data

Surface net radiation, representing surface radiation energy balance, is closely related to several land processes, such as evapotranspiration, photosynthesis, and turbulent and conductive heat fluxes. Reanalysis products can provide a long-term surface net radiation; however, their coarse spatial resolution and large uncertainties hinder us from well applicating the data at a regional scale. Satellite products also include surface net radiation retrievals with high accuracy. The short time span of satellite products (i.e., GLASS product) makes these satellite products not suitable for long-term climate change study. Therefore, we used a deep learning method to upscale in situ measurements collected from global-distributed sites to generate a daily surface net radiation product with 0.05° spatial resolution from AVHRR data (1995-2006). After comprehensive validation, the RMSE of AVHRR net radiation product was ~26 Wm-2, which is generally better than some current reanalysis and satellite products.

地表净辐射（Surface net radiation）表征地表辐射能量平衡，与蒸散发（evapotranspiration）、光合作用（photosynthesis）以及湍流和传导热通量（turbulent and conductive heat fluxes）等多项陆地过程密切相关。再分析产品（Reanalysis products）虽可提供长时序地表净辐射数据，但其空间分辨率较粗且不确定性较大，限制了其在区域尺度上的精准应用。卫星产品同样可提供高精度的地表净辐射反演结果，但卫星产品（如GLASS产品）的时间序列较短，难以适用于长期气候变化研究。因此，本研究采用深度学习方法，对全球分布式站点采集的原位实测数据（in situ measurements）进行尺度上推，基于AVHRR数据（1995-2006年）生成了空间分辨率为0.05°的逐日地表净辐射产品。经综合验证，该AVHRR地表净辐射产品的均方根误差（RMSE, Root Mean Square Error）约为26 W·m⁻²，整体性能优于部分现有再分析产品与卫星产品。