Surface upward longwave radiation (SULR) is a key parameter that regulates surface radiation budget balance and matter–energy exchange. However, the state-of-the-art SULR retrieval methods based on remotely sensed data are only effective under clear skies, which mean that the existing methods are unable to generate spatiotemporal continuous SULR product at regional or global scale. Herein, taking the advantage of long-pending abundant ground-based radiation observations, satellite products, and meteorological reanalysis data, a data-driven random forest (RF) method is proposed to retrieve the instantaneous SULR under all-sky conditions. Based on spectral samples of different surface types and simulation results from the moderate resolution atmospheric transmission (MODTRAN), spectral transformation is carried out to transform SULR of various measured domains into the defined 4–100 $\mu \text{m}$ domain at first. SULR and surface downward shortwave radiation (SDSR) observations from seven stations of the surface radiation budget network (SURFRAD) and nine stations of the baseline surface radiation network (BSRN) are used in model’s training and testing procedures, and the RF model achieves a high accuracy with the root-mean-square error (RMSE) of 10.45 W/ $\text{m}^{2}$ on test set. In model evaluation, ground measurements from 14 stations of FLUXNET have been used, and the overall RMSE is 18.40 W/ $\text{m}^{2}$ . In the actual application process, SDSR is estimated by remotely sensed data of Meteosat Second Generation (MSG). The accuracy of RF model has been validated with the observations from five stations of BSRN in 2021, and RMSEs are 17.00, 10.94, 12.17, 27.89, and 12.54 W/ $\text{m}^{2}$ , respectively. Validation result shows that the data-driven method is capable of estimating SULR under all-sky conditions with a high accuracy. Finally, sensitivity analysis has been carried out, and the established RF model keeps robust even though there are great uncertainties among input parameters.

中文翻译：

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利用遥感和气象再分析数据反演全天条件下的白天地表上行长波辐射

地表向上长波辐射（SULR）是调节地表辐射收支平衡和物质-能量交换的关键参数。然而，基于遥感数据的最先进的SULR反演方法仅在晴空下有效，这意味着现有方法无法在区域或全球范围内生成时空连续的SULR产物。在此，利用长期悬而未决的丰富地基辐射观测、卫星产品和气象再分析数据，提出了一种数据驱动的随机森林（RF）方法来反演全天空条件下的瞬时 SULR。基于不同表面类型的光谱样本和中分辨率大气透射（MODTRAN）的模拟结果， $\mu \text{m}$一开始是域名。地表辐射收支网络（SURFRAD）7个站和地面辐射基线网络（BSRN）9个站的SULR和地表向下短波辐射（SDSR）观测用于模型的训练和测试程序，RF模型实现了高均方根误差 (RMSE) 为 10.45 W/ $\文本{m}^{2}$在测试集上。在模型评估中，使用了 FLUXNET 14 个站的地面测量值，整体 RMSE 为 18.40 W/ $\文本{m}^{2}$. 在实际应用过程中，SDSR 是通过 Meteosat Second Generation (MSG) 的遥感数据进行估算的。RF模型的准确性已经通过BSRN 2021年5站观测验证，RMSE分别为17.00、10.94、12.17、27.89和12.54 W/ $\文本{m}^{2}$， 分别。验证结果表明，数据驱动的方法能够在全天空条件下以较高的准确度估计 SULR。最后，进行了敏感性分析，建立的RF模型在输入参数存在很大不确定性的情况下仍保持稳健。