ABSTRACT

The fast radiative transfer model has a wide range of applications in remote sensing, such as satellite L2 product retrieval and satellite L1 data quality monitoring and assessment. The traditional radiative transfer model, particularly reflective solar bands, has the disadvantage of being time-consuming. To improve the calculation efficiency and realize fast observation simulation over the global ocean under clear-sky conditions, machine learning technology is applied. A fast radiative transfer simulation method based on the extreme gradient boosting (XGBoost) algorithm is proposed. The polynomial regression and XGBoost regression models in the machine learning field were used in the fast simulation process. By comparing and analysing the experimental results of the two regression models, it was revealed that the prediction results of the XGBoost algorithm were better than those of the polynomial regression model. The shorter the wavelength, the better the prediction performance, with a small error and a larger determination coefficient. The blue bands demonstrated the best results, with an R²(R-Squire) value of approximately 0.99. The deviation analysis between the predicted and simulated values demonstrated that there was no obvious functional dependence between the prediction error and the influencing factors.

摘要

快速辐射传输模型在遥感中具有广泛的应用，例如卫星L2产品检索和卫星L1数据质量监视和评估。传统的辐射传递模型，特别是反射性太阳波段，具有耗时的缺点。为了提高计算效率并在晴空条件下实现对全球海洋的快速观测模拟，应用了机器学习技术。提出了一种基于极端梯度增强（XGBoost）算法的快速辐射传递仿真方法。快速仿真过程中使用了机器学习领域中的多项式回归和XGBoost回归模型。通过比较和分析两个回归模型的实验结果，结果表明，XGBoost算法的预测结果优于多项式回归模型的预测结果。波长越短，预测性能越好，误差较小，确定系数较大。蓝带显示了最佳效果，R ²（R-Squire）值约为0.99。预测值与模拟值之间的偏差分析表明，预测误差与影响因素之间没有明显的功能依赖性。