Fast observation simulation method based on XGBoost for visible bands over the ocean surface under clear-sky conditions

Abstract

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.