Improving Instantaneous Rainfall Estimation from MSG Imagery with a Soft-Weighted Dual U-Net–XGBoost Strategy

This study aims to improve quantitative precipitation estimation at high spatial and temporal resolutions using Meteosat Second Generation (MSG) satellite imagery. In this context, two complementary models are exploited for the classification of precipitation intensities into five classes, namely: No rain, Light rain, Moderate rain, Heavy rain and Very heavy rain. To better exploit the information, XGBoost captures the radiometric response at the individual pixel level, while U-Net extracts the texture and spatial context of the cloud neighborhood. The classification architecture merges the probabilistic responses derived from spectral signatures via XGBoost with the extraction of cloud spatio-textural features from the deep multiclass U-Net network. Their output probabilities are combined and recalibrated using a logistic regression meta-classifier into a unified probability vector. To obtain instantaneous estimates, the final value corresponds to the sum of the previously defined precipitation intensities, each multiplied by the probability predicted by the meta-classifier. The models are trained on ground radar observations and validated against temporally independent radar test events from Sétif. Instantaneous precipitation estimates, as well as accumulations, were calculated. The proposed hybrid model significantly outperforms state-of-the-art benchmark architectures (notably, MCM, Ada/Bag-ANN, and DST-ANN/SVM/RF). At the instantaneous scale, the model achieves remarkable accuracy, with a correlation coefficient CC = 0.983, a reduction of RMSE to 0.40 mm/h (representing a decrease of over 45% compared to U-Net alone), and a near-zero bias (BIAS = −0.02 mm/h). During longer-scale temporal integrations, the model maintains strong volumetric consistency and minimal cumulative drift across temporal scales, reaching a CC = 0.993 and an RMSE = 1.38 mm/24 h at the daily scale, as well as a CC = 0.997, an RMSE = 2.76 mm/week, and a BIAS = 0.02 mm/week at the weekly scale. These results confirm that combining spatio-textural and point-wise spectral probabilities provides a reliable solution for high-resolution quantitative precipitation estimation, offering valuable inputs for regional hydrometeorological monitoring in complex geophysical environments. While the trained model is tailored to Mediterranean winter conditions and requires targeted retraining for application to other climatic regions or summer convective regimes, these results confirm that combining spatio-textural and point-wise spectral probabilities provides a highly reliable solution for high-resolution quantitative precipitation estimation.