Cascaded Downscaling-Calibration Networks for Satellite Precipitation Estimation

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Jing, Yinghong, Lin, Liupeng, Li, Xinghua, Li, Tongwen, Shen, Huanfeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agricultural production; Belief networks; Belief propagation; Climate change; Convolutional network; Convolutional neural networks; deep belief network; Deep learning; Estimation; ground–satellite fusion; Hydrologic processes; Hydrology; Precipitation; Precipitation estimation; Rain; Resolution; Resource management; Root-mean-square errors; Satellite ground stations; Satellite navigation systems; Satellite observation; Satellites; Spatial discrimination; spatial downscaling; Spatial resolution; Surface treatment; Terrestrial atmosphere; Water circulation; Water resources; Water resources management; Weather forecasting; China
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2022.3214083

Precipitation is a critical process in the terrestrial hydrological circulation, affecting climate change, water resource management, and agricultural production. Satellite-borne observations have prominent advantages in macro and mesoscopic quantitative precipitation estimation. Nevertheless, they are subject to low spatial resolution and inherent biases. Therefore, this study utilizes the surface-surface downscaling network and point-surface fusion network for fine-resolution and high-precision precipitation mapping over China. To deeply explore the complicated relationships between various ancillary factors, ground measurements, and satellite precipitation, an attention mechanism-based convolutional network (AMCN) is used for spatial downscaling and a geo-intelligent deep belief network (Geoi-DBN) is used for ground-satellite fusion. Experimental results indicate that cascaded networks toward two different objectives are superior to baseline methods, achieving <inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula> and root mean square error (RMSE) of about 0.84 and 27.23 mm/month, respectively. Besides, the assistance of geo-intelligent items and ancillary factors contributes to fusion accuracy. This study provides an effective way for precipitation estimation over China.