# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
###### Abstract  
Precipitation is the basic component of the Earth's water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI. F 2024161267. [[https://doi.org/10.3390/rs16071267](https://doi.org/10.3390/rs16071267)]([https://doi.org/10.3390/rs16071267](https://doi.org/10.3390/rs16071267)) 10.390/rs16071267 10.  
losses to China's industrial and agricultural production and economy [2]. Therefore, obtaining high-resolution and high-precision precipitation data is of great significance to study surface water cycle processes, monitor natural disasters and simulate the watershed hydrology process. At present, there are three main methods for the acquisition of precipitation data: the first is ground rainfall stations' observations, the second is meteorological radar observations and the third is satellite remote sensing observations [3]. The observations of ground rainfall stations are the most direct way to obtain precipitation data, which can accurately reflect the precipitation at a certain point. However, this requires the deployment of very dense rain gauges to accurately observe the characteristics of rainfall in a region [4]. Meteorological radar observations can obtain precipitation data over large areas and with a high level of spatiotemporal accuracy. However, the observation range of precipitation by a single radar is limited, as the accuracy of rainfall measurement decreases with the increase in distance from the radar, and factors such as clutter, beam blockage and abnormal propagation also need to be considered [5]. Satellite remote sensing utilizes satellites acquiring visible light, infrared and passive microwave radiation information to make indirect observations of precipitation. It has the advantage of a high spatiotemporal resolution and wide coverage area and can capture rapidly changing weather phenomena in a short time [6].