Remote sensing of the earth is the main task of meteorological satellites.Due to factors such as cloud cover and cosmic ray radiation,remote sensing data obtained by meteorological satellites often have a large number of missing and ab-normal data.Fourier neural operators have the characteristics of high efficiency,high accuracy,and flexible resolution.This pa-per proposes a remote sensing data prediction algorithm based on Fourier neural operators.The algorithm first fills in the miss-ing values of remote sensing data using the spatial mean method and Lagrange interpolation method,and then trains the map-ping relationship of spatial values in the time domain using Fourier neural operators.Finally,the trained model is used to pre-dict the latest remote sensing data.Simulation experiments based on real remote sensing data of Fengyun-4 remote sensing sat-ellite show that the method proposed in this paper is more effective than others.Good prediction accuracy can still be main-tained in long-term time series forecasting.
earth remote sensingtime series predictionlong-term predictionfourier neural operator
维普
万方数据
