Advances in the Study of Land Surface Emissivity Retrieval from Passive Microwave Remote Sensing

Abstract

The microwave land surface emissivity(MLSE)is a very important parameter for describing the characteristics of the lands,and it is also a key factor for retrieving the parameters of land and atmosphere.Space-borne passive microwave radiometers provide direct retrieved land surface emissivity spectra with larger temporal and spatial scales compared with physical modeling simulation in that the physical modeling simulation needs plenty of parameters,but quite a few of these parameters,such as parameters of land surface and vegetation,are not available from traditional measurements.This paper systematically reviews MLSE retrieving algorithms for passive microwave remote sensing data,which include statistical approach,atmospheric radiation transfer model approach,index analysis approach,neural network approach and one-dimensionally variational analysis approach.The main advantages and limitations of these five methods are also discussed.Finally,the development tendencies of estimating MLSE by remote sensing are pointed out,such as developing algorithms of Radio Frequency Interference(RFI) detection and correction,improving algorithms of detection of clouds and rain-affected radiances,and intensive research on microwave atmospheric radiation transfer process.