ABSTRACT
An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.
Subject(s)
Soil , Water , Water/analysis , MicrowavesABSTRACT
Mirror jitters around a bias tilt angle can make noise performance degradation for a space-borne Michelson interferometer. A numerical model simulates the Hyperspectral Infrared Atmospheric Sounder (HIRAS) spectra affected by the mirror jitters. According to the simulation, mirror jitters mainly generate spectrally correlated noise, which can be estimated by subtracting the random noise component from the total noise. The random noise is estimated through a principal component analysis (PCA) technique. Applying the PCA noise estimator as a diagnostic tool to monitor the noise level in the process of bias tilt angle tuning, optimized HIRAS noise performance is achieved with the correlated noise component minimized.
