In this paper,a new scheme is proposed for simultaneously retrieving Soil Moisture (SM) and Vegetation Optical Depth (VOD),solely from the Cyclone Global Navigation Satellite System (CyGNSS) data.This work is accomplished by employing two pre-trained neural networks (NNs),including one for computing SM from the CyGNSS data and Soil Moisture Active Passive (SMAP) VOD as well as the other for calculating VOD from the CyGNSS data and SMAP SM product,through a brute-force searching.By adopting the proposed method,the posterior SM/VOD can be estimated merely using the CyGNSS data,free from other auxiliary data.The attained results are validated against SMAP products for two separate periods:the whole year of 2018 and a recent course in 2020.Satisfactory agreements between the retrieved and referred SM/VOD are achieved,with correlation coefficients (r) of 0.86 and 0.84,along with root-mean-square errors (RMSEs) of 0.064 and 0.071 cm³/cm³ for SM in the years of 2018 and 2020,respectively;and for the verification of VOD,r=0.98 and RMSE=0.079 are acquired for 2018,and r=0.98 and RMSE=0.084 for 2020,respectively.The good consistency obtained in this work illustrates the capability of CyGNSS as a new independent source for estimating pan-tropical SM and VOD.