Abstract: Abstract: Understanding the distribution of soil water content of plow layer (0-40 cm) is important for agriculture water management for plant growth. In our study, Hyperion data (EO-1, USGS) was firstly used to inverse topsoil water content and then the measured water content values of 0-40 cm depth were used to calculate the average water content of plow layer. Both data can be used together to obtain a distribution of regional plow layer water content. By use of such method, a study was carried out by taking soil samples in a 64 hm2 area located in Hetao Irrigation District, Inner Mongolia, China in late April. The soil samples were arranged in gird and the grids size were 20, 50, and 100 meters respectively. There were 136 different sampling points and 103 of them had soil samples (0-40 cm depth with 10 cm increment). The time of Hyperion data was April 11, 2013 and it was pre-processed by EVNI 5.0 software. Then derivative filter (1st) was used to remove the scattering and other disturbance. Both raw and filtered images were used to inverse the water content of topsoil using the flag index method and partial least square regression (PLS). After that, the water content of topsoil obtained by Hyperion data were used as the co - variable and the average water contents of 0-40 cm depth were used as the main variable in the co - kriging method to map the water content of plow layer (0-40 cm depth) in the study area. The results indicated that sensitive wavelength bands for topsoil water content were ranged from 1295 nm to 2224 nm when using the flag index method, and the accuracy of prediction models of the flag index method was poor (r<0.2 in the validation process). However, prediction models established by PLS method can yield higher accuracy compared to the flag index method (r>0.5 in both calibration and validation process. The co-kriging interpolation had a consideration of water content of both topsoil (0-10 cm) and plow layer (0-40 cm), and the C0/(C0+C) values in the models were all <25%, demonstrating small random variations in these interpolation models. In addition, compared with the method of linear fitting using topsoil water content and plow layer water content, the co-kriging method can increase 72.6% of r2 and 89.9% of NSE. Therefore, combined hyperspectral inversion with the co-kriging interpolation can be used to predict soil water content of plow layer effectively.