The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture spatial and temporal variability and estimate the ground truth for validating remote sensing productions. However, prior information for a target variable is not available. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions has been developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one that represents the variability of the variogram modeling and another that improves the spatial prediction in the evaluation of remote sensing productions. The reasonability of the optimized EHWSN is validated based on the representativeness, variogram modeling and spatial accuracy through the use of 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness, and the variograms estimated by samples have a mean error of less than 3% compared with true variograms. These samples are used to predict fields at multiple scales, and as the scale increases, the estimated fields are more similar to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when the characteristics of an optimized variable are unknown.