Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration, and runoff, hence a vital state variable in hydrological modelling. However, due to the heterogeneity of soil moisture in space, most existing in situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on principal component analysis (PCA) for the investigation of the network redundancy degree and K-means cluster analysis (CA) and a selection of statistical criteria for the determination of the optimal sensor number and placements. Furthermore, the long-term (10-year) 5 km surface soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia-Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale surface soil moisture (i.e. with NSE and r at 0.995 and 0.999, respectively, for the areal mean estimation; and 0.973 and 0.990, respectively, for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22 124 km2 catchment is needed, and in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme, which are also discussed in the paper.