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At the so-called hyper-resolution scale (i.e. grid cells of 1 km2) Land Surface Model (LSM) parameters are sometimes calibrated with Eddy-Covariance (EC) data and Point Scale (PS) soil moisture data. However, measurement scales of EC and PS data differ substantially. In our study, we investigated the impact of reducing the scale mismatch between surface energy flux data and soil moisture data by replacing PS soil moisture data with observations derived from Cosmic-Ray Neutron Sensors (CRNS) made at larger spatial scales. Five soil-evapotranspiration parameters of the Joint UK Land Environment Simulator (JULES) were calibrated against PS and CRNS soil moisture data separately. We calibrated the model for twelve sites in the USA representing a range of climatic, soil, and vegetation conditions. The improvement in surface energy partitioning for the two calibration solutions was assessed by comparing to EC data and to a version of JULES runs with default parameter values. We found that simulated surface energy partitioning did not differ substantially between the PS and CRNS calibrations, despite their differences in actual soil moisture observations. We concluded that potential differences due to distinct spatial scales represented by the PS and CRNS soil moisture sensor techniques were substantially undermined by the weak coupling between soil moisture and evapotranspiration within JULES.