Remotely sensed soil moisture (SM) with the enhanced spatial resolution (several kilometers or even tens of meters) is of value for important local applications like irrigation scheduling and farm-scale water management. The Disaggregation based on Physical and Theoretical scale Change (DISPATCH) algorithm is an established evaporation-based tool for passive-SM downscaling. However, its performance in areas where evapotranspiration is generally energy-limited has not been adequately evaluated. Here, DISPATCH is applied to coarse-scale L-band passive-SM to evaluate its applicability to produce a 1-km downscaled product in the semi-humid/humid Huai River Basin (HRB), which has been identified as challenging for DISPATCH in previous studies. The influence of different calibration modes of linear SEE (soil evaporative efficiency) model on disaggregation results and LST (land surface temperature) inputs at different temporal resolutions are also investigated. The performances of DISPATCH were evaluated using a spatio-temporal statistical analysis against available in-situ SM. Globally, DISPATCH performs poorly in HRB with low correlation and least-square regression slopes (significantly different to unity) between downscaled-SM and in-situ observations. This relatively poor performance is attributed to low background SM spatial variability and weak moisture-evaporation coupling within HRB. However, in spite of those global poor performances, an improvement in the SM spatio-temporal representation was observed under summer-dry conditions over HRB. The uncertainty of DISPATCH estimates can be reduced by the multi-date calibration of linear SEE model within HRB. Likewise, downscaled SM based on merging multiple LST products for three consecutive days has better temporal coverage and lower uncertainty than comparable products only using daily LST retrievals. These configurations are expected to further enlarge the applicability of DISPATCH in more humid climates.