<p><strong>Abstract.</strong> Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (<i>LST</i>) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived <i>LST</i> and vegetation cover fraction (<i>f_c</i>). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1&amp;thinsp;km resolution MODIS (Moderate resolution imaging spectroradiometer) <i>LST</i> and <i>f_c</i> data and the 1&amp;thinsp;km resolution <i>SM</i> data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014&amp;ndash;2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014&amp;ndash;2018). The field was seeded for the 2014&amp;ndash;2015 (S1), 2016&amp;ndash;2017 (S2) and 2017&amp;ndash;2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015&amp;ndash;2016 (B1) agricultural season. The mean retrieved values of (<i>a_rss</i>, <i>b<sub>rss<sub></i>) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated <i>&amp;alpha;_PT</i> ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved <i>&amp;alpha;_PT</i> remains at a mostly constant value (&amp;sim;&amp;thinsp;0.7) throughout the study period, because of the lack of clear sky disaggregated <i>SM</i> and <i>LST</i> observations during this season. Compared to eddy covariance measurements, TSEB driven only by <i>LST</i> and <i>f_c</i> data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181&amp;thinsp;<i>W/m²</i> for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and <i>LST</i> combined data) with the mean bias values estimated as 39, 4, 7 and 62&amp;thinsp;<i>W/m²</i> for S1, S2, S3 and B1 respectively.</p>