Since its launch in 2009, the ESA’s SMOS mission is providing global soil moisture (SM) maps at ~40 km, using the first L-band microwave radiometer on space. Its spatial resolution meets the needs of global applications, but prevents the use of the data in regional or local applications, which require higher spatial resolutions (~1-10 km). SM disaggregation algorithms based generally on the land surface temperature (LST) and vegetation indices have been developed to bridge this gap. This study analyzes the SM-LST relationship at a variety of LST acquisition times and its influence on SM disaggregation algorithms. Two years of in situ and satellite data over the central part of the river Duero basin and the Iberian Peninsula are used. In situ results show a strong anticorrelation of SM to daily maximum LST (R˜-0.5 to -0.8). This is confirmed with SMOS SM and MODIS LST Terra/Aqua at day time-overpasses (R˜-0.4 to -0.7). Better statistics are obtained when using MODIS\udLST day (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.06 m3 /m3 ) than LST night (R˜0.45 to 0.80; ubRMSD˜0.04 to 0.07 m3 /m3 ) in the SM disaggregation. An averaged ensemble of day and night MODIS LST Terra/Aqua disaggregated SM estimates also leads to robust statistics (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.07 m3 /m3 ) with a coverage improvement of ~10-20 %.
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