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This research examines the use of surface soil moisture from the AMSR-E passive microwave satellite to derive surface soil moisture anomalies by exploiting spatial resolution to compensate for the shorter temporal record of the satellite sensor.
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This paper presents MULESME, a software designed for the systematic mapping of surface soil moisture using Sentinel-1 SAR data.
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Comparison of prediction models of surface soil horizons using ordinary kriging and regression kriging indicated that ordinary kriging had greater predictive power; however, regression kriging using principal components of covariate data more effectively captured the spatial patterns of soil property landscape relationships.
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In a synthetic study we explore the potential of using surface soil moisture measurements obtained from different satellite platforms to retrieve soil moisture profiles and soil hydraulic properties using a sequential data assimilation procedure and a 1D mechanistic soil water model.
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