ANALYSIS OF THE EFFECT OF INCIDENCE ANGLE AND MOISTURE CONTENT ON THE PENETRATION DEPTH OF L- AND S-BAND SAR SIGNALS INTO THE GROUND SURFACE

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9. The SMOS-Derived Soil Water EXtent and equivalent layer thickness facilitate determination of soil water resources B. Usowicz et al. 10.1038/s41598-020-75475-x
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