For many applications such as climatology, agriculture, and fire prevention, information regarding soil moisture can prove very relevant. With a large number of spaceborne or airborne sensors, optical remote sensing can be used to assess the surface soil moisture. In this paper, several indices and the MARMIT4SMC method, relying on the multilayer radiative transfer model of soil reflectance (MARMIT) model, were applied to hyperspectral images of a bare soil in the 0.4 to 2.5 μm spectral range, with a high spatial resolution (4.2 cm), and compared them in terms of robustness. Indeed, only a few experiments were performed with images in the field, and the indices were calibrated due to in situ measurements with probes or sample weighing. In our experiment, only a very small area was available for in situ measurements. This motivated the use of laboratory calibration relying on soil samples and a robustness comparison of the involved metrics. MARMIT parameters and a set of indices were computed for each pixel of a couple of images of bare dry and wet soil, respectively. The indices or parameter values were converted to soil moisture content, using a laboratory calibration. Visual and numerical analyses on the moisture content images obtained for each index or method were performed. The soil heterogeneity and the width of the histograms, reaching 20%, show that the step from the laboratory to the field can be relevant only at the scale of a set of pixels: the large discrepancy prevents from a reliable moisture assessment from a single pixel, but a histogram can bring a fairly reliable one. It also leads to the conclusion that MARMIT4SMC and the Normalized Soil Moisture Index, look more robust than the others when going from the laboratory to the field.