**4.1. Argentinean test site**

Over the Argentinean test site, the algorithm (fig.8) is divided in two main parts: one to be used in plots with bare soil or covered with sparse vegetation and another for vegetated soils. In both cases, two versions of the algorithm were developed: a simplified one working on a vector of mean values for each plot where the aim is to analyze the backscatter coefficient behavior using random values within ranges of *s* and *l*, and another one to work on the whole image, on pixel basis, to investigate the SM spatial distributions. Working with average values of backscattering coefficients has two objectives: to understand the effect on the SM estimates when the signal noise in the single plot is strongly reduced and to lower the computation burden when applying a random function for *s* and *l* variables.

An extensive analysis was conducted in order to understand the behavior of variables such as surface roughness and vegetation presence in the final SM estimation through the variability of the prior information. The different cases analyzed are listed below:


added to the mean values of *s* and *l*. The pseudo random values are drawn from a standard normal distribution.

Case1 and 3 results are reported in the form of SM maps in fig.10. A detailed analysis of the maps in fig.10 indicated error patterns detected for cases with rows of plots oriented orthog‐ onally to the direction of the sensor observation. As it was observed, the backscattering coefficients for HH polarization is sensitive to the orientation of lines tillage and no inversion algorithms consider this factor. Consequently the results show significant errors in plots

Integration of Remotely Sensed Images and Electromagnetic Models into a Bayesian Approach for…

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**Figure 10.** Soil moisture maps for Case 1 (left) and Case 3 (right) over the selected test site.

Case 1 shows that the northern plots with bare soils (1N and 2N) have moisture values very similar to the ground truth. On the contrary, southern plots with bare soils (1S and 2S) have higher moisture values than the measured ones, having the first of them a value of 25%, while the in situ data shown values around 20%. Case 3 shows that plot 1N and 2N obtained moisture values around 15%, which represents an over-estimation of the actual value of around 5%. For plots 1S and 2S, the estimate values are between 22 to 24%. Case 1 could model with good accuracy plots 1N and 2N losing accuracy in southern plots. On the contrary, Case 3 could model with relatively accuracy plots 1S and 2S losing precision in northern plots. The factor of apparent roughness change can be attributed to the orientation of the rows with respect to

As illustrated in previous paragraphs, the inversion methodologies based on Bayesian approach can be applied to different sensors configurations. In this way different polarizations and/or bands can be exploited to extract soil features. In fact, due to the different way C band or L band signals interact with soil and the above canopy layer, they are sensitive to different surface characteristics. Thus a proper combination of the two bands can help disentangle the

perpendicular to the observation.

the SAR signal [41].

**4.2. Results on the SMEX'02 experiments**


In Fig.9, preliminary results are presented where the different analyzed cases based on various prior conditions are numbered from 1 to 9. In general, for bare soil (fig. 9), the results showed a sensitivity of the algorithms to the different roughness conditions of each plot with a variability of around 5-7% (excluding the extreme cases). The highest variability among the cases is around 40% and is found when the roughness interval is very small (case 2 and 3). When considering a random function for roughness (case 7) and when performing the retrieval over average values of backscattering coefficients (case 5), the mean different with respect to ground measurements is around 15%.

For vegetated areas, due to the limited availability of field measurements (field 5N), the evaluation of the performances is still under work. More extensive results for vegetation are presented for the SMEX'02 experiments.

**Figure 9.** Comparison of SM estimates with measured values. Behavior diagram of the described cases.

Case1 and 3 results are reported in the form of SM maps in fig.10. A detailed analysis of the maps in fig.10 indicated error patterns detected for cases with rows of plots oriented orthog‐ onally to the direction of the sensor observation. As it was observed, the backscattering coefficients for HH polarization is sensitive to the orientation of lines tillage and no inversion algorithms consider this factor. Consequently the results show significant errors in plots perpendicular to the observation.

**Figure 10.** Soil moisture maps for Case 1 (left) and Case 3 (right) over the selected test site.

Case 1 shows that the northern plots with bare soils (1N and 2N) have moisture values very similar to the ground truth. On the contrary, southern plots with bare soils (1S and 2S) have higher moisture values than the measured ones, having the first of them a value of 25%, while the in situ data shown values around 20%. Case 3 shows that plot 1N and 2N obtained moisture values around 15%, which represents an over-estimation of the actual value of around 5%. For plots 1S and 2S, the estimate values are between 22 to 24%. Case 1 could model with good accuracy plots 1N and 2N losing accuracy in southern plots. On the contrary, Case 3 could model with relatively accuracy plots 1S and 2S losing precision in northern plots. The factor of apparent roughness change can be attributed to the orientation of the rows with respect to the SAR signal [41].
