**4. Discussions**

Concerning the damaged orchards' H-α-A distributions (Figure 5 and Table 1), higher values of H and α in the damaged parcels could be attributed to the changes in vegetation structure (Figure 8). In fact, the inter-row spaces of the damaged orchards, after the storm, were completely covered with the crowns of the broken or uprooted plants, which determined a different scattering geometry. Pre-event plant row geometry was characterized by a regular pattern, which drastically changed to a more disordered one, where the fallen crown elements increased the H values. Since the pre-event scattering mechanism was determined by regularly aligned and spaced plants (rows) alternating with bare soil/grass (inter-rows), it determined intermediate α values. After the storm, it can be assumed that the scattering mechanism was strongly influenced by crown volume, inducing an increase in the α values. Conversely, A appeared to reduce after the event. This could be possibly related to a reduction in the eigenvalue difference λ<sup>1</sup> − λ<sup>2</sup> related to the slightly different scattering mechanism after the storm. The volumetric mechanism appeared to be the prevailing one in the damaged parcels, as proved by the H increase. Since the canopy causes a strong depolarization of the SAR signal, the degree of depolarization (i.e., 1-A) tends to increase with crown closure [18]. Given these interpretation keys, the results obtained seem to support the idea that, after a relevant event able to significantly change vegetation structure, the orchards' polarimetric behavior significantly changes. Based on the collected reference data, damaged orchards tend to show (i) higher values of H and α due to the increased contribution of the volume scattering mechanism, and (ii) lower A values, possibly due to the inter-row closure generated by broken/fallen trees, which increase signal depolarization.

**Figure 8.** A sketch representing orchard condition before (**a**) and after (**b**) the storm. In (**a**) the pattern row/inter-row is well defined; (**b**) after the storm, apple tree uprooting occurred, altering the row/inter-row pattern, and crowns covering the ground increased volumetric scattering.

Concerning the mapping of damaged orchards, the results reported in Table 2 suggest that polarimetric decomposition of S1 data is an effective approach to map orchards affected by a storm, especially during cloudy weather situations. Nevertheless, it is worth stressing that some limitations still persist while working with dual-pol decomposition. In comparison to quad polarization, dual-pol SAR sensors collect half of the scattering matrix components involved in fully polarimetric imagery. Therefore, dual-pol derived products may vary from the classical Wishart distribution. In fact, [57] highlighted that entropy/alpha decomposition using one co-polarization and one cross-polarization does not adequately extract scattering mechanisms in the H-α plane. Nevertheless, Cloude [62] proved how these differences result similarly to the conventional quad-pol one while working with vegetation. In spite of these differences, many operative frameworks were proposed proving how information lost during the dual-pol acquisition can be compensated for enhancing image swath and satellite revisit frequency. Moreover, often quad-pol SAR data are not available free of charge and not readily available for operative purposes. S1 is currently one of the largest space-borne missions providing free and open-access SAR data having high temporal resolution, fitting well with vegetation dynamics monitoring requirement.

Future developments are expected to test if pre- and post-H-α-A differences can be used to semi-automatically detect significance changes. It is worth highlighting that the majority of apple orchards in the study area are covered by plastic nets to protect the trees against hail. Probably, plastic nets can influence the complex permittivity of the analyzed volume and therefore affect the polarimetric response of the observed uprooted trees. Since in the study area, a few fields do not have hail nets, the authors did not survey such orchards, and therefore no assessment looking for the effects of nets on polarimetric response was performed. A specific research should be addressed to assess how plastic hail nets can affect backscattered signal.

#### **5. Conclusions**

In this work, a preliminary assessment about the polarimetric behavior of orchards after a storm was performed. The analysis was aimed at proposing a first methodological approach to detect orchard damage by a storm based on the PolSAR decomposition technique using S1 data. The joint adoption of free accessible S1 data, institutional free auxiliary data (a cadastral map and farmers' CAP application database), and open software (SNAP) constituted a peculiar trait of the proposed approach. It moves in the direction of technological transfer, aiming at making SAR data/techniques an operational tool for agronomic applications, with special concern about weather-related damages to crops, which could be of interest to insurance companies or public administrations. The results proved that storm damages significantly increase the H and α parameters. By contrast, the

A parameter tends to be lower in the damaged orchards. This phenomenon is possibly related to the changes affecting vegetation structure in the damaged fields, where the crowns and branches of fallen/broken plants fill the inter-row space, changing the regular pattern ordinarily characterizing apple orchards. Based on this evidence, the authors proposed a methodology to map possibly damaged orchards that relies on the knowledge about the behavior of witness (and neighboring) undamaged orchards. The method permitted the mapping of the probability that an orchard is damaged or not, constituting a new free tool able to improve orchard monitoring after a calamitous event by regional agencies and insurance companies. It is worth reminding that only apple orchards were considered for this case study. Future developments are expected to test the effectiveness of this method in other orchard types, as pear or peach, which are very diffuse in the AOI.

**Author Contributions:** Conceptualization, S.D.P.; data curation, S.D.P.; formal analysis, S.D.P. and F.S.; investigation, E.B.-M.; methodology, S.D.P. and E.B.-M.; software, S.D.P.; validation, M.G.; visualization, F.S.; writing—original draft, S.D.P., F.S. and M.G.; writing—review and editing, E.T. and E.B.-M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing not applicable.

**Acknowledgments:** We would like to thank Az. Agr. Fessia Franca for having provided ground control data and fundamental operational information useful to reach the results presented in this work.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

