**5. Conclusions**

The proposed σ0 and β0 local incidence angle normalization methods facilitate the use of all the images acquired by the Sentinel-1 constellation under the full range of typical incidence angles. This is supported by an improvement in the correlations between the SAR measurements and crop variables such as LAI, crop height, and Kc following these normalization procedures in three crops: cotton, tomatoes, and wheat. Models based on the suggested normalization of the incidence angle show considerable R<sup>2</sup> and RMSE improvements over the models that were not based on these transformations. This increase in performance is the most notable for the wheat height and LAI models, processing tomato σ0-based LAI and β0-based height models, and the cotton models. Most Kc, LAI, and height models worked well with imagery acquired from ascending and descending orbits, but winter imagery performed better with ascending orbits. This approach to estimate vegetation variables is useful for routine vegetation and agricultural monitoring, having a higher temporal resolution and accuracy than the previous approaches. Despite these results, we wish to stress that the most important achievement is not only the improvement in the models' performance but also the enablement of the conjoint use of images acquired under different incidence angles and even different orbits.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2073-4 45X/10/7/680/s1; Table S1: Sentinel-1 image inventory used in the development of models for wheat. Each line represents a dataset with specific geometrical parameters; Table S2: Sentinel-1 image inventory used in the development of models for cotton. Each line represents a dataset with specific geometrical parameters; Table S3: Sentinel-1 image inventory used in the development of models for processing tomatoes. Each line represents a dataset with specific geometrical parameters; Table S4: Wheat height, LAI, and Kc models; Table S5: Processing tomato height, LAI, and Kc models based on the σ0 normalization method; Table S6: Cotton height and Kc models based on the σ0 normalization method; Table S7: Processing tomato height, LAI, and Kc models based on the β0 normalization methods; Table S8: Cotton height and Kc models based on the β0 normalization method.

**Author Contributions:** Conceptualization, O.R., G.K.; methodology, G.K., software, G.K., L.F., V.L.; validation, G.K.; formal analysis, G.K.; investigation, G.K.; fieldwork, G.K., V.L., L.F., V.S.M.; writing—original draft preparation, G.K., O.R.; writing—review and editing, O.R., G.K., V.S.M., J.T.; visualization, G.K., V.S.M., O.R.; supervision, O.R.; project administration, O.R., J.T.; funding acquisition, O.R., J.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** The cotton field measurements were funded by the Chief Scientist of the Ministry of Agriculture, Israel, under gran<sup>t</sup> number 304-0505, and the wheat and tomato field measurements were funded by the Ministry of Science and Technology, Israel, under gran<sup>t</sup> numbers 3-14559, 3-15605. Gregoriy Kaplan was supported by an absorption gran<sup>t</sup> for new immigrant scientists provided by the Israeli Ministry of Immigrant Absorption. Manivasagam V.S. was supported by the ARO Postdoctoral Fellowship Program from the Agriculture Research Organization, Volcani Institute, Israel.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Sentinel-1 data were obtained from the ESA Copernicus Open Access Hub website (https://scihub.copernicus.eu/dhus/#/home, accessed on 26 June 2021).

**Acknowledgments:** We thank Leonid Dinevich from Tel-Aviv University for his helpful advice. We thank Nitai Haymann for his contribution to software processing and fieldwork. We thank all the growers. We also thank the reviewers.

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