## 5 Discussion
### Comparison of Our Regression Models with Existing Moisture Indexes  
Regarding the existing soil moisture indexes, we follow the same procedure in the verifications, with quantitative and quantitative validation. For the qualitative comparison, we compare our results with the soil moisture index calculated by Sentinel Playground [26], see Figure 11.  
In Figure 11, we can see that results for both days, 12 June 2021 and 20 September 2021 reflect the same trends observed in the data of soil moisture index. We can see those surroundings above the golf course have higher soil moisture values in both proposed and existing models in September than in June. On the other hand, the large field in the northwest of the picture is dryer in September, and this trend is reflected in both the proposed and existing index. Other areas in which the trends can be observed are the golf course itself and the round area in the northeast of the picture in which we identify a pivot that irrigates the crops (corn), and soil moisture was higher in September. Finally, an advantage of our index is that edifications present very different values, which allows extracting edifications from the analysis (black areas). Nonetheless, in some instances, fields with extremely low irrigations are considered as edifications. Figure 10: Results of quantitative verification in terms of (**a**) graphic bars of estimated and sensed data and (**b**) bivariate correlations. The second part of the comparison is performed comparing the obtained R\({}^{2}\) of the models and the MAE of the verification with the results of other authors. Regarding the papers presented in the related work [10; 11; 12; 13; 14; 15; 16; 17; 18], based on their information, none of them presents the modeled indexes, the R\({}^{2}\), and MAEs to allow a comparison. Very few papers on this topic include details of the verification in these terms. Therefore, we have limited data to compare our results. In [27], [PERSON] et al., the obtained MAEs are between 5.5% and 8.2% using different spatial interpolation methods. In this case, the obtained regression model is characterized by an R\({}^{2}\) of 0.64. On the other hand, [PERSON] et al. [28] used linear regression models to predict soil moisture among the six generated mathematical models. The maximum obtained R\({}^{2}\) was 0.39, and no information about MAE was given. In [29], [PERSON] et al. estimate the soil moisture at 7.5 cm with different remote sensing sources, and the maximum obtained R\({}^{2}\) was 0.28 and the MAEs range from 7% to 11% approximately (this data was obtained from a graphic and the exact values might be slightly different). Figure 11: Comparison of proposed models with the model presented in Sentinel Playground. According to the results of [PERSON] et al. [30], the greatest R\({}^{2}\) was 0.85 with an MAE of 2.4%. Finally, [PERSON] et al. in [31] obtained two correlations characterized by R\({}^{2}\) of 0.45 to 0.49 and MAEs of 3% to 8%. Those are the only found related work in which the R\({}^{2}\) and MAEs are provided for the developed indexes. In our calibrations, we obtained R\({}^{2}\) of 0.46 and 0.70 with MAEs of 3.42% and 2.55% for models calculated with data of satellite with a spatial resolution of 10 m and 20 m, respectively. In the verifications, the MAEs increased to 12.5 and 8.4%. Thus, we can affirm that our models have similar characteristics to the existing models with the lowest MAEs in the calibrations and highest R\({}^{2}\). No papers provide information about the MAEs calculated during the verification phase. Therefore, it is not possible to perform this type of comparison. More information was found regarding other types of studies based on remote sensing data, which provide information about the R\({}^{2}\) and MAEs of the training and validation dataset. In [32]. The authors measured the above-ground biomass of wheat; their results of the validation showed a modification of R\({}^{2}\) (average reduction of 6%) and MAEs (average increase of 11%).