## 3 Results
### Validation of Satellite-Derived Soil Moisture  
Validating the satellite-derived soil moisture was critical in assessing the reliability of remote sensing technologies for monitoring soil water dynamics in the avocado orchard. This analysis compared field-measured soil moisture data with satellite estimates to evaluate the effectiveness of satellite-based soil moisture assessments, particularly for different soil depths (30 cm, 60 cm, and 120 cm). The statistical significance of the Pearson correlation coefficients (r) was consistently evaluated throughout the analyses, using a threshold of \(p<0.01\). Correlations meeting this threshold are identified with an asterisk (*) in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Top 10 Sensor Correlations**} & \multicolumn{3}{c}{**Bottom 10 Sensor Correlations**} \\ \hline
**Rank** & **Sensor** & **Correlation (r)** & **Rank** & **Sensor** & **Correlation (r)** \\ \hline
1 & L-S5-30 & 0.8237 * & 41 & G-S7-12 & \(-\)0.147 \\
2 & I-S6-60 & 0.7867 * & 42 & C-S6-60 & \(-\)0.1464 \\
3 & F-S8-30 & 0.7297 * & 43 & H-S4-12 & \(-\)0.1358 \\
4 & I-S8-30 & 0.7189 * & 44 & H-S2-30 & \(-\)0.1341 \\
5 & M-S5-30 & 0.6949 * & 45 & F-S5-30 & \(-\)0.1329 \\
6 & H-S7-12 & 0.6828 * & 46 & H-S3-60 & \(-\)0.1189 \\
7 & J-S6-60 & 0.6615 * & 47 & J-S3-60 & \(-\)0.106 \\
8 & H-S5-30 & 0.659 * & 48 & B-S2-30 & \(-\)0.1054 \\
9 & L-S7-12 & 0.6448 * & 49 & O-S6-60 & \(-\)0.1039 \\
10 & E-S8-30 & 0.6319 * & 50 & B-S3-60 & \(-\)0.1022 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Top 10 and bottom 10 correlations between field-measured soil moisture data and satellite-derived estimates for 2021 and 2022 seasons. Figure 5: Temperature, precipitation, and solar radiation variability for 2021 and 2022. Field vs. Satellite Soil Moisture  
A detailed comparison was conducted between field-measured soil moisture data obtained from sensors at three different soil depths (30 cm, 60 cm, and 120 cm) and satellite-derived estimates. The comparison revealed that the strongest correlations between field and satellite data occurred at a depth of 30 cm for both the 2021 and 2022 seasons (Table 1). The correlation analysis supports the hypothesis that the 30 cm depth is particularly effective for capturing soil moisture dynamics, as it reflects both near-surface soil moisture variations and deeper water retention, which can be effectively monitored through remote sensing. The variability in correlations between soil moisture sensor data and satellite-derived estimates across depths likely reflects differences in sensor placement and environmental factors. Shallow sensors show stronger correlations with remote sensing data due to their sensitivity to surface-level moisture changes influenced by precipitation and evapotranspiration. In contrast, deeper sensors reflect longer-term hydrological trends less influenced by short-term meteorological conditions. Differences in soil texture and microclimate further contribute to water retention and drainage variability across soil depths. While these aspects were not the primary focus of this study, they underscore the complexity of integrating field and satellite data. In contrast, sensors with weaker or negative correlations, such as G-S7-12 and B-S3-60, showed lower accuracy in capturing soil moisture variations, emphasizing the importance of selecting the appropriate sensor depth and configuration for reliable data acquisition. The previous analysis is complemented by Figure 6, which visually highlights the performance of the top 10 and bottom 10 sensors based on their correlations with satellite-derived soil moisture data from the 2021 and 2022 seasons. The bar plots on the left depict the sensors with the highest positive correlation coefficients, emphasizing those that closely align with satellite measurements, while the plots on the right display the sensors with the lowest or negative correlation coefficients, indicating a weaker or inverse relationship.