The application of geotechnology for accurately estimating peanut maturity is essential for enhancing crop management, facilitating monitoring, and understanding spatial variability. This study aimed to identify and select the most relevant variables for maturity estimation and to evaluate the performance of a multiple linear regression (MLR) model using those variables. The research was conducted in a commercial field during the 2022/2023 season with the IAC 503 cultivar, which has a 150-day growth cycle. Forty sampling points were evaluated across five dates, beginning 28 days before harvest and ending one day prior to harvest. Maturity was assessed using the Hull-Scrape method. Satellite imagery was obtained from the PlanetScope platform, offering 3-meter spatial resolution and daily temporal coverage. Five cloud-free images corresponding to the sampling dates were selected. Dimensionality reduction was performed using principal component analysis (PCA), followed by stepwise regression to identify the most influential variables. The MLR model, despite its simplicity, achieved high performance with an R² of 0.93. The most relevant variables identified were growing degree days (GDD), green band reflectance, NDVI, and SAVI. These results demonstrate that a small set of well-selected variables is sufficient for accurately estimating peanut pod maturity.