Water shortages in the Western United States will continue to be one of the foremost American agricultural challenges in the coming years. As agriculture is the largest consumer of water in the western US, improvements in irrigation scheduling and modeling are needed to maximize production under limited water. Various satellite-based remote sensing models have been developed to estimate crop water use. However, water balance-based evapotranspiration (ET) models need field-scale irrigation information for initiating the seasonal soil water balance. This initialization has been challenging due to the lack of remotely sensed irrigation event data. In this study, we utilized a recently launched satellite constellation (Planet) with high temporal and spatial resolution data (daily, ~ 3 m) to evaluate if Planet data can facilitate early season irrigation detection. We utilized normalized difference vegetation index (NDVI), moisture index, and individual spectral bands to detect moisture and ultimately infer irrigation. As part of this comparison, a hybrid two-source energy and water balance model BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution) was used to estimate ET with Planet-based vegetation indices and irrigation information. We also compared the results to eddy covariance (EC) located at lettuce fields in Yuma, Arizona in the lower Colorado River basin between 2016 and 2020. Overall, the results indicated that Planet’s data helped to establish the field-scale onset of irrigation, which assisted to initiate soil water balance in the BAITSSS model, thus ultimately improving ET. Further, these results should support the development of near-real-time landscape-scale ET and should be highly beneficial to agricultural communities for sub-field-scale effective water management.