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Abstract. A detailed understanding of variation in soil moisture is needed for developing precision irrigation strategies that can maximize crop production while minimizing consumptive water use. The overarching aim of the study was to optimize the representative location for precision water management with the integration of unsupervised learning and temporal stability analyses. The hypothesis for this objective was that a zone-specific representative sensor location can be determined for variable rate irrigation management in the field. The study was conducted in Tennessee Valley Region of Northern Alabama, USA. The study found that each zone in the field can be represented with a temporally stable location to determine the average zone-specific soil moisture using the concept of unsupervised learning for management zone delineation and temporal stability in soil moisture, which showed that the zone-specific irrigation can be scheduled during the growing season. Farmers can use the dry, wet, and temporally stable locations for establishing the best irrigation scheduling to increase water-use efficiency during the growing season.