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A small-scale field test of soil salinity control is demonstrated that fuses multisensor array data, state estimators, and optimization algorithms to autonomously maintain specified salt levels at a prescribed soil depth. Soil salinization is a problem associated with irrigation in arid and semi-arid environments; even more so when reclaimed water is applied. Salinity conveyed by the irrigation water tends to accumulate in the soil, and can necessitate transitioning to more salt-tolerant crops. Left unchecked, soil salinization will eventually render a soil non-arable. In this work, vertical multisensor arrays and physically-based state prediction models were incorporated in an optimal irrigation management scheme known as Receding Horizon Control (RHC) to balance reclaimed water reuse and soil/groundwater quality. A simulation model including a one- (vertical) dimensional form of the Richards equation coupled to energy and solute transport equations is employed to estimate states (soil moisture, temperature, and soil salinity) using a least squares method. The optimization algorithm determines the optimal irrigation rate using current state status which continuously maximizes the reclaimed water usage while maintaining soil salinity at a certain level. The finding suggests that real-time application of sensor networks for environmental monitoring can be achieved in the context of RHC not only to understand current environmental problems but also to autonomously control negative impacts on environmental quality when reclaimed water is reused for irrigation.