Evaluating Uncertainties in an SM-Based Inversion Algorithm for Irrigation Estimation in a Subtropical Humid Climate

Abstract

Monitoring irrigation is crucial for sustainable water management in freshwater-limited regions. Even though soil moisture (SM)-based inversion algorithms have been widely used to estimate irrigation, scarcity of irrigation records has prevented a thorough understanding of their uncertainties, especially in humid regions. This study assesses the suitability of the SM2RAIN algorithm for estimating irrigation at field scale using high-temporal-resolution data from four corn growing experiments conducted in north-central Florida. Daily irrigation estimates were compared with observations, revealing root mean squared differences of 1.26 to 3.84 mm/day and Nash–Sutcliffe Efficiencies of 0.33 to 0.89. The estimates were more sensitive to uncertainties in static inputs of porosity, saturation moisture and soil thickness than they were to noise in time series inputs. Defining the saturation moisture as porosity made the algorithm insensitive to both parameters, while increasing soil thickness from 40 to 200 mm improved detection accuracies by 34–46%. In addition, the impact of SM on the estimations was investigated based on satellite overpass times. The analysis showed that morning passes produced more accurate estimates for the study site, while evening passes doubled the uncertainty. This study enhances the understanding of the SM2RAIN algorithm for irrigation estimation in subtropical humid conditions, guiding future high-resolution applications.