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Estimating irrigation water use accurately is critical for sustainable irrigation and studying terrestrial water cycle in irrigated croplands. However, irrigation is not monitored in most places, and current estimations of irrigation water use has coarse spatial and/or temporal resolutions. This study aims to estimate irrigation water use at the daily and field scale through the proposed model‐data fusion framework, which is achieved by particle filtering with two configurations (concurrent, CON, and sequential, SEQ) by assimilating satellite‐based evapotranspiration (ET) observations into an advanced agroecosystem model, ecosys. Two types of experiments using synthetic and real ET observations were conducted to study the efficacy of the proposed framework for estimating irrigation water use at the irrigated fields in eastern and western Nebraska, United States. The experiments using synthetic ET observations indicated that, for two major sources of uncertainties of ET difference between observations and model simulations, which are bias and noise, noise had larger impacts on degrading the estimation performance of irrigation water use than bias. For the experiments using real ET observations, monthly and annual estimations of irrigation water use matched well with farmer irrigation records, with Pearson correlation coefficient (r) around 0.80 and 0.50, respectively. Although detecting daily irrigation records was very challenging, our method still gave a good performance with RMSE, BIAS, and r around 2.90, 0.03, and 0.4 mm/d, respectively. Our proposed model‐data fusion framework for estimating irrigation water use at high spatio‐temporal resolution could contribute to regional water management, sustainable irrigation, and better tracking terrestrial water cycle. Key Points: Combining remotely sensed evapotranspiration (ET) and an agroecosystem model is effective to estimate irrigation water use at daily and field scaleFor two sources of ET uncertainties, noise has larger impacts on degrading the estimation performance of irrigation water use than biasRemotely sensed ET observations and ET simulations from ecosys with high accuracy can improve the estimations of irrigation water use [ABSTRACT FROM AUTHOR]

Understanding plant water stress (PWS) in the soil‐plant‐atmosphere‐continuum (SPAC) that connects water supply from soil, water demand from atmosphere, and plant self‐regulation is a prerequisite for efficient irrigation in response to water scarcity. Currently, PWS can be defined in various ways, for example, based on environmental factors and/or plant‐centric metrics. The environment‐based metrics usually do not take plants into consideration. Regarding the existing plant‐centric metrics, their interconnections and abilities to capture the physical water constraints from both soil water supply and atmospheric water demand are still unclear. This research investigates the theoretical foundations behind different PWS metrics, and assesses their efficacy and potentials for irrigation scheduling. This study first investigated the interconnections among different PWS metrics and the co‐regulation of soil moisture and vapor pressure deficit (VPD) on the plant‐centric metrics through an advanced process‐based model, ecosys. We then use ecosys to test different PWS metrics' performance in guiding irrigation in terms of water use, maize yield, and economic profits. The case study was conducted at sites across a dramatic rainfall gradient in Nebraska, the largest irrigation state in the United States Corn Belt. The ecosys simulation indicates that canopy water potential and stomatal conductance (gs) are the most effective plant‐centric metrics in the SPAC system in indicating PWS. In addition, our findings show that using the plant‐centric metrics‐based irrigation schemes, which capture the co‐regulation of soil moisture and VPD, can improve producers' economic profits through water savings. Key Points: We conducted a systematic study to interpret six plant water stress (PWS) metrics and to assess their efficacy for guiding irrigationCanopy water potential and stomatal conductance are the most effective PWS metrics to characterize co‐regulation of soil moisture and vapor pressure deficitThe plant‐centric irrigation schemes could save irrigation water use compared with soil‐moisture‐only metrics [ABSTRACT FROM AUTHOR]