Field water use efficiency is an important parameter for evaluating the quality of field irrigation in irrigated areas, which directly affects the country’s food security and water resource allocation. However, most current studies use point-scale soil moisture (SM) or remote sensing water balance models to calculate the field water use coefficient, which cannot avoid errors caused by the spatial heterogeneity of SM and insufficient spatial resolution of remote sensing data. Therefore, in this study, the cosmic-ray neutron sensor (CRNS), Time-Domain Reflectometers (TDR) and Automatic Weather Stations (AWS) were used to monitor the meteorological and hydrological data such as SM, atmospheric pressure, and precipitation in the experimental area of Jinghuiqu Irrigation District for three consecutive years. The scale of the CRNS SM lies between the point and the remote sensing. Based on the CRNS SM, the calculation method for canal head and tail water was used to calculate the field water use efficiency to evaluate the level of agricultural irrigation water use in the experimental irrigation area. The results showed that CRNS could accurately detect the change in SM, and four irrigation events were monitored during the winter wheat growth period from October 2018 to June 2019; the calculation result of field water use efficiency in the experimental area was 0.77. According to the field water use efficiency of the same irrigation area from October 2013 to October 2015 in other studies, the field water use efficiency during the growing period of winter wheat in this area increased from 0.503 to 0.770 in 2013–2019, indicating a significant improvement in the field water use level. In general, this study not only solves the problem of low calculation accuracy of field water use efficiency caused by the mismatch of SM monitoring scales but also explores the application potential of CRNS in agricultural irrigation management and water resource allocation.

Irrigation alleviates drought in croplands and maintains or increases crop yields. The accurate monitoring of irrigated areas is important to regional water resource management, food security, climate change, drought monitoring, and emergency disaster relief. Based on field experiments that demonstrate the feasibility of irrigated area mapping using land-surface temperature, we propose a method to map spring irrigation areas using historical meteorological data, main crop phenological characteristics, irrigation regimes, and MODIS land-surface temperature (LST) products. The distribution of irrigation intensity, spring irrigated areas (SIA, considering the irrigation intensity), and total area of spring irrigation (STIA, regardless of irrigation intensity) were monitored by the proposed method for the Donglei Irrigated District (Phase II) in northwestern China from 2011 to 2018. The spring irrigation of the study area was divided into three periods (16 January–23 February, 24 February–24 March, and 25 March–31 May). Then, the temperature threshold of the irrigated area in each period was determined by the diurnal temperature range (DTR) of the rain-fed plots and precipitation data; for the three periods, this was 12 °C, 15 °C, and 11 °C, respectively. The results showed that most of the croplands in the study area were irrigated once or twice. The SIA in most years varied between 55,900 and 73,100 ha, but in 2016, the irrigation area reached 100,200 ha. The STIA accounted for 60–70% of the irrigated area except 2016. The average accuracy of SIA monitoring was satisfactory and above 94% for years when sufficient and reliable data was available.