## 1 Introduction
Eddy covariance (EC) techniques have facilitated critical comparisons: [PERSON] et al. [32] analyzed the long-term WUE\({}_{\text{GPP}}\) variability across soybean, maize, winter wheat, and rice systems; [PERSON] et al. [33] conducted wheat-maize double-cropping analyses using data from Luancheng and Weishan stations; and [PERSON] et al. [34] characterized WUE\({}_{\text{GPP}}\) trends at the Jinzhou agroecosystem field experiment station in Shenyang City. Regional remote sensing analyses address spatial limitations of station observations by providing large-scale coverage and continuous spatiotemporal data, enabling multi-source data fusion and model validation. Examples include [PERSON] et al. [35], who mapped the WUE\({}_{\text{GPP}}\) in Saudi Arabian olive plantations; [PERSON] et al. [36], who documented rising WUE\({}_{\text{GPP}}\) trends in China's croplands; and [PERSON] et al. [37], who revealed ecosystem-specific WUE\({}_{\text{GPP}}\) gradients in southwestern China from 2000 to 2017, following the order of forestland > scrub > cropland > grassland. Drawing on findings from CWUE research in agroecosystems, optimization irrigation strategies have emerged as a key means to balance water conservation and enhance agricultural productivity. Crop water consumption is quantified by the cumulative ET during the growing season. For instance, research by [PERSON] et al. [38] demonstrated that optimizing irrigation scheduling improved WUE by 10-25% in wheat-maize rotation systems in the North China Plain. Field evidence suggests that controlled deficit irrigation strategies can maximize WUE through medium water stress [39]. Conversely, over-irrigation has been shown to decrease both the yield and WUE [40]. The accurate quantification of ET fluxes and biomass accumulation patterns remains the basis for reliable WUE assessments, as emphasized by multi-method validation studies [40, 41]. While previous studies have advanced our understanding of the spatiotemporal variability in cropland ecosystems, limited research has focused on the impacts of irrigation on cropland CWUE and the synergistic effects of drivers on CWUE in irrigated cropland. Irrigation plays a critical role in sustainable agriculture [42], as it significantly alters the soil moisture distribution and modifies the wetting patterns in the crop root zone. These changes directly influence the root water and nutrient uptake [43], hereby affecting cropland CWUE. Consequently, effective irrigation water management is essential for optimizing the crop system performance [44]. As a major grain-producing region in China, Henan Province is of strategic importance to national food security [45], highlighting the need for an in-depth investigation into the spatiotemporal dynamics of its cropland CWUE and the underlying driving mechanisms. Therefore, this study aims to (1) analyze the spatiotemporal changes in cropland CWUE across Henan Province during 2001-2019; (2) quantitatively evaluate the impact of irrigation on CWUE; and (3) systematically examine the synergistic effects of driving factors on the CWUE in the region. The structure of this study is organized as follows: Section 3.1 analyzes the spatiotemporal changes in the cropland CWUE, GPP, NPP, and ET, Section 3.2 presents a quantitative evaluation of the impact of irrigation on CWUE in cropland, and Section 3.3 explores the synergistic effects of multiple driving factors on CWUE in irrigated cropland.