## 1 Introduction  
Remote sensing has proven to be a suitable instrument for land use classification and land surface monitoring. The time series of remote sensing data allow for detecting land use change and changes in agricultural patterns and management practices. Agriculture uses vast amounts of natural resources such as fresh water for irrigation in an often-non-sustainable way [1]. To secure current and future global food supplies in a sustainable way, agriculture has to increase the efficiency of the water it uses, which is expressed in the principle of \"more crop per drop\" [2]. Developing and finally establishing monitoring capabilities for agricultural irrigation and its efficiency therefore constitutes a major prerequisite towards improving the efficiency, effectiveness and sustainability of agricultural water use. Remote sensing is the central data source for a quantitative global, regional and local monitoring of areal extent, timing and technique of irrigation. The Copernicus Sentinel missions provide, on an operational basis, high spatial and temporal resolution data. Together with increasing computing capacities, they extend our Earth observation capacities to develop and deploy the monitoring systems necessary to achieve the necessary efficiency gains in irrigation. Since approx. 20% of the total cropland is irrigated and approx. 40% of the world food is produced on this cropland, irrigation plays a crucial role in global food production [3]. Irrigated cropland consumes 69% of the global water withdrawal from surface and groundwater [4]. Global irrigated area doubled in the last 50 years [3] and future expansion is expected [5,6]. Over 50% of the irrigated areas are located in regions characterized by annual precipitation smaller than 750 mm, which is considered the limit below which diverse demands for water may lead to allocation conflicts [7; 8]. Additionally, the impact of climate change may regionally influence precipitation and snowmelt patterns and, consequently, river flows and groundwater recharge, and may thus reduce the availability of fresh water, aggravate water scarcity and amplify water-related conflicts [8]. The high demand for irrigation water strains the regional hydrological cycle mainly through withdrawal from local and regional rivers and aquifers. Invigation water is diverted into the atmosphere and, as a result, lost for further downstream uses. Dramatic reductions in run-off and aquifer levels caused by irrigation with adverse effects for the regional environment and for the downstream population are documented worldwide [9; 10; 11; 12]. It is, therefore, crucial to monitor, with high accuracy, the location and extent of irrigated area. Mapping of irrigated areas still represents a challenge for remote sensing. Several studies have shown the feasibility of mapping irrigated areas using remote sensing data from the local to global scale [13; 14; 15; 16; 17; 18]. Existing irrigation mapping methods combine different data to exclude rain-fed and irrigated land by strong indicators such as evapotranspiration [19], climatic conditions [17], thermal variations over an irrigated field [20] or soil moisture [21]. The few existing global studies about irrigated areas show large differences in its extent and spatial pattern and are subject to controversial discussions in the scientific community [22; 23]. The differences are caused by different assumptions and definitions, different time periods and data from different satellite missions with different spatial resolution and spectral coverage. This study focusses on the influence of spatial resolution of remote sensing data on the resulting location and extent of derived irrigated areas. [PERSON] et al. [24] already showed, in a case study, that finer spatial resolution can result in an increase in classified irrigated area. They conclude that current operational irrigation monitoring systems, which are based on coarser resolution imagery from, e.g., AVHRR, MODIS or PROBA-V, neglect relevant parts of the global irrigated area [24].