## 1 Introduction  
Irrigation is typically performed in areas with arid climates, low precipitation, and/or frequent droughts, which makes water management both complex and important. On a continental scale, irrigation consumes approximately 86% of freshwater withdrawals in Africa [1]. This is already higher than the global average and is expected to increase with increasing prosperity and therefor food demands. Water governance in Africa must be performed in such a way to provide for increasing demands in food and simultaneous increasing demands from industry, the environment, and municipalities. Quantifying water balance components and land productivity for irrigation schemes scales has a wide variety of applications. This includes but is not limited to initiating and evaluating water conservation practices, evaluating equitable water distribution [2; 3; 4; 5], assessing water and land productivities [6; 7], input to water policy and resource management [8; 9], and improving irrigation management and systems [2; 10]. Remote sensing is a powerful tool to understand agricultural performance at high spatial and temporal resolutions. The application of remote sensing in estimating agricultural performance indicators is becoming more prolific as it provides more information, in both time and space, than can be provided by traditional methods, such as water balance or ground measurements [11]. Remote sensing can provide insight into various aspects of agricultural production, including estimation of actual evapotranspiration (ETa) and biomass production. With the increase in open access to satellite imagery and retrieval algorithms, remote sensing provides effective yet spatially and temporally extensive options to estimate agricultural indices, which are especially beneficial for evaluating irrigation performance in data-scar regions, like Africa. The use of remote sensing to estimate and evaluate various indicators for irrigation performance at the irrigation scheme level (i.e., the irrigation perimeter) has been tested and reported for multiple spatial and temporal resolutions (e.g., spatial resolution refers to the pixel size, and temporal resolution refers to the satellite revisit time). These include studies on indicators including equity [2; 6; 12], adequacy [6; 13; 14], sustainability [2; 13], and water productivity [15; 16; 17]. These studies used datasets with input sensors of varying resolution, including Landsat TM with a resolution of 30 m and 16-day revisit and moderate resolution imaging spectroradiometer (MODIS) Terra with resolutions of 250 m and 1-day revisit or 500 m and 8-day revisit. The accuracy of these studies is often not reported, and when it is, it varies considerably [11]. Studies utilising global datasets show a large range in errors in ETa, net primary productivity (NPP), or biomass production and crop water productivity (CWP) [11]. Local studies, with parametrised models typically have a higher accuracy. However, it is not clear how the resolution of the dataset influences the accuracy or the potential application of irrigation performance indicators. For example, at what resolution can different indicators be assessed for a given scale of application (e.g., inter- or intra-scheme) or a given scale of the irrigation scheme (e.g., plots size)? The variation in the satellite revisit period can lead to different irrigation performance indicator values when interpolating images, particularly in rainy periods and during the growing season [18]. Uncertainty in ETa of up to 40% has been attributed to the difference in a 16-day revisit as compared to 4-day revisit, depending on climate and season with no assimilation of daily meteorological data [19]. Likewise, spatial resolution is highly important when using non-linear models in heterogeneous areas due to pixel purity [20; 21]. Therefore a higher spatial resolution, when analysing irrigation performance indicators, is expected to improve the assessment in areas of higher spatial heterogeneity [22].