## 1 Introduction  
Reliable and timely country-wide crop yield forecasts play an important role in supporting national agricultural policies, food security, and planning of food supplies in countries affected by food shortage [1]. Moreover, crop yield data have been increasingly used to analyze agricultural productivity potential [2, 3], carbon and nitrogen cycles [4, 5], greenhouse gas emissions from agriculture [6], as well as the impact of climate change on agricultural production [7]. In this respect, food shortage has become more frequent due to extreme weather events that are more likely to occur under the changing climate [8]. Remotely sensed satellite data sets offer unique, timely, objective, economical, and spatially homogeneous information on agriculture over vast areas [9]. Therefore, they have been widely used to monitor crop growth and forecast crop yields (e.g., [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]). The comprehensive overview on the utilization of satellite data for agriculture monitoring is given by [PERSON] et al. (2020) [20]. Recently, the Copernicus programme with Sentinel-1 (radar) and Sentinel-2 (optical) satellite constellations has opened a new chapter in monitoring agriculture production on a country level. Coupling data from these two instruments featuring frequent revisit (3-6 days depending on latitude) and high spatial resolution (10-20 m) has proved to provide accurate crop type maps at a regional level [21; 22; 23]. Nevertheless, applicability of Sentinel-2 imagery to forecast crop yields is limited due to a short time series (5 years to date) and due to a lack of consistent crop yield reference data at a field scale across a country. The most common approach to forecast crop yields using satellite data is to build a statistical model that relates annual crop condition anomalies described by vegetation indices, e.g., Normalized Difference Vegetation Index (NDVI) or Fraction of Absorbed Photosynthetically Active Radiation (fAPAR), to national statistics of crop yields [9; 11; 24; 25; 26]. Such an approach benefits from long-term multidecadal satellite observations, which are available from low and moderate resolution (250-1000 m) satellite sensors such as the Advanced Very-High-Resolution Radiometer (AVHRR), Vegetation, or the Moderate Resolution Imaging Spectroradiometer (MODIS). The main drawback of a coarse resolution imagery is related to heterogeneity of pixels which often contain several agricultural fields covered by different crops or even by non-agricultural land. MODIS sensors mounted aboard the Terra and Aqua satellites provide 250 m imagery in the red and near infrared bands which are crucial for vegetation studies. Consequently, many studies incorporated MODIS data to forecast crop yields Refs. [9; 19; 26; 27]. Nevertheless, MODIS instruments operating since 1999 (Terra platform) and 2002 (Aqua platform), several times exceeded the expected 6 years of mission duration. Therefore, in this study data from Sentinel-3 satellite constellation (designed to operate till 2032) to operationally acquire near-real time vegetation indices at 300-m resolution have been employed. However, in order to compute temporal anomalies, the Sentinel-3 time series have been extended with MODIS measurements, re-calibrated to match the Sentinel-3 signal. The ultimate aim of this study is to provide a methodology to estimate crop yields on a country scale during the growing season. This responds to the need of statistical offices (in this case Statistics Poland) that are obliged to provide statistical information on agricultural production. In this context, this study aims to evaluate with what accuracy yield forecasts can be produced using an automated system running on publicly available data.