prevented _in situ_ data collection during the spring, a ground survey for the 2022 crop could eventually be organized in June 2022 and is still ongoing at the time of writing.

All optical data from Sentinel-2 and SAR data from Sentinel-1 during the vegetation period was then used to generate crop classification map using a convoluted neural network on the Amazon Web Services cloud computing platform (Kussul _et al._ 2017; Shelestov _et al._ 2020) as well as a random forests classifier on the Google Earth Engine (GEE) platform (Shelestov _et al._ 2017).<sup>13</sup> For classifier training, half of the data was randomly assigned to training and independent validation samples and accuracies calculated based on independent validation dataset for each of the crops received (Kussul et al. 2018). For 2022, Sentinel-2 data was used to create a winter crop mask for the 2022 cropping season by computing the maximum value of vegetation index NDVI for the entire territory of Ukraine from Feb. 1 to May 31 on GEE and applying a threshold segmentation for winter crop mask creation.<sup>14</sup> Table 2 shows the number of training samples for winter and summer crops collected each year as well as the F1 scores for winter crop classification maps which exceeds 95% in each of the years. Maps of the estimated winter crop area by VC generated on this basis as displayed in figure 2 illustrate a concentration of winter crops in the country’s South and East and suggest a much lower level of winter crop cover in 2022 and to some extent in 2020 than in 2021 and 2019.

Panel A of table 3 supports this by showing that, with 8.38 mn ha, area cultivated with winter crops in 2022 is indeed 11% below the 2019-21 average but well above the 7.5 mn. ha attained in 2020 when adverse weather conditions led to widespread winter crop failure, especially in the southern and central part of the country. Figures at national level are in line with those reported in the JRC’s MARS bulletin (Ben Aoun _et al._ 2022) who attribute most of the changes to differences in weather, highlighting the importance of controlling for such factors before attributing changes over time to conflict.<sup>15</sup> A first step towards assessing direct conflict impacts is to compare the deviation of 2022 winter crop area from the 2019-21 level between VCs that were affected by the different types of conflict and those that were not. Results from doing so, reported in panel B of table 2 point towards three relevant factors. First, in conflict-affected villages, winter crop area is more than double that of non-affected ones, suggesting that conflict disproportionately affected villages with high winter crop potential.

Second, winter crop area in VCs where crops were damaged directly is significantly lower (by 15.7%) than in VCs that did not experience such direct damage (where the decline was 10.7%), suggesting that interpreting satellite imagery provides additional information. Third, none of the other open-source based

> 13 See Kussul _et. al_ (2016 and 2017) for a detailed description of methodology for crop mapping based on satellite data. As the classification map based on a deep learning model on AWS is more accurate at small fields, it is used below. 14 Threshold segmentation was based on maximum NDVI values and to separate winter cereal and rapeseed, an additional band index was calculated to detect the characteristic yellow flowering of winter rapeseed (d’Andrimont _et al._ 2020).

> 15 The importance of adjusting for time-varying factors is illustrated by Schierhorn _et al._ (2021) who find that weather and climate variables explain 54% of country-level winter wheat yield variability in Ukraine.

9