A Rapid Assessment Framework to monitor harvest progress in Ukraine
Authors: S Nair Shabarinath , Becker Reshef Inbal , Wagner Josef , Sadeh Yuval , Hosseini Mehdi , Khabbazan Saeed , Skakun Sergii , Munshell Blake , Baber Sheila , Duncan Erik , Li Fangjie , Sahajpal Ritvik , Kalecinski Natacha , Baker Brian , Humber Michael 
Published: February 2023

The Russian forces invaded Ukraine on 24th February 2022 leading&#160; to widespread disruption of Ukraine's agricultural system. Ukraine is a major exporter of crops , the invasion therefore poses a significant risk to global food security. Quantifying the extent of this impact is critical, and requires monitoring of Ukraine&#8217;s agricultural lands. Total production is one of the prime indicators in this regard. Production in turn is directly proportional to the total harvested area.&#160;&#160;Harvested areas at regional scales have previously been estimated from satellite data. The majority of these studies use a complete satellite derived phenological time series and make the assumption that senescence leads to harvest. Both these conditions are not applicable in this case, as harvest estimates are required in-season and all planted fields would not necessarily be harvested due to the conflict . A delayed harvest also results in a long browning phase prior to harvest, making it particularly difficult to differentiate from post-harvest signatures.&#160;&#160;Given these constraints and challenges, we developed a method to monitor crop harvest near-real time using high resolution Planet satellite imagery. Our method includes training a model to cluster change patterns on historic data and then identify harvest patterns in the current season. Samples used to train the model consist of information from two consecutive images. Such samples are collected across the season and spatially across four&#160; agro-climatic zones, ensuring we capture a complete representation of change patterns that exist. Clusters are assigned as &#8216;harvested&#8217; or &#8216;non-harvested&#8217; by visually inspecting imagery at a higher temporal resolution, using which,&#160; harvest can be seen as a clear change event. On clusters which are not fully separable, we apply a hierarchical approach to further separate them. Our method works in the absence of extensive training labels and does not use predefined thresholds or assumptions. We applied the method across the harvesting period for winter crops in Ukraine.&#160;&#160;Contrary to initial reports and expectations we found a higher percentage of harvested fields in Ukraine. In free Ukraine we found 94% of planted winter crops to be harvested and in occupied Ukraine it was 88% as of 19th September 2022. Strong visual patterns of non-harvested crops were observed along the occupation borders in eastern and southern Ukraine. Harvesting trends in the north and south were largely unaffected by the conflict. With no possibility to collect ground samples, we visually interpreted satellite imagery at a higher temporal frequency to generate statistically significant validation data for model accuracy calculation. We obtained an overall accuracy of 85% with an f1-score of 90% for the harvested class and 73% for the non-harvested class. Our assessments and analysis were directed to different organizations and agencies dealing with the Ukraine crisis and led to several key insights and derived interpretations.Following NASA EarthObservatory article was published based on this work: https://earthobservatory.nasa.gov/images/150590/larger-wheat-harvest-in-ukraine-than-expected&#160;&#160;&#160;