<p>The invasion of Ukraine by Russian forces was expected to have global impact on food trade and security, since Ukraine is a breadbasket cereals and oil seeds producer. The NASA Harvest &#171; Rapid Agricultural Assessment for Policy Support &#187; (RAAPS) team was triggered early in the conflict to provide answers to the following questions :&#160;</p><p>(i) How much winter cereals, winter oil seeds and summer crops were planted in Ukraine during the 2021-2022 cropping season?</p><p>(ii) What proportion of those crops fell under the Russian occupied area?&#160;</p><p>(iii) How much cropland was left unplanted in 2022 due to the war?</p><p>As insights had to be produced within season, the NASA Harvest RAAPS team produced the first ever, Ukraine scale in-season crop type map based on Planet Labs 3 meter spatial -, 4 bands spectral -, and daily&#160; temporal &#8211; resolution data.&#160; Since &#160; no &#160; labeled &#160; datasets &#160; were &#160; available &#160; early &#160; enough &#160; in-season&#160; for applying supervised machine learning techniques, cropland was progressively mapped &#160; into &#160; four &#160; classes &#160; (winter &#160; cereals, &#160; rapeseed, &#160; summer &#160; crops &#160; and barren/non cultivated plots), using semi-supervised clustering techniques and heuristical thresholdings. Expert domain&#160; knowledge&#160; allowed to cope&#160; with missing ground truth training data. First, active cropland was separated into winter crops and potential summer crops. K-means clustering of April and May Planet images, followed by visual cluster assignment, allowed to efficiently separate green crops (winter crops) from barren soils (potential summer crops). Then, another K-means clustering allowed to split winter crops into winter cereals and rapeseed as of end of May, based&#160; on the intense yellow flowering signal of the latter. Finally a set of NDVI based heuristics was applied on potential summer crops in order to assess if green-up happened or not. Crops which &#160; did &#160; not &#160; green &#160; up &#160; as &#160; of &#160; the &#160; 11th &#160; of &#160; July &#160; 2022 &#160; were &#160; considered barren/non-planted.&#160;</p><p>Road side ground surveyed crop type information collected in free Ukraine has been provided by Kussul & al. (2022) in August 2022. Validation against this data provided an overall accuracy of 94 % and a mean F1-score of 91 % for winter cereals, rapeseed and summer crops. No unplanted fields&#160; were collected as part of the ground campaign. Several assessments of proportional area per crop type and occupation status were performed throughout the growing season, as occupation boundaries kept moving. As of the 11th of July 2022, 23.03 % of Ukraines cropland was occupied. 55.29 % of all detected barren fields were located within occupied territories, mainly scattered around the front line. 33.9 % of all winter crops were under occupied territory when harvest ready (mid July).&#160;</p><p>This crop type map was used for computing harvested area, estimating yield and &#160; for &#160; production computation. Following NASA EarthObservatory articles were published, &#160; providing &#160; information &#160; to &#160; the &#160; public &#160; and &#160; private &#160; sector : &#160; (i) https://earthobservatory.nasa.gov/images/150025/measuring-wars-effect-on-a-global-breadbasket&#160; &#160; (ii) https://earthobservatory.nasa.gov/images/150590/larger-wheat-harvest-in-ukraine-than-expected&#160;</p>
<p>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;</p><p>&#160;</p><p>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;</p><p>&#160;</p><p>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;</p><p>&#160;</p><p>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.</p><p>Following NASA EarthObservatory article was published based on this work: https://earthobservatory.nasa.gov/images/150590/larger-wheat-harvest-in-</p><p>ukraine-than-expected&#160;&#160;</p><p>&#160;</p>
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