{"id":2815,"date":"2025-09-05T17:09:32","date_gmt":"2025-09-05T17:09:32","guid":{"rendered":"https:\/\/wp-productionenv-bjg9h2g2bgg5b8aa.southeastasia-01.azurewebsites.net\/news\/ohb-demonstrates-improved-monitoring-of-grassland-and-sugar-beets-from-space\/"},"modified":"2025-09-05T17:09:32","modified_gmt":"2025-09-05T17:09:32","slug":"ohb-demonstrates-improved-monitoring-of-grassland-and-sugar-beets-from-space","status":"publish","type":"post","link":"https:\/\/starpath.global\/news\/ohb-demonstrates-improved-monitoring-of-grassland-and-sugar-beets-from-space\/","title":{"rendered":"OHB Demonstrates Improved Monitoring of Grassland and Sugar Beets from Space"},"content":{"rendered":"

\"OHB<\/p>\n

OHB System AG<\/strong> aims to demonstrate in the HyLAP project (Hyperspectral Products for Agricultural Practice) how grassland can be optimally managed through hyperspectral satellite data. These data provide insights into plant properties that are invisible to the naked eye \u2013 opening up new possibilities for more precise and resource-efficient farming.<\/p>\n

At the heart of the study is the German hyperspectral satellite EnMAP (Environmental Mapping & Analysis Program), which has been in orbit for three years. With its 242 spectral bands, it captures light from the visible to the shortwave infrared range. Developed and built by OHB on behalf of the German Space Agency at the German Aerospace Center (DLR), EnMAP delivers data of unprecedented quality for scientific research. These data are now being explored for practical applications \u2013 for example, by farmers seeking to manage their fields more precisely.<\/p>\n

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Also Read: What are the Best Practices for optimizing Satellite Orbit Control?<\/h4>\n<\/div>\n

\u201cWith HyLAP, we want to show how hyperspectral data can be meaningfully applied in practice \u2013 even in combination with traditional methods such as field inspections,\u201d<\/em> explains <\/strong>Dr. Anita Bayer<\/strong>, head of OHB\u2019s \u201cEarth Observation Applications\u201d<\/em> team<\/strong>. Together with stakeholders across the agricultural value chain \u2013 including state authorities, agricultural insurers, and private companies \u2013 her team is investigating grassland areas and whether improved monitoring can help to increase the quality and quantity of forage.<\/p>\n

Timing the Cut is a Key Factor in Forage Quality<\/strong><\/p>\n

Grassland is mowed up to six times a year \u2013 but the exact timing is usually based on the farmer\u2019s experience and weather conditions. Laboratory analyses are rare, and guidelines from state agencies rely on local samples. Hyperspectral satellite data, on the other hand, can provide comprehensive information on the nitrogen content of plants \u2013 and thus their protein levels, a key indicator of forage quality. Regular data acquisition could help determine precisely when and where to mow, depending on the animals\u2019 needs: Dairy cows require protein-rich feed, while young animals can manage with less.<\/p>\n

Sugar Beets in Focus \u2013 In Collaboration with KWS<\/strong><\/p>\n

In addition to grassland, the HyLAP team is also focusing on sugar beets in collaboration with the seed company KWS. Here, a unique feature of hyperspectral sensors is leveraged: unlike other satellite-based sensors, they capture a continuous spectrum, which is crucial for determining plant and crop characteristics. This enables new possibilities for precise plant monitoring and sustainable cultivation. The collaboration with KWS is already underway in the ESA<\/strong> project AgriCEM, which prepares the use of data from future satellite missions (especially the Copernicus missions CHIME and LSTM) in agricultural practice.<\/p>\n

HyLAP, by contrast, focuses on using the already available EnMAP data for demonstration purposes. The project started beginning of 2025, runs for 16 months, and is co-financed by the German Space Agency at DLR.<\/p>\n","protected":false},"excerpt":{"rendered":"

OHB System AG aims to demonstrate in the HyLAP project (Hyperspectral Products for Agricultural Practice) how grassland can be optimally managed through hyperspectral satellite data. These data provide insights into plant properties that are invisible to the naked eye \u2013 opening up new possibilities for more precise and resource-efficient farming. At the heart of the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[2],"tags":[77,25,78,20,45],"class_list":["post-2815","post","type-post","status-publish","format-standard","hentry","category-news","tag-command-data-handling-systems","tag-launch","tag-payload-data-handling-system","tag-satellite","tag-sensors"],"acf":[],"_links":{"self":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts\/2815"}],"collection":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/comments?post=2815"}],"version-history":[{"count":0,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts\/2815\/revisions"}],"wp:attachment":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/media?parent=2815"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/categories?post=2815"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/tags?post=2815"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}