{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T23:13:18Z","timestamp":1776294798335,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032168078","type":"print"},{"value":"9783032168085","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-16808-5_2","type":"book-chapter","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T22:23:00Z","timestamp":1776291780000},"page":"15-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Case Study of Detecting Nutrient Deficiencies in Corn Using Multispectral Satellite Imagery"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6127-3686","authenticated-orcid":false,"given":"Gintautas","family":"\u0160edys","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7963-285X","authenticated-orcid":false,"given":"Mantas","family":"Luko\u0161evi\u010dius","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ernestas","family":"Petrauskas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Art\u016bras","family":"Gotceitas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6306-3143","authenticated-orcid":false,"given":"Ernestas","family":"Zaleckas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martynas","family":"Grei\u010dius","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"issue":"12","key":"2_CR1","doi-asserted-by":"publisher","first-page":"4747","DOI":"10.1109\/JSTARS.2018.2878502","volume":"11","author":"M Ameline","year":"2018","unstructured":"Ameline, M., Fieuzal, R., Betbeder, J., Berthoumieu, J.F., Baup, F.: Estimation of corn yield by assimilating SAR and optical time series into a simplified agrometeorological model: from diagnostic to forecast. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(12), 4747\u20134760 (2018). https:\/\/doi.org\/10.1109\/JSTARS.2018.2878502","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"2","key":"2_CR2","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/s11119-020-09733-3","volume":"22","author":"F Argento","year":"2021","unstructured":"Argento, F., Anken, T., Abt, F., Vogelsanger, E., Walter, A., Liebisch, F.: Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data. Precis. Agric. 22(2), 364\u2013386 (2021). https:\/\/doi.org\/10.1007\/s11119-020-09733-3","journal-title":"Precis. Agric."},{"issue":"3","key":"2_CR3","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/s11119-021-09861-4","volume":"23","author":"BW Burns","year":"2022","unstructured":"Burns, B.W., et al.: Determining nitrogen deficiencies for maize using various remote sensing indices. Precis. Agric. 23(3), 791\u2013811 (2022). https:\/\/doi.org\/10.1007\/s11119-021-09861-4","journal-title":"Precis. Agric."},{"issue":"17","key":"2_CR4","doi-asserted-by":"publisher","DOI":"10.3390\/rs16173183","volume":"16","author":"A Htitiou","year":"2024","unstructured":"Htitiou, A., M\u00f6ller, M., Riedel, T., Beyer, F., Gerighausen, H.: Towards optimising the derivation of phenological phases of different crop types over Germany using satellite image time series. Remote Sens. 16(17), 10. 3390\/rs16173183 (2024) https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3183","journal-title":"Remote Sens."},{"issue":"8","key":"2_CR5","doi-asserted-by":"publisher","first-page":"10646","DOI":"10.3390\/rs70810646","volume":"7","author":"S Huang","year":"2015","unstructured":"Huang, S., et al.: Satellite remote sensing-based in- season diagnosis of rice nitrogen status in Northeast China. Remote Sens. 7(8), 10646\u201310667 (2015). https:\/\/doi.org\/10.3390\/rs70810646. https:\/\/www.mdpi. com\/2072-4292\/7\/8\/10646","journal-title":"Remote Sens."},{"key":"2_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.104063","volume":"132","author":"Y Huang","year":"2024","unstructured":"Huang, Y., Liu, N., Wagner Hokanson, E., Hansen, N., Townsend, P.A.: exploring the potential of multi-source satellite remote sensing in monitoring crop nutrient status: a multi-year case study of cranberries in Wisconsin, USA. Int. J. Appl. Earth Obs. Geoinf. 132, 104063 (2024). https:\/\/doi.org\/10.1016\/j.jag.2024.104063. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1569843224004175","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"108860","DOI":"10.1016\/j.fcr.2023.108860","volume":"294","author":"J Jiang","year":"2023","unstructured":"Jiang, J., et al.: Combining UAV and Sentinel-2 satellite multi-spectral images to diagnose crop growth and N status in winter wheat at the county scale. Field Crop Res. 294, 108860 (2023). https:\/\/doi.org\/10.1016\/j.fcr.2023.108860. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0378429023000539","journal-title":"Field Crop Res."},{"issue":"6","key":"2_CR8","doi-asserted-by":"publisher","first-page":"2592","DOI":"10.1007\/s11119-023-10054-4","volume":"24","author":"AM Lapaz Olveira","year":"2023","unstructured":"Lapaz Olveira, A.M., et al.: Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen. Precis. Agric. 24(6), 2592\u20132606 (2023)","journal-title":"Precis. Agric."},{"key":"2_CR9","unstructured":"Lapaz Olveira, A., et al.: New Vegetation Indices for Satellite Monitoring of the Nitrogen Nutrient Index in Corn (2022)"},{"issue":"3","key":"2_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/rs15030824","volume":"15","author":"A Lapaz Olveira","year":"2023","unstructured":"Lapaz Olveira, A., et al.: Monitoring corn nitrogen concentration from radar (C-SAR), optical, and sensor satellite data fusion. Remote Sens. 15(3), 10.3390\/rs15030824 (2023) https:\/\/www.mdpi.com\/ 2072\u20134292\/15\/3\/824","journal-title":"Remote Sens."},{"issue":"9","key":"2_CR11","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1016\/j.rse.2010.04.004","volume":"114","author":"F Meggio","year":"2010","unstructured":"Meggio, F., Zarco-Tejada, P., N\u00fa\u00f1ez, L., Sepulcre-Cant\u00f3, G., Gonz\u00e1lez, M., Mart\u00edn, P.: Grape quality assessment in vineyards affected by iron deficiency chlorosis using narrow-band physiological remote sensing indices. Remote Sens. Environ. 114(9), 1968\u20131986 (2010). https:\/\/doi.org\/10.1016\/j.rse.2010.04.004. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0034425710001173","journal-title":"Remote Sens. Environ."},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1002\/9780470027318.a1018","volume-title":"Encyclopedia of Analytical Science","author":"BG Osborne","year":"2007","unstructured":"Osborne, B.G.: Near-infrared spectroscopy in food analysis. In: Worsfold, P., Townshend, A., Poole, C. (eds.) Encyclopedia of Analytical Science, pp. 195\u2013204. Elsevier (2007). https:\/\/doi.org\/10.1002\/9780470027318.a1018"},{"issue":"14","key":"2_CR13","doi-asserted-by":"publisher","first-page":"5191","DOI":"10.1002\/jsfa.10568","volume":"100","author":"A Sharifi","year":"2020","unstructured":"Sharifi, A.: Remotely sensed vegetation indices for crop nutrition mapping. J. Sci. Food Agric. 100(14), 5191\u20135196 (2020). https:\/\/doi.org\/10.1002\/jsfa.10568. https:\/\/scijournals.onlinelibrary.wiley.com\/doi\/abs\/10.1002\/jsfa.10568","journal-title":"J. Sci. Food Agric."},{"issue":"10","key":"2_CR14","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1080\/01904160701615533","volume":"30","author":"L Shou","year":"2007","unstructured":"Shou, L., Jia, L., Cui, Z., Chen, X., Zhang, F.: Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat. J. Plant Nutr. 30(10), 1669\u20131680 (2007). https:\/\/doi.org\/10.1080\/01904160701615533","journal-title":"J. Plant Nutr."},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Wu, L., Gong, Y., Bai, X., Wang, W., Wang, Z.: Nondestructive determination of leaf nitrogen content in corn by hyperspectral imaging using spectral and texture fusion. Appl. Sci. 13(3) (2023). https:\/\/doi.org\/10.3390\/app13031910. https:\/\/www.mdpi.com\/2076\u20133417\/13\/3\/1910","DOI":"10.3390\/app13031910"}],"container-title":["Communications in Computer and Information Science","Information and Software Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16808-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T22:23:01Z","timestamp":1776291781000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16808-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032168078","9783032168085"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16808-5_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information and Software Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icist2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icist.ktu.edu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}