{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:10:47Z","timestamp":1770682247130,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,9]],"date-time":"2018-02-09T00:00:00Z","timestamp":1518134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)\/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration\/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g\/m2 are reached for the validation of predicted vs. observed dry biomass, while Willmott\u2019s refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g\/m2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time.<\/jats:p>","DOI":"10.3390\/rs10020268","type":"journal-article","created":{"date-parts":[[2018,2,9]],"date-time":"2018-02-09T12:46:27Z","timestamp":1518180387000},"page":"268","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2332-8896","authenticated-orcid":false,"given":"Sebastian","family":"Brocks","sequence":"first","affiliation":[{"name":"Institute of Geography, GIS &amp; RS Group, University of Cologne, 50923 Cologne, Germany"}]},{"given":"Georg","family":"Bareth","sequence":"additional","affiliation":[{"name":"Institute of Geography, GIS &amp; RS Group, University of Cologne, 50923 Cologne, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1080\/01431168908904015","article-title":"Estimation of crop growth from optical and microwave soil cover","volume":"10","author":"Bouman","year":"1989","journal-title":"Int. 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