{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T19:37:43Z","timestamp":1769283463811,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005908","name":"German Federal Office for Agriculture and Food","doi-asserted-by":"publisher","award":["281B200716"],"award-info":[{"award-number":["281B200716"]}],"id":[{"id":"10.13039\/501100005908","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cover crops are known to provide beneficial effects to agricultural systems such as a reduction in nitrate leaching, erosion control, and an increase in soil organic matter. The monitoring of cover crops\u2019 growth (e.g., green area index (GAI), nitrogen (N) uptake, or dry matter (DM)) using remote sensing techniques allows us to identify the physiological processes involved and to optimise management decisions. Based on the data of a two-year trial (2018, 2019) in Kiel, Northern Germany, the multispectral sensor Sequoia (Parrot) was calibrated to the selected parameters of the winter cover crops oilseed radish, saia oat, spring vetch, and winter rye as sole cover crops and combined in mixtures. Two simple ratios (SRred, SRred edge) and two normalised difference indices (NDred, NDred edge) were calculated and tested for their predicting power. Furthermore, the advantage of the species\/mixture\u2013individual compared to the universal models was analysed. SRred best predicted GAI, DM, and N uptake (R2: 0.60, 0.53, 0.45, respectively) in a universal model approach. The canopy parameters of saia oat and spring vetch were estimated by species\u2013individual models, achieving a higher R2 than with the universal model. Comparing mixture\u2013individual models to the universal model revealed low relative error differences below 3%. The findings of the current study serve as a tool for the rapid and inexpensive estimation of cover crops\u2019 canopy parameters that determine environmental services.<\/jats:p>","DOI":"10.3390\/rs14184525","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"4525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Estimation of Biomass and N Uptake in Different Winter Cover Crops from UAV-Based Multispectral Canopy Reflectance Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8099-6842","authenticated-orcid":false,"given":"Katja","family":"Holzhauser","sequence":"first","affiliation":[{"name":"Institute of Crop Science and Plant Breeding, Kiel University, 24118 Kiel, Germany"}]},{"given":"Thomas","family":"R\u00e4biger","sequence":"additional","affiliation":[{"name":"Institute of Crop Science and Plant Breeding, Kiel University, 24118 Kiel, Germany"}]},{"given":"Till","family":"Rose","sequence":"additional","affiliation":[{"name":"Institute of Crop Science and Plant Breeding, Kiel University, 24118 Kiel, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5317-7745","authenticated-orcid":false,"given":"Henning","family":"Kage","sequence":"additional","affiliation":[{"name":"Institute of Crop Science and Plant Breeding, Kiel University, 24118 Kiel, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2873-2425","authenticated-orcid":false,"given":"Insa","family":"K\u00fchling","sequence":"additional","affiliation":[{"name":"Institute of Crop Science and Plant Breeding, Kiel University, 24118 Kiel, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1111\/gcb.14644","article-title":"A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity","volume":"25","author":"Abdalla","year":"2019","journal-title":"Glob. Change Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.2136\/sssaj2010.0430","article-title":"Addition of Cover Crops Enhances No-Till Potential for Improving Soil Physical Properties","volume":"75","author":"Mikha","year":"2011","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4736","DOI":"10.1111\/gcb.16269","article-title":"When do cover crops reduce nitrate leaching? A global meta-analysis","volume":"28","author":"Nouri","year":"2022","journal-title":"Glob. Change Biol."},{"key":"ref_4","unstructured":"European Commission (2017). Report from the Comission to the European Parliament and the Council: On the Implementation of the Ecological Focus Area Obligation under the Green Direct Payment Scheme, European Commission."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"152142","DOI":"10.1016\/j.scitotenv.2021.152142","article-title":"Mineralisation of catch crop residues and N transfer to the subsequent crop","volume":"810","author":"Vogeler","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.agee.2005.07.003","article-title":"Replacing bare fallows with cover crops in fertilizer-intensive cropping systems: A meta-analysis of crop yield and N dynamics","volume":"112","author":"Tonitto","year":"2006","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"B\u00f6ldt, M., Taube, F., Vogeler, I., Reinsch, T., Klu\u00df, C., and Loges, R. (2021). Evaluating Different Catch Crop Strategies for Closing the Nitrogen Cycle in Cropping Systems\u2014Field Experiments and Modelling. Sustainability, 13.","DOI":"10.3390\/su13010394"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0065-2113(02)79005-6","article-title":"Catch crops and green manures as biological tools in nitrogen management in temperate zones","volume":"79","author":"Magid","year":"2003","journal-title":"Adv. Agron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s10705-019-10024-1","article-title":"Nitrogen provisioned and recycled by cover crops in monoculture and mixture across two organic farms","volume":"115","author":"Holmes","year":"2019","journal-title":"Nutr. Cycl. Agroecosystems"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1002\/agj2.20340","article-title":"Do diverse cover crop mixtures perform better than monocultures?: A systematic review","volume":"112","author":"Florence","year":"2020","journal-title":"Agron. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.eja.2013.11.012","article-title":"Barley\u2013hairy vetch mixture as cover crop for green manuring and the mitigation of N leaching risk","volume":"54","author":"Tosti","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3796","DOI":"10.3390\/rs4123796","article-title":"Estimation of Evapotranspiration from Fields with and without Cover Crops Using Remote Sensing and in situ Methods","volume":"4","author":"Hankerson","year":"2012","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"303","DOI":"10.2489\/jswc.64.5.303","article-title":"Using satellite remote sensing to estimate winter cover crop nutrient uptake efficiency","volume":"64","author":"Hively","year":"2009","journal-title":"J. Soil Water Conserv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.3389\/fpls.2019.01798","article-title":"High-Throughput Prediction of Whole Season Green Area Index in Winter Wheat with an Airborne Multispectral Sensor","volume":"10","author":"Bukowiecki","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_16","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1029\/2002GL016450","article-title":"Remote estimation of leaf area index and green leaf biomass in maize canopies","volume":"30","author":"Gitelson","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/0034-4257(81)90018-3","article-title":"Remote sensing of total dry-matter accumulation in winter wheat","volume":"11","author":"Tucker","year":"1981","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote estimation of canopy chlorophyll content in crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"340","DOI":"10.2489\/jswc.70.6.340","article-title":"Remote sensing to monitor cover crop adoption in southeastern Pennsylvania","volume":"70","author":"Hively","year":"2015","journal-title":"J. Soil Water Conserv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"126278","DOI":"10.1016\/j.eja.2021.126278","article-title":"Field-scale assessment of Belgian winter cover crops biomass based on Sentinel-2 data","volume":"126","author":"Goffart","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_23","first-page":"88","article-title":"Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States","volume":"39","author":"Prabhakara","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.agrformet.2015.12.064","article-title":"Informative spectral bands for remote green LAI estimation in C3 and C4 crops","volume":"218","author":"Kira","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1080\/01431169008955129","article-title":"Visible and near infrared reflectance characteristics of dry plant materials","volume":"11","author":"Elvidge","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/0034-4257(89)90069-2","article-title":"Remote sensing of foliar chemistry","volume":"30","author":"Curran","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.eja.2012.12.001","article-title":"A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems","volume":"46","author":"Delegido","year":"2013","journal-title":"Eur. J. Agron."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TGRS.1995.8746029","article-title":"The interpretation of spectral vegetation indexes","volume":"33","author":"Myneni","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","unstructured":"Rouse, J.W., Haars, J.R.H., Schell, J.A., and Deering, D.W. (1974, January 1). Monitoring Vegetation Systemsin the Great Plains Witherts. Proceedings of the 3rd ERTS Symposium, Washingston, DC, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chapagain, T., Lee, E.A., and Raizada, M.N. (2020). The Potential of Multi-Species Mixtures to Diversify Cover Crop Benefits. Sustainability, 12.","DOI":"10.3390\/su12052058"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1139\/juvs-2018-0018","article-title":"Vegetation monitoring using multispectral sensors\u2014Best practices and lessons learned from high latitudes","volume":"7","author":"Assmann","year":"2019","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_34","unstructured":"DWD (2022, April 29). Wetter und Klima\u2014Deutscher Wetterdienst: Kiel-Kronshagen (2565). Available online: https:\/\/opendata.dwd.de\/climate_environment\/CDC\/observations_germany\/climate\/multi_annual\/mean_91-20\/."},{"key":"ref_35","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_36","unstructured":"Hijmans, R.J. (2022). Raster: Geographic Data Analysis and Modeling [R package version 3.5-29], Comprehensive R Archive Network (CRAN)."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1109\/JSTARS.2015.2400134","article-title":"Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation","volume":"8","author":"Dong","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/S0176-1617(96)80284-7","article-title":"Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4527","DOI":"10.3390\/rs70404527","article-title":"A Comparison of Two Approaches for Estimating the Wheat Nitrogen Nutrition Index Using Remote Sensing","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.fcr.2016.08.023","article-title":"Remote detection of canopy leaf nitrogen concentration in winter wheat by using water resistance vegetation indices from in-situ hyperspectral data","volume":"198","author":"Feng","year":"2016","journal-title":"Field Crops Res."},{"key":"ref_42","first-page":"140","article-title":"Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm","volume":"192","author":"Peng","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/0034-4257(94)90016-7","article-title":"On the relationship between FAPAR and NDVI","volume":"49","author":"Myneni","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Stow, D., Nichol, C., Wade, T., Assmann, J., Simpson, G., and Helfter, C. (2019). Illumination Geometry and Flying Height Influence Surface Reflectance and NDVI Derived from Multispectral UAS Imagery. Drones, 3.","DOI":"10.3390\/drones3030055"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7714","DOI":"10.1109\/JSTARS.2021.3098720","article-title":"Progress in Remote Sensing of Grass Senescence: A Review on the Challenges and Opportunities","volume":"14","author":"Royimani","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","first-page":"5415","article-title":"Effect of senescent leaves on NDVI-based estimates of f APAR: Experimental and modelling evidences","volume":"25","author":"Paruelo","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Olsson, P.-O., Vivekar, A., Adler, K., Garcia Millan, V.E., Koc, A., Alamrani, M., and Eklundh, L. (2021). Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor. Remote Sens., 13.","DOI":"10.3390\/rs13040577"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4525\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:28:54Z","timestamp":1760142534000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,10]]},"references-count":48,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184525"],"URL":"https:\/\/doi.org\/10.3390\/rs14184525","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,10]]}}}