{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T17:34:19Z","timestamp":1773768859199,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T00:00:00Z","timestamp":1664064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Helsinki"},{"name":"Maatalouskoneiden tutkimuss\u00e4\u00e4ti\u00f6 (Agricultural Machinery Research Foundation)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing is a method used for monitoring and measuring agricultural crop fields. Unmanned aerial vehicles (UAV) are used to effectively monitor crops via different camera technologies. Even though aerial imaging can be considered a rather straightforward process, more focus should be given to data quality and processing. This research focuses on evaluating the influences of field conditions on raw data quality and commonly used vegetation indices. The aerial images were taken with a custom-built UAV by using a multispectral camera at four different times of the day and during multiple times of the season. Measurements were carried out in the summer seasons of 2019 and 2020. The imaging data were processed with different software to calculate vegetation indices for 10 reference areas inside the fields. The results clearly show that NDVI (normalized difference vegetation index) was the least affected vegetation index by the field conditions. The coefficient of variation (CV) was determined to evaluate the variations in vegetation index values within a day. Vegetation index TVI (transformed vegetation index) and NDVI had coefficient of variation values under 5%, whereas with GNDVI (green normalized difference vegetation index), the value was under 10%. Overall, the vegetation indices that include near-infrared (NIR) bands are less affected by field condition changes.<\/jats:p>","DOI":"10.3390\/rs14194792","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T03:34:17Z","timestamp":1664163257000},"page":"4792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2599-1894","authenticated-orcid":false,"given":"Mikael","family":"\u00c4n\u00e4kk\u00e4l\u00e4","sequence":"first","affiliation":[{"name":"Department of Agricultural Sciences, University of Helsinki, 00790 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2175-7833","authenticated-orcid":false,"given":"Antti","family":"Lajunen","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Helsinki, 00790 Helsinki, Finland"}]},{"given":"Mikko","family":"Hakoj\u00e4rvi","sequence":"additional","affiliation":[{"name":"Mtech Digital Solutions Ltd., 01370 Vantaa, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4327-9852","authenticated-orcid":false,"given":"Laura","family":"Alakukku","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Helsinki, 00790 Helsinki, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105100","DOI":"10.1109\/ACCESS.2019.2932119","article-title":"Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","article-title":"UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras","volume":"146","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","first-page":"102177","article-title":"Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging","volume":"92","author":"Shendryk","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xu, X., Fan, L., Li, Z., Meng, Y., Feng, H., Yang, H., and Xu, B. (2021). Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV. Remote Sens., 13.","DOI":"10.3390\/rs13030340"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106155","DOI":"10.1016\/j.compag.2021.106155","article-title":"Crop height estimation based on UAV images: Methods, errors, and strategies","volume":"185","author":"Xie","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","article-title":"Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach","volume":"174","author":"Kerkech","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.plantsci.2018.10.022","article-title":"A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform","volume":"282","author":"Hassan","year":"2019","journal-title":"Plant Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106292","DOI":"10.1016\/j.compag.2021.106292","article-title":"Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction","volume":"187","author":"Qi","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mazzia, V., Comba, L., Khaliq, A., Chiaberge, M., and Gay, P. (2020). UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture. Sensors, 20.","DOI":"10.3390\/s20092530"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105519","DOI":"10.1016\/j.compag.2020.105519","article-title":"UAV and a deep convolutional neural network for monitoring invasive alien plants in the wild","volume":"174","author":"Qian","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105331","DOI":"10.1016\/j.compag.2020.105331","article-title":"Monitoring of sugar beet growth indicators using wide-dynamic-range vegetation index (WDRVI) derived from UAV multispectral images","volume":"171","author":"Cao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/0034-4257(94)90018-3","article-title":"Development of vegetation and soil indices for MODIS-EOS","volume":"49","author":"Huete","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.fcr.2010.01.010","article-title":"Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index\u2014The canopy chlorophyll content index (CCCI)","volume":"116","author":"Fitzgeralda","year":"2010","journal-title":"Field Crops Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, Y., Senthilnath, J., Wu, W., Zhang, X., Zeng, Z., and Huang, H. (2019). Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform. Sustainability, 11.","DOI":"10.3390\/su11040978"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"N\u00e4si, R., Viljanen, N., Kaivosoja, J., Alhonoja, K., Hakala, T., Markelin, L., and Honka-vaara, E. (2018). Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features. Remote Sens., 10.","DOI":"10.3390\/rs10071082"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Viljanen, N., Honkavaara, E., N\u00e4si, R., Hakala, T., Niemel\u00e4inen, O., and Kaivosoja, J. (2018). Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture, 8.","DOI":"10.3390\/agriculture8050070"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.agrformet.2008.03.005","article-title":"Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation","volume":"148","author":"Wu","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_19","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS-1 Symposium, NASA SP-351."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_21","unstructured":"Suab, S.A., Syukur, M.S.B., Avtar, R., and Korom, A. (2019, January 1\u20133). Unmanned aerial vehicle (uav) derived normalised difference vegetation index (ndvi) and crown projection area (cpa) to detect health conditions of young oil palm trees for precision agriculture. Proceedings of the 6th International Conference on Geomatics and Geospatial Technology (GGT 2019), Kuala Lumpur, Malaysia."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.biosystemseng.2020.11.010","article-title":"Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize","volume":"202","author":"Lu","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean yield prediction from UAV using multimodal data fusion and deep learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2021.08.035","article-title":"Nutritional status assessment of olive crops by means of the analysis and modelling of multispectral images taken with UAVs","volume":"211","author":"Noguera","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1111\/j.1365-3180.1974.tb01084.x","article-title":"A decimal code for the growth stages of cereals","volume":"14","author":"Zadoks","year":"1974","journal-title":"Weed Res."},{"key":"ref_26","unstructured":"Finnish Meteorological Institute (2021, October 01). Precipitation Amount and Air Temperature. Available online: https:\/\/en.ilmatieteenlaitos.fi\/download-observations."},{"key":"ref_27","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. ASAE"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/BF00031911","article-title":"GEMI: A non-linear index to monitor global vegetation from satellites","volume":"101","author":"Pinty","year":"1992","journal-title":"Vegetatio"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative estimation of chlorophyll a using reflectance spectra: Experiments with autumn chestnut and maple leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobioliology B Biol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations formonitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","first-page":"1385","article-title":"\u00c9tude des propri\u00e9t\u00e9s spectrales des sols arides appliqu\u00e9e \u00e0 l\u2019am\u00e9lioration des indices de v\u00e9g\u00e9tation obtenus par t\u00e9l\u00e9d\u00e9tection","volume":"312","author":"Escadafal","year":"1991","journal-title":"CR Acad. Sci."},{"key":"ref_37","unstructured":"Pearson, R.L., and Miller, L.D. (1972, January 2\u20136). Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado. Proceedings of the 8th International Symposium on Remote Sensing of the Environment II, Ann Arbor, MI, USA."},{"key":"ref_38","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation, Texas A&M University, Remote Sensing Center. Report RSC 1978-4."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Marino, S., and Alvino, A. (2021). Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits. Remote Sens., 13.","DOI":"10.3390\/rs13040541"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, F., and Zhou, G. (2019). Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecol., 19.","DOI":"10.1186\/s12898-019-0233-0"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/S0034-4257(01)00347-9","article-title":"Relating soil surface moisture to reflectance","volume":"81","author":"Weidong","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A., and Landivar, J. (2019). Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture. Remote Sens., 11.","DOI":"10.3390\/rs11131548"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hashimoto, N., Saito, Y., Maki, M., and Homma, K. (2019). Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields. Remote Sens., 11.","DOI":"10.3390\/rs11182119"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"14079","DOI":"10.3390\/rs71014079","article-title":"The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands\u2014Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements","volume":"7","author":"Ishihara","year":"2015","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"112433","DOI":"10.1016\/j.rse.2021.112433","article-title":"Impact of the reproductive organs on crop BRDF as observed from a UAV","volume":"259","author":"Li","year":"2021","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4792\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:39:22Z","timestamp":1760143162000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,25]]},"references-count":46,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194792"],"URL":"https:\/\/doi.org\/10.3390\/rs14194792","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,25]]}}}