{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:02:34Z","timestamp":1774645354307,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001823","name":"Ministry of Education, Youth and Sports of the Czech Republic","doi-asserted-by":"publisher","award":["CZ.02.2.69\/0.0\/0.0\/18_053\/0016953"],"award-info":[{"award-number":["CZ.02.2.69\/0.0\/0.0\/18_053\/0016953"]}],"id":[{"id":"10.13039\/501100001823","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001823","name":"Ministry of Education, Youth and Sports of the Czech Republic","doi-asserted-by":"publisher","award":["MZE-RO0418"],"award-info":[{"award-number":["MZE-RO0418"]}],"id":[{"id":"10.13039\/501100001823","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001823","name":"Ministry of Education, Youth and Sports of the Czech Republic","doi-asserted-by":"publisher","award":["001171"],"award-info":[{"award-number":["001171"]}],"id":[{"id":"10.13039\/501100001823","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006533","name":"Ministry of Agriculture of the Czech Republic institutional support","doi-asserted-by":"publisher","award":["CZ.02.2.69\/0.0\/0.0\/18_053\/0016953"],"award-info":[{"award-number":["CZ.02.2.69\/0.0\/0.0\/18_053\/0016953"]}],"id":[{"id":"10.13039\/501100006533","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006533","name":"Ministry of Agriculture of the Czech Republic institutional support","doi-asserted-by":"publisher","award":["MZE-RO0418"],"award-info":[{"award-number":["MZE-RO0418"]}],"id":[{"id":"10.13039\/501100006533","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006533","name":"Ministry of Agriculture of the Czech Republic institutional support","doi-asserted-by":"publisher","award":["001171"],"award-info":[{"award-number":["001171"]}],"id":[{"id":"10.13039\/501100006533","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EU Interreg \u00d6KS","award":["CZ.02.2.69\/0.0\/0.0\/18_053\/0016953"],"award-info":[{"award-number":["CZ.02.2.69\/0.0\/0.0\/18_053\/0016953"]}]},{"name":"EU Interreg \u00d6KS","award":["MZE-RO0418"],"award-info":[{"award-number":["MZE-RO0418"]}]},{"name":"EU Interreg \u00d6KS","award":["001171"],"award-info":[{"award-number":["001171"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop status from hyperspectral data. The present study evaluates GPR model accuracy in the context of spring wheat dry matter, nitrogen content, and nitrogen uptake estimation. Models with the squared exponential covariance function were trained on images from two hyperspectral cameras (a frenchFabry\u2013P\u00e9rot interferometer camera and a push-broom scanner). The most accurate predictions were obtained for nitrogen uptake (R2=0.75\u20130.85, RPDP=2.0\u20132.6). Modifications of the basic workflow were then evaluated: the removal of soil pixels from the images prior to the training, data fusion with apparent soil electrical conductivity measurements, and replacing the Euclidean distance in the GPR covariance function with the spectral angle distance. Of these, the data fusion improved the performance while predicting nitrogen uptake and nitrogen content. The estimation accuracy of the latter parameter varied considerably across the two hyperspectral cameras. Satisfactory nitrogen content predictions (R2&gt;0.8, RPDP&gt;2.4) were obtained only in the data-fusion scenario, and only with a high spectral resolution push-broom device capable of capturing longer wavelengths, up to 1000 nm, while the full-frame camera spectral limit was 790 nm. The prediction performance and uncertainty metrics indicated the suitability of the models for precision agriculture applications. Moreover, the spatial patterns that emerged in the generated crop parameter maps accurately reflected the fertilization levels applied across the experimental area as well as the background variation of the abiotic growth conditions, further corroborating this conclusion.<\/jats:p>","DOI":"10.3390\/rs14235977","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"5977","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Gaussian Process Modeling of In-Season Physiological Parameters of Spring Wheat Based on Airborne Imagery from Two Hyperspectral Cameras and Apparent Soil Electrical Conductivity"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0298-8369","authenticated-orcid":false,"given":"Wiktor R.","family":"\u017belazny","sequence":"first","affiliation":[{"name":"Division of Crop Management Systems, Crop Research Institute Praha-Ruzyn\u011b, Drnovsk\u00e1 507\/73, 161 06 Praha, Czech Republic"},{"name":"Faculty of Engineering, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, 165 00 Praha, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6302-3707","authenticated-orcid":false,"given":"Krzysztof","family":"Kusnierek","sequence":"additional","affiliation":[{"name":"Department of Agricultural Technology, Norwegian Institute of Bioeconomy Research, Pb 115, 1431 \u00c5s, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7171-9501","authenticated-orcid":false,"given":"Jakob","family":"Geipel","sequence":"additional","affiliation":[{"name":"Department of Agricultural Technology, Norwegian Institute of Bioeconomy Research, Pb 115, 1431 \u00c5s, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107047","DOI":"10.1016\/j.ecolecon.2021.107047","article-title":"Benefits of Increasing Information Accuracy in Variable Rate Technologies","volume":"185","author":"Huber","year":"2021","journal-title":"Ecol. Econ."},{"key":"ref_2","first-page":"221","article-title":"COVID-19 and its Global Impact on Food and Agriculture","volume":"9","author":"Poudel","year":"2020","journal-title":"J. Biol. Today\u2019s World"},{"key":"ref_3","first-page":"1","article-title":"War in Ukraine and its Effect on Fertilizer Exports to Brazil and the U.S","volume":"12","author":"Colussi","year":"2022","journal-title":"Farmdoc Dly."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.eja.2008.01.005","article-title":"Diagnosis tool for plant and crop N status in vegetative stage: Theory and practices for crop N management","volume":"28","author":"Lemaire","year":"2008","journal-title":"Eur. J. Agron."},{"key":"ref_5","first-page":"102174","article-title":"Retrieval of aboveground crop nitrogen content with a hybrid machine learning method","volume":"92","author":"Berger","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","first-page":"637","article-title":"Producing nitrogen (N) uptake maps in winter wheat by combining proximal crop measurements with Sentinel-2 and DMC satellite images in a decision support system for farmers","volume":"67","author":"Piikki","year":"2017","journal-title":"Acta Agric. Scand. Sect. B\u2014Soil Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1007\/s11119-020-09733-3","article-title":"Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data","volume":"22","author":"Argento","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_8","first-page":"37","article-title":"Managing Soil Variability at Different Spatial Scales as a Basis for Precision Agriculture","volume":"Volume 22","author":"Lal","year":"2015","journal-title":"Soil-Specific Farming: Precision Agriculture"},{"key":"ref_9","first-page":"31","article-title":"Application of remote sensing methods in agriculture","volume":"11","author":"Piekarczyk","year":"2016","journal-title":"Commun. Biometry Crop. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Banerjee, B.P., Sharma, V., Spangenberg, G., and Kant, S. (2021). Machine Learning Regression Analysis for Estimation of Crop Emergence Using Multispectral UAV Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13152918"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.isprsjprs.2021.12.006","article-title":"UAV in the advent of the twenties: Where we stand and what is next","volume":"184","author":"Nex","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gewali, U.B., Monteiro, S.T., and Saber, E. (2019). Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth. Remote Sens., 11.","DOI":"10.3390\/rs11131614"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"126241","DOI":"10.1016\/j.eja.2021.126241","article-title":"An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives","volume":"124","author":"Fu","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"964","DOI":"10.3389\/fpls.2018.00964","article-title":"Assessing the Impact of Spatial Resolution on the Estimation of Leaf Nitrogen Concentration Over the Full Season of Paddy Rice Using Near-Surface Imaging Spectroscopy Data","volume":"9","author":"Zhou","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bruning, B., Liu, H., Brien, C., Berger, B., Lewis, M., and Garnett, T. (2019). The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum). Front. Plant Sci., 10.","DOI":"10.3389\/fpls.2019.01380"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mao, H., Meng, J., Ji, F., Zhang, Q., and Fang, H. (2019). Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands. Appl. Sci., 9.","DOI":"10.3390\/app9071459"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kganyago, M., Mhangara, P., and Adjorlolo, C. (2021). Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13214314"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1007\/s11119-022-09893-4","article-title":"Processing of remote sensing information to retrieve leaf area index in barley: A comparison of methods","volume":"23","author":"Rosso","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1080\/01431161.2021.2019847","article-title":"Combining spectral and texture features of UAV hyperspectral images for leaf nitrogen content monitoring in winter wheat","volume":"43","author":"Zhang","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s10712-018-9478-y","article-title":"Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods","volume":"40","author":"Verrelst","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0169-7439(01)00153-8","article-title":"Reliable and relevant modelling of real world data: A personal account of the development of PLS Regression","volume":"58","author":"Martens","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.chemolab.2004.01.002","article-title":"Comparing support vector machines to PLS for spectral regression applications","volume":"73","author":"Thissen","year":"2004","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1002\/widm.1114","article-title":"Mining data with random forests: Current options for real-world applications","volume":"4","author":"Ziegler","year":"2014","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MGRS.2015.2510084","article-title":"A survey on Gaussian processes for earth-observation data analysis: A comprehensive investigation","volume":"4","author":"Verrelst","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_25","first-page":"63","article-title":"Gaussian Processes in Machine Learning","volume":"Volume 3176","author":"Bousquet","year":"2004","journal-title":"Advanced Lectures on Machine Learning"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmp.2018.03.001","article-title":"A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions","volume":"85","author":"Schulz","year":"2018","journal-title":"J. Math. Psychol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/TGRS.2011.2168962","article-title":"Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques","volume":"50","author":"Verrelst","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"554","article-title":"Spectral band selection for vegetation properties retrieval using Gaussian processes regression","volume":"52","author":"Verrelst","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Est\u00e9vez, J., Berger, K., Vicent, J., Rivera-Caicedo, J.P., Wocher, M., and Verrelst, J. (2021). Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow. Remote Sens., 13.","DOI":"10.3390\/rs13081589"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, G., Li, Z., Song, X., Li, Z., Xu, X., Wang, P., and Zhao, C. (2020). Winter Wheat Nitrogen Status Estimation Using UAV-based RGB Imagery and Gaussian Processes Regression. Remote Sens., 12.","DOI":"10.3390\/rs12223778"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Abebe, G., Tadesse, T., and Gessesse, B. (2022). Estimating Leaf Area Index and biomass of sugarcane based on Gaussian process regression using Landsat 8 and Sentinel 1A observations. Int. J. Image Data Fusion, 1\u201331.","DOI":"10.1080\/19479832.2022.2055157"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2011.11.002","article-title":"Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3","volume":"118","author":"Verrelst","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e20180008","DOI":"10.1590\/0103-8478cr20180008","article-title":"Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle","volume":"48","author":"Wen","year":"2018","journal-title":"Ci\u00eancia Rural"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1109\/LGRS.2020.3014676","article-title":"Intelligent Sampling for Vegetation Nitrogen Mapping Based on Hybrid Machine Learning Algorithms","volume":"18","author":"Verrelst","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gewali, U.B., and Monteiro, S.T. (2016, January 25\u201328). A novel covariance function for predicting vegetation biochemistry from hyperspectral imagery with Gaussian processes. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532752"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gewali, U.B., and Monteiro, S.T. (2016, January 21\u201324). Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071716"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7104","DOI":"10.1080\/01431161.2018.1465614","article-title":"A tutorial on modelling and inference in undirected graphical models for hyperspectral image analysis","volume":"39","author":"Gewali","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106893","DOI":"10.1016\/j.compag.2022.106893","article-title":"Real-time control for multi-parametric data fusion and dynamic offset optimization in sensor-based variable rate nitrogen application","volume":"196","author":"Paraforos","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Crema, A., Boschetti, M., Nutini, F., Cillis, D., and Casa, R. (2020). Influence of Soil Properties on Maize and Wheat Nitrogen Status Assessment from Sentinel-2 data. Remote Sens., 12.","DOI":"10.3390\/rs12142175"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"108543","DOI":"10.1016\/j.fcr.2022.108543","article-title":"An assessment of background removal approaches for improved estimation of rice leaf nitrogen concentration with unmanned aerial vehicle multispectral imagery at various observation times","volume":"283","author":"Wang","year":"2022","journal-title":"Field Crop. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.05.008","article-title":"Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding","volume":"154","author":"Hu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, C., Yang, C., Xie, T., Jiang, Z., Hu, T., Luo, Z., Zhou, G., and Xie, J. (2020). Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12071207"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2017.06.008","article-title":"Estimating leaf chlorophyll content in sugar beet canopies using millimeter-to centimeter-scale reflectance imagery","volume":"198","author":"Jay","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106008","DOI":"10.1016\/j.compag.2021.106008","article-title":"Modeling and simulation of a multi-parametric fuzzy expert system for variable rate nitrogen application","volume":"182","author":"Paraforos","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Stafford, J.V. (2013). Fusion of data from multiple soil sensors for the delineation of water holding capacity zones. Precision Agriculture\u201913, Wageningen Academic Publishers.","DOI":"10.3920\/978-90-8686-778-3"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.geoderma.2019.01.006","article-title":"Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study","volume":"341","author":"Ji","year":"2019","journal-title":"Geoderma"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11119-007-9036-y","article-title":"Multi-temporal wheat disease detection by multi-spectral remote sensing","volume":"8","author":"Franke","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_48","unstructured":"Meier, U. (2001). Growth Stages of Mono- and Dicotyledoneous Plants. BBCH Monograph, Federal Biological Research Centre for Agriculture and Forestry."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1007\/s11119-021-09790-2","article-title":"Forage yield and quality estimation by means of UAV and hyperspectral imaging","volume":"22","author":"Geipel","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1019807","DOI":"10.1117\/12.2267476","article-title":"Real-time hyperspectral image processing for UAV applications, using HySpex Mjolnir-1024","volume":"Volume 10198","author":"Messinger","year":"2017","journal-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII"},{"key":"ref_51","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"Volume 1","author":"Freden","year":"1973","journal-title":"Third Earth Resources Technology Satellite-1 Symposium"},{"key":"ref_52","first-page":"180","article-title":"Analysar av grovf\u00f4rkvalitet p\u00e5 NIRS","volume":"Volume 1","author":"Kristoffersen","year":"2006","journal-title":"Plantem\u00f8tet \u00d8stlandet 2006"},{"key":"ref_53","unstructured":"Jones, D.B. (1941). Factors for Converting Percentages of Nitrogen in Foods and Feeds into Percentages of Proteins."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"10823","DOI":"10.3390\/s130810823","article-title":"A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances","volume":"13","year":"2013","journal-title":"Sensors"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.eja.2016.10.009","article-title":"Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption","volume":"82","author":"Guo","year":"2017","journal-title":"Eur. J. Agron."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compag.2004.03.002","article-title":"On-the-go soil sensors for precision agriculture","volume":"44","author":"Adamchuk","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/00401706.1969.10490666","article-title":"Computer Aided Design of Experiments","volume":"11","author":"Kennard","year":"1969","journal-title":"Technometrics"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Bonfil, D.J., Michael, Y., Shiff, S., and Lensky, I.M. (2021). Optimizing Top Dressing Nitrogen Fertilization Using VEN\u03bcS and Sentinel-2 L1 Data. Remote Sens., 13.","DOI":"10.3390\/rs13193934"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.compag.2015.08.001","article-title":"Simultaneous identification of spring wheat nitrogen and water status using visible and near infrared spectra and Powered Partial Least Squares Regression","volume":"117","author":"Kusnierek","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_60","first-page":"100598","article-title":"Monitoring of nitrogen accumulation in wheat plants based on hyperspectral data","volume":"23","author":"Song","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_62","unstructured":"Python Software Foundation (2022, August 08). Python Language Reference, Available online: https:\/\/docs.python.org\/3.9\/reference\/index.html."},{"key":"ref_63","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_64","unstructured":"R Core Team (2022). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_65","unstructured":"Stevens, A., Ramirez-Lopez, L., and Hans, G. (2022, August 18). Prospectr: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data, Available online: https:\/\/github.com\/l-ramirez-lopez\/prospectr."},{"key":"ref_66","unstructured":"Byers, J., Davidson, M., and Zhukov, Y.M. (2022). SUNGEO: Sub-National Geospatial Data Archive: Geoprocessing Toolkit, Available online: https:\/\/github.com\/zhukovyuri\/SUNGEO."},{"key":"ref_67","unstructured":"Stallman, R.M., McGrath, R., and Smith, P.D. (2020). GNU Make. A Program for Directing Recompilation, Free Software Foundation."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hunold, S., Costan, A., Gim\u00e9nez, D., Alexandru, I., Ricci, L., G\u00f3mez Requena, M.E., Scarano, V., Verbanescu, A.L., Scott, S.L., and Lankes, S. (2015). Reproducible and User-Controlled Software Environments in HPC with Guix. Euro-Par 2015: Parallel Processing Workshops, Vienna University of Technology.","DOI":"10.1007\/978-3-319-27308-2"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.biosystemseng.2005.05.001","article-title":"Potential for Onsite and Online Analysis of Pig Manure Using Visible and Near Infrared Reflectance Spectroscopy","volume":"91","author":"Saeys","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_71","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Integrated applications. Remote Sensing of Vegetation. Principles, Techniques, and Applications, Oxford University Press."},{"key":"ref_72","unstructured":"Jacquemoud, S., and Ustin, S.L. (2001, January 8\u201312). Leaf optical properties: A state of the art. Proceedings of the 8th International Symposium of Physical Measurements & Signatures in Remote Sensing, Aussois, France."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Liu, H., Bruning, B., Garnett, T., and Berger, B. (2020). The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat. Sensors, 20.","DOI":"10.3390\/s20164550"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s13007-017-0198-y","article-title":"Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images","volume":"13","author":"Knauer","year":"2017","journal-title":"Plant Methods"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Fan, L., Zhao, J., Xu, X., Liang, D., Yang, G., Feng, H., Yang, H., Wang, Y., Chen, G., and Wei, P. (2019). Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables. Sensors, 19.","DOI":"10.3390\/s19132898"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Yu, K.Q., Zhao, Y.R., Li, X.L., Shao, Y.N., Liu, F., and He, Y. (2014). Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0116205"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13007-019-0448-2","article-title":"Predicting the quality of ryegrass using hyperspectral imaging","volume":"15","author":"Shorten","year":"2019","journal-title":"Plant Methods"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/5977\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:57Z","timestamp":1760146017000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/5977"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,25]]},"references-count":77,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14235977"],"URL":"https:\/\/doi.org\/10.3390\/rs14235977","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,25]]}}}