{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T20:53:04Z","timestamp":1781556784008,"version":"3.54.5"},"reference-count":68,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/P016855\/1"],"award-info":[{"award-number":["BB\/P016855\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions.<\/jats:p>","DOI":"10.3390\/rs13050898","type":"journal-article","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T20:55:44Z","timestamp":1614459344000},"page":"898","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0352-227X","authenticated-orcid":false,"given":"Pouria","family":"Sadeghi-Tehran","sequence":"first","affiliation":[{"name":"Department of Plant Sciences, Rothamsted Research, Harpenden AL5 2JQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Virlet","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, Rothamsted Research, Harpenden AL5 2JQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8759-3969","authenticated-orcid":false,"given":"Malcolm J.","family":"Hawkesford","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, Rothamsted Research, Harpenden AL5 2JQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Stuart, M.B., McGonigle, A.J.S., and Willmott, J.R. (2019). Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors, 19.","DOI":"10.3390\/s19143071"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13007-017-0233-z","article-title":"Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress","volume":"13","author":"Lowe","year":"2017","journal-title":"Plant Methods"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","article-title":"Three decades of hyperspectral remote sensing of the Earth: A personal view","volume":"113","author":"Goetz","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_5","unstructured":"Amigo, J.M., Marti, I., and Gowen, A. (2010). Hyperspectral Imaging for Food Quality Analysis and Control, Elsevier."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00216-010-3828-z","article-title":"Practical issues of hyperspectral imaging analysis of solid dosage forms","volume":"398","author":"Amigo","year":"2010","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","article-title":"Medical hyperspectral imaging: A review","volume":"19","author":"Lu","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.compag.2015.09.005","article-title":"Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics","volume":"118","author":"Thorp","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3892","DOI":"10.3390\/rs4123892","article-title":"Evaluation of Broadband and Narrowband Vegetation Indices for the Identification of Archaeological Crop Marks","volume":"4","author":"Agapiou","year":"2012","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1111\/j.1445-6664.2006.00234.x","article-title":"Plant classification for weed detection using hyperspectral imaging with wavelet analysis","volume":"7","author":"Okamoto","year":"2007","journal-title":"Weed Biol. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.fcr.2011.02.003","article-title":"Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat","volume":"122","author":"Vigneau","year":"2011","journal-title":"Field Crop. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1111\/j.1469-8137.2010.03284.x","article-title":"Remote sensing of plant functional types","volume":"186","author":"Ustin","year":"2010","journal-title":"New Phytol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2018.02.003","article-title":"Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform","volume":"138","author":"Asaari","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1111\/pce.13718","article-title":"Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression","volume":"43","author":"Fu","year":"2020","journal-title":"Plant Cell Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s13007-017-0226-y","article-title":"A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions","volume":"13","author":"Williams","year":"2017","journal-title":"Plant Methods"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"5357","DOI":"10.1080\/01431161.2017.1338785","article-title":"A shadow identification method using vegetation indices derived from hyperspectral data","volume":"38","author":"Liu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/2150704X.2014.1001079","article-title":"Development of a multi-scale object-based shadow detection method for high spatial resolution image","volume":"6","author":"Luo","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral image data analysis","volume":"19","author":"Landgrebe","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.fcr.2017.05.005","article-title":"Chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping","volume":"210","author":"Jay","year":"2017","journal-title":"Field Crop. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/S0034-4257(97)00070-9","article-title":"Analyzing the effect of structural variability and canopy gaps on forest BRDF using a geometric-optical model","volume":"62","author":"Gerard","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/S0034-4257(96)00079-X","article-title":"Effects of shadowing types on ground-measured visible and near-infrared shadow reflectances","volume":"58","author":"Leblon","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1109\/LGRS.2015.2450218","article-title":"An Analysis of Shadow Effects on Spectral Vegetation Indexes Using a Ground-Based Imaging Spectrometer","volume":"12","author":"Zhang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3201","DOI":"10.1016\/j.rse.2008.03.015","article-title":"Multi-angle remote sensing of forest light use efficiency by observing PRI variation with canopy shadow fraction","volume":"112","author":"Hall","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1016\/j.rse.2008.01.011","article-title":"Separating physiologically and directionally induced changes in PRI using BRDF models","volume":"112","author":"Hilker","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2380","DOI":"10.1016\/j.rse.2009.06.018","article-title":"Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery","volume":"113","author":"Berni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2011.10.007","article-title":"Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera","volume":"117","author":"Berni","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","first-page":"281","article-title":"Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)","volume":"171","author":"Catalina","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Camino, C., Zarco-Tejada, P.J., and Gonzalez-Dugo, V. (2018). Effects of Heterogeneity within Tree Crowns on Airborne-Quantified SIF and the CWSI as Indicators of Water Stress in the Context of Precision Agriculture. Remote Sens., 10.","DOI":"10.3390\/rs10040604"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Maimaitiyiming, M., Sagan, V., Sidike, P., Maimaitijiang, M., Miller, A.J., and Kwasniewski, M. (2020). Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology. Remote Sens., 12.","DOI":"10.3390\/rs12193216"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.5194\/bg-16-2937-2019","article-title":"Assessing shaded-leaf effects on photochemical reflectance index (PRI) for water stress detection in winter wheat","volume":"16","author":"Yang","year":"2019","journal-title":"Biogeosciences"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, K., Deng, X., Yao, X., Tian, Y., Cao, W., Zhu, Y., Ustin, S.L., and Cheng, T. (2017). Assessing the Spectral Properties of Sunlit and Shaded Components in Rice Canopies with Near-Ground Imaging Spectroscopy Data. Sensors, 17.","DOI":"10.3390\/s17030578"},{"key":"ref_34","first-page":"3","article-title":"The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery","volume":"8","year":"2006","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"729","DOI":"10.13031\/2013.24370","article-title":"Yield Estimation from Hyperspectral Imagery Using Spectral Angle Mapper (SAM)","volume":"51","author":"Yang","year":"2008","journal-title":"Trans. ASABE"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-Based Methods for Hyperspectral Image Classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of Hyperspectral Remote Sensing Images with Support Vector Machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hay, E.A., and Parthasarathy, R. (2018). Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets. PLoS Comput. Biol., 14.","DOI":"10.1101\/273318"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"23029","DOI":"10.1364\/OE.27.023029","article-title":"Compressive spectral imaging system for soil classification with three-dimensional convolutional neural network","volume":"27","author":"Yu","year":"2019","journal-title":"Opt. Express"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MGRS.2020.2979764","article-title":"Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep","volume":"8","author":"Rasti","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","first-page":"1","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_44","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv."},{"key":"ref_45","unstructured":"Home\u2014OpenCV (2021, February 01). Undefined. Available online: https:\/\/opencv.org\/."},{"key":"ref_46","unstructured":"Hyperspectral Sensors|Hyperspectral Cameras (2020, August 03). Undefined. Available online: https:\/\/www.headwallphotonics.com\/hyperspectral-sensors."},{"key":"ref_47","unstructured":"Wheat Growth Guide|AHDB (2021, February 22). Undefined. Available online: https:\/\/ahdb.org.uk\/wheatgg."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1071\/FP16163","article-title":"Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring","volume":"44","author":"Virlet","year":"2017","journal-title":"Funct. Plant Biol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1111\/nph.13524","article-title":"Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data","volume":"208","author":"Turnbull","year":"2015","journal-title":"New Phytol."},{"key":"ref_50","first-page":"721","article-title":"Hyperspectral reflectance and fluorescence imaging system for food quality and safety","volume":"44","author":"Kim","year":"2001","journal-title":"Trans. ASAE"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, L., and You, J. (2018). Hyperspectral Image Classification Based on Two-Stage Subspace Projection. Remote Sens., 10.","DOI":"10.3390\/rs10101565"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2005.09.002","article-title":"Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy","volume":"99","author":"Miller","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/0034-4257(84)90013-0","article-title":"Functional equivalence of spectral vegetation indices","volume":"14","author":"Perry","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1080\/01431169308904370","article-title":"In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation","volume":"14","author":"Buschmann","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1071\/AR9950113","article-title":"Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite","volume":"46","author":"Smith","year":"1995","journal-title":"Aust. J. Agric. Res."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/S0176-1617(96)80081-2","article-title":"Detection of Vegetation Stress Via a New High Resolution Fluorescence Imaging System","volume":"148","author":"Lichtenthaler","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(92)90059-S","article-title":"A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency","volume":"41","author":"Gamon","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_66","first-page":"243","article-title":"Detecting sub-surface soil disturbance using hyperspectral first derivative band rations of associated vegetation stress","volume":"27","author":"White","year":"2008","journal-title":"Int. Soc. Photogramm. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Liang, H., and Li, Q. (2016). Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features. Remote Sens., 8.","DOI":"10.3390\/rs8020099"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1111\/pce.12710","article-title":"Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies","volume":"39","author":"Pinto","year":"2016","journal-title":"Plant Cell Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/898\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:30:02Z","timestamp":1760160602000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,27]]},"references-count":68,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050898"],"URL":"https:\/\/doi.org\/10.3390\/rs13050898","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,27]]}}}