{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:16:36Z","timestamp":1772759796678,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,15]],"date-time":"2020-01-15T00:00:00Z","timestamp":1579046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003406","name":"Tekes","doi-asserted-by":"publisher","award":["1711\/31\/2016"],"award-info":[{"award-number":["1711\/31\/2016"]}],"id":[{"id":"10.13039\/501100003406","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning validation dataset and calculating chlorophyll maps from empirical remotely sensed hyperspectral data and comparing them to     TCARI OSAVI    , an index that has strong negative correlation with chlorophyll concentration. With the validation dataset, coefficients of determination (    R 2    ) of 0.97 were obtained for chlorophyll a and 0.95 for chlorophyll b. The chlorophyll maps correlate with the     TCARI OSAVI     map. The correlation coefficient (R) is \u22120.87 for chlorophyll a and \u22120.68 for chlorophyll b in selected plots. These results indicate that the approach is highly promising approach for estimating vegetation chlorophyll content.<\/jats:p>","DOI":"10.3390\/rs12020283","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T04:14:41Z","timestamp":1579234481000},"page":"283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8297-8859","authenticated-orcid":false,"given":"Leevi","family":"Annala","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, FI-40014 Jyv\u00e4skyl\u00e4n Yliopisto, Jyv\u00e4skyl\u00e4, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7236-2145","authenticated-orcid":false,"given":"Eija","family":"Honkavaara","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute, National Land Survey of Finland, FI-02430 Masala, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5429-3433","authenticated-orcid":false,"given":"Sakari","family":"Tuominen","sequence":"additional","affiliation":[{"name":"Natural Resources Institute Finland, FI-00790 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5129-7364","authenticated-orcid":false,"given":"Ilkka","family":"P\u00f6l\u00f6nen","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, FI-40014 Jyv\u00e4skyl\u00e4n Yliopisto, Jyv\u00e4skyl\u00e4, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A large and persistent carbon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1093\/treephys\/26.11.1487","article-title":"Assessment of oak forest condition based on leaf biochemical variables and chlorophyll fluorescence","volume":"26","author":"Rossini","year":"2006","journal-title":"Tree Physiol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bjorkman, C., and Niemela, P. (2015). Climate Change and Insect Pests, CABI.","DOI":"10.1079\/9781780643786.0000"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (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_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2013a review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","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":"2018","journal-title":"Surv. Geophys."},{"key":"ref_8","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014, January 23\u201328). Large-scale video classification with convolutional neural networks. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.223"},{"key":"ref_10","unstructured":"Szegedy, C., Toshev, A., and Erhan, D. (2013, January 5\u201310). Deep neural networks for object detection. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cecotti, H., and Graeser, A. (2008, January 8\u201311). Convolutional neural network with embedded Fourier transform for EEG classification. Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761638"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","article-title":"Deep convolutional neural networks and data augmentation for environmental sound classification","volume":"24","author":"Salamon","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","unstructured":"LeCun, Y., and Bengio, Y. (1995). Convolutional networks for images, speech, and time-series. The Handbook of Brain Theory and Neural Networks, MIT Press."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2015.01.008","article-title":"Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modeling framework","volume":"102","author":"Croft","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","unstructured":"Atzberger, C., Jarmer, T., Schlerf, M., K\u00f6tz, B., and Werner, W. (2003, January 13\u201316). Retrieval of wheat bio-physical attributes from hyperspectral data and SAILH+ PROSPECT radiative transfer model. Proceedings of the 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, Germany."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(90)90100-Z","article-title":"PROSPECT: A model of leaf optical properties spectra","volume":"34","author":"Jacquemoud","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT+ SAIL models: A review of use for vegetation characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.rse.2003.12.002","article-title":"Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM\/I data","volume":"90","author":"Tedesco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1109\/TGRS.2007.909951","article-title":"Soil moisture retrieval from remotely sensed data: Neural network approach versus Bayesian method","volume":"46","author":"Notarnicola","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2007.04.013","article-title":"Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA","volume":"112","author":"Trombetti","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.1109\/36.934081","article-title":"Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks","volume":"39","author":"Cipollini","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00118-7","article-title":"SLOP: A Revised Version of the Stochastic Model for Leaf Optical Properties","volume":"68","author":"Maier","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1364\/AO.16.000635","article-title":"Leaf optical system modeled as a stochastic process","volume":"16","author":"Tucker","year":"1977","journal-title":"Appl. Opt."},{"key":"ref_24","unstructured":"Jacquemoud, S., and Ustin, L. (2008). Modeling leaf optical properties. Photobiol. Sci. Online, Available online: http:\/\/photobiology.info\/Jacq_Ustin.html."},{"key":"ref_25","unstructured":"Buschmann, C., and Nagel, E. (1991, January 3\u20136). Reflection Spectra Of Terrestrial Vegetation As Influenced By Pigment-protein Complexes And The Internal Optics Of The Leaf Tissue. Proceedings of the Remote Sensing: Global Monitoring for Earth Management (IGARSS\u201991), Espoo, Finland."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/0076-6879(87)48036-1","article-title":"[34] Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes","volume":"Volume 148","author":"Lichtenthaler","year":"1987","journal-title":"Plant Cell Membranes"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1111\/j.1751-1097.1994.tb05028.x","article-title":"Authentic in vivo absorption spectra for chlorophyll in leaves as derived from in situ and in vitro measurements","volume":"59","author":"Richter","year":"1994","journal-title":"Photochem. Photobiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1515\/znc-1981-5-614","article-title":"Light-Induced Accumulation and Stability of Chlorophylls and Chlorophyll-Proteins during Chloroplast Development in Radish Seedlings","volume":"36","author":"Lichtenthaler","year":"1981","journal-title":"Z. Naturforschung C"},{"key":"ref_29","first-page":"135","article-title":"A quantitative analysis of light distribution between the two photosystems, considering variation in both the relative amounts of the chlorophyll-protein complexes and the spectral quality of light","volume":"10","author":"Evans","year":"1986","journal-title":"Photobiochem. Photobiophys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1364\/AO.12.000555","article-title":"Optical Constants of Water in the 200-nm to 200-\u03bcm Wavelength Region","volume":"12","author":"Hale","year":"1973","journal-title":"Appl. Opt."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3348","DOI":"10.1364\/AO.18.003348","article-title":"Optical absorptions of light and heavy water by laser optoacoustic spectroscopy","volume":"18","author":"Tam","year":"1979","journal-title":"Appl. Opt."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1364\/JOSA.64.001107","article-title":"Optical properties of water in the near infrared","volume":"64","author":"Palmer","year":"1974","journal-title":"J. Opt. Soc. Am."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_34","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_35","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_36","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_37","unstructured":"Casella, G., and Lehmann, E.L. (1998). Theory of Point Estimation, Springer."},{"key":"ref_38","first-page":"557","article-title":"Correlation and causation","volume":"20","author":"Wright","year":"1921","journal-title":"J. Agric. Res."},{"key":"ref_39","unstructured":"(2020, January 09). Keras. Available online: keras.io."},{"key":"ref_40","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2020, January 09). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyypp\u00e4, J., Saari, H., P\u00f6l\u00f6nen, I., and Imai, N.N. (2017). Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sens., 9.","DOI":"10.3390\/rs9030185"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0367-2530(17)30456-5","article-title":"Seasonal fluctuations in chlorophyll content in birch stems with special reference to bark thickness and light transmission, a comparison between sprouts and seedlings","volume":"185","author":"Kauppi","year":"1991","journal-title":"Flora"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1139\/cjfr-2013-0493","article-title":"Variation in 13 leaf morphological and physiological traits within a silver birch (Betula pendula) stand and their relation to growth","volume":"44","author":"Possen","year":"2014","journal-title":"Can. J. For. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/0098-8472(92)90034-Y","article-title":"A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants","volume":"32","author":"Barnes","year":"1992","journal-title":"Environ. Exp. Bot."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1111\/j.1469-8137.1991.tb01028.x","article-title":"Seasonal changes in the pigments of Norway spruce, Picea abies (L.) Karst, and the influence of summer ozone exposures","volume":"119","author":"Robinson","year":"1991","journal-title":"New Phytol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1080\/02757250009532389","article-title":"Monte Carlo ray tracing in optical canopy reflectance modeling","volume":"18","author":"Disney","year":"2000","journal-title":"Remote Sens. Rev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6585","DOI":"10.1364\/AO.35.006585","article-title":"Three-dimensional radiation transfer modeling in a dicotyledon leaf","volume":"35","author":"Govaerts","year":"1996","journal-title":"Appl. Opt."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/36.662732","article-title":"Raytran: A Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media","volume":"36","author":"Govaerts","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0034-4257(98)00007-8","article-title":"LIBERTY\u2014Modeling the effects of leaf biochemical concentration on reflectance spectra","volume":"65","author":"Dawson","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0034-4257(84)90057-9","article-title":"Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model","volume":"16","author":"Verhoef","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0022-4073(01)00007-3","article-title":"A two-layer canopy reflectance model","volume":"71","author":"Kuusk","year":"2001","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/02757258809532105","article-title":"Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data","volume":"4","author":"Goel","year":"1988","journal-title":"Remote Sens. Rev."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(94)90035-3","article-title":"A multispectral canopy reflectance model","volume":"50","author":"Kuusk","year":"1994","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/283\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:43:03Z","timestamp":1760362983000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/2\/283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,15]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12020283"],"URL":"https:\/\/doi.org\/10.3390\/rs12020283","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,15]]}}}