{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T21:03:52Z","timestamp":1769547832017,"version":"3.49.0"},"reference-count":89,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2019YFA0706401"],"award-info":[{"award-number":["2019YFA0706401"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42001314"],"award-info":[{"award-number":["42001314"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["62172014"],"award-info":[{"award-number":["62172014"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["62172015"],"award-info":[{"award-number":["62172015"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["62272009"],"award-info":[{"award-number":["62272009"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["62002002"],"award-info":[{"award-number":["62002002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2019YFA0706401"],"award-info":[{"award-number":["2019YFA0706401"]}]},{"name":"National Natural Science Foundation of China","award":["42001314"],"award-info":[{"award-number":["42001314"]}]},{"name":"National Natural Science Foundation of China","award":["62172014"],"award-info":[{"award-number":["62172014"]}]},{"name":"National Natural Science Foundation of China","award":["62172015"],"award-info":[{"award-number":["62172015"]}]},{"name":"National Natural Science Foundation of China","award":["62272009"],"award-info":[{"award-number":["62272009"]}]},{"name":"National Natural Science Foundation of China","award":["62002002"],"award-info":[{"award-number":["62002002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Leaf carotenoids (Cxc) play a crucial role in vegetation as essential pigments responsible for capturing sunlight and protecting leaf tissues. They provide vital insights into a plant physiological status and serve as sensitive indicators of plant stress. However, remote sensing of Cxc at the leaf level has been challenging due to the low Cxc content and weaker absorption features compared to those of chlorophylls in the visible domain. Existing vegetation indices have been widely applied but often lack a solid physical foundation, which limits their applicability and robustness in characterizing Cxc. Yet, physical models can confront this ill-posed problem, though with high operational costs. To address this issue, this study presents a novel hybrid inversion method that combines the multilayer perceptron (MLP) algorithm with PROSPECT model simulations to accurately retrieve Cxc. The effectiveness of the MLP method was investigated through comparisons with the classical PROSPECT model inversion (look-up table [LUT] method), the convolutional neural network (CNN) hybrid model, and the Transformer hybrid model. In the pooled results of six experimental datasets, the MLP method exhibited its robustness and generalization capabilities for leaf Cxc content estimation, with RMSE of 3.12 \u03bcg\/cm2 and R2 of 0.52. The Transformer (RMSE = 3.14 \u03bcg\/cm2, R2 = 0.46), CNN (RMSE = 3.42 \u03bcg\/cm2, R2 = 0.28), and LUT (RMSE = 3.82 \u03bcg\/cm2, R2 = 0.24) methods followed in descending order of accuracy. A comparison with previous studies using the same public datasets (ANGERS and LOPEX) also demonstrated the performance of the MLP method from another perspective. These findings underscore the potential of the proposed MLP hybrid method as a powerful tool for accurate Cxc retrieval applications, providing valuable insights into vegetation health and stress response.<\/jats:p>","DOI":"10.3390\/rs15204997","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T10:43:10Z","timestamp":1697539390000},"page":"4997","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Novel Hybrid Model to Estimate Leaf Carotenoids Using Multilayer Perceptron and PROSPECT Simulations"],"prefix":"10.3390","volume":"15","author":[{"given":"Weilin","family":"Hao","sequence":"first","affiliation":[{"name":"School of Computer Science, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-364X","authenticated-orcid":false,"given":"Jia","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing 100871, China"}]},{"given":"Kan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Suzhou Enterprise Credit Service Co., Ltd., Suzhou 215000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0422-3493","authenticated-orcid":false,"given":"Feng","family":"Qiu","sequence":"additional","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"}]},{"given":"Jin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing 100871, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1038\/s41559-022-01920-x","article-title":"Plant traits alone are good predictors of ecosystem properties when used carefully","volume":"7","author":"Hagan","year":"2023","journal-title":"Nat. Ecol. Evol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ross, J. (1981). The Radiation Regime and Architecture of Plant Stands, Springer Science & Business Media.","DOI":"10.1007\/978-94-009-8647-3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/S1360-1385(96)80019-7","article-title":"The role of xanthophyll cycle carotenoids in the protection of photosynthesis","volume":"1","author":"Adams","year":"1996","journal-title":"Trends Plant Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1096\/fasebj.10.4.8647339","article-title":"In vivo functions of carotenoids in higher plants","volume":"10","author":"Gilmore","year":"1996","journal-title":"FASEB J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1562\/0031-8655(2002)075<0272:ACCIPL>2.0.CO;2","article-title":"Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy","volume":"75","author":"Gitelson","year":"2002","journal-title":"Photochem. Photobiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.rse.2012.09.014","article-title":"Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT+DART simulations","volume":"127","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, J., Han, W., Huang, L., Zhang, Z., and Ma, Y. (2016). Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors. Sensors, 16.","DOI":"10.3390\/s16040437"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s10712-019-09514-2","article-title":"Imaging Spectroscopy of Forest Ecosystems: Perspectives for the Use of Space-borne Hyperspectral Earth Observation Systems","volume":"40","author":"Hill","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110959","DOI":"10.1016\/j.rse.2018.11.002","article-title":"Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning","volume":"231","author":"Feret","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"L11402","DOI":"10.1029\/2006GL026457","article-title":"Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves","volume":"33","author":"Gitelson","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108330","DOI":"10.1016\/j.fcr.2021.108330","article-title":"Upscaling from leaf to canopy: Improved spectral indices for leaf biochemical traits estimation by minimizing the difference between leaf adaxial and abaxial surfaces","volume":"274","author":"Wan","year":"2021","journal-title":"Field Crops Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2012.12.015","article-title":"Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer","volume":"131","author":"Malenovsky","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiao, Q., Sun, Q., Zhang, B., Huang, W., Ye, H., Zhang, Z., Zhang, X., and Qian, B. (2022). A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle. Remote Sens., 14.","DOI":"10.3390\/rs14010098"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1080\/2150704X.2020.1795294","article-title":"Quantifying chlorophyll-a and b content in tea leaves using hyperspectral reflectance and deep learning","volume":"11","author":"Sonobe","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.31018\/jans.v13i3.2892","article-title":"Deep Convolutional neural network (CNN) in tea leaf chlorophyll estimation: A new direction of modern tea farming in Assam, India","volume":"13","author":"Barman","year":"2021","journal-title":"J. Appl. Nat. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Prilianti, K.R., Onggara, I.C., Adhiwibawa, M.A., Brotosudarmo, T.H., Anam, S., and Suryanto, A. (2018, January 16\u201318). Multispectral imaging and convolutional neural network for photosynthetic pigments prediction. Proceedings of the 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Malang, Indonesia.","DOI":"10.1109\/EECSI.2018.8752649"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.04.024","article-title":"Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion","volume":"212","author":"Sun","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TGRS.2013.2237780","article-title":"Use of general regression neural networks for generating the GLASS Leaf Area Index Product from Time Series MODIS Surface Reflectance","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e01201","DOI":"10.1016\/j.gecco.2020.e01201","article-title":"A simple method for estimation of leaf dry matter content in fresh leaves using leaf scattering albedo","volume":"23","author":"Yang","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1016\/j.rse.2008.06.005","article-title":"Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass","volume":"112","author":"Soudani","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1109\/TGRS.2011.2109390","article-title":"Quan Retrieval of Leaf Biochemical Parameters Using PROSPECT Inversion: A New Approach for Alleviating Ill-Posed Problems","volume":"49","author":"Wang","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111479","DOI":"10.1016\/j.rse.2019.111479","article-title":"The global distribution of leaf chlorophyll content","volume":"236","author":"Croft","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","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\u2014A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Berger, K., Caicedo, J.P.R., Martino, L., Wocher, M., and Verrelst, J. (2021). A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data. Remote Sens., 13.","DOI":"10.3390\/rs13020287"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112101","DOI":"10.1016\/j.rse.2020.112101","article-title":"Quantifying vegetation biophysical variables from the Sentinel-3\/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources","volume":"251","author":"Verrelst","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Brede, B., Verrelst, J., Gastellu-Etchegorry, J.-P., Clevers, J.G., Goudzwaard, L., den Ouden, J., Verbesselt, J., and Herold, M. (2020). Assessment of workflow feature selection on forest LAI prediction with sentinel-2A MSI, landsat 7 ETM+ and Landsat 8 OLI. Remote Sens., 12.","DOI":"10.3390\/rs12060915"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9904","DOI":"10.1364\/AO.58.009904","article-title":"Effect of different regression algorithms on the estimating leaf parameters based on selected characteristic wavelengths by using the PROSPECT model","volume":"58","author":"Zhang","year":"2019","journal-title":"Appl. Opt."},{"key":"ref_33","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_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","first-page":"2923","DOI":"10.1080\/01431161.2016.1186850","article-title":"Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method","volume":"37","author":"Liang","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1109\/36.263783","article-title":"LAI inversion using a back-propagation neural network trained with a multiple scattering model","volume":"31","author":"Smith","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/014311699213631","article-title":"Inverting a canopy reflectance model using a neural network","volume":"20","author":"Gong","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shi, R.H., and Sun, J. (2007, January 19\u201322). Estimating Leaf Biochemical Information from Leaf Reflectance Spectrum using Artificial Neural Network. Proceedings of the International Conference on Machine Learning & Cybernetics, Hong Kong, China.","DOI":"10.1109\/ICMLC.2007.4370515"},{"key":"ref_40","first-page":"102719","article-title":"A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance","volume":"108","author":"Shi","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014A review of applications in the atmospheric sciences","volume":"32","author":"Dorling","year":"1998","journal-title":"Atmos. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0893-6080(05)80131-5","article-title":"Original Contribution: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function","volume":"6","author":"Leshno","year":"1993","journal-title":"Neural Netw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.isprsjprs.2021.01.017","article-title":"Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops","volume":"173","author":"Danner","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_46","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_47","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_48","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2017.03.004","article-title":"PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle","volume":"193","author":"Gitelson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1016\/j.rse.2008.02.012","article-title":"PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments","volume":"112","author":"Feret","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2010.09.012","article-title":"Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS\/PROBA observations","volume":"115","author":"Verger","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.isprsjprs.2017.11.010","article-title":"Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval","volume":"135","author":"Sun","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2742","DOI":"10.1016\/j.rse.2011.06.016","article-title":"Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., Wocher, M., Mauser, W., and Hank, T. (2018). Model-based optimization of spectral sampling for the retrieval of crop variables with the PROSAIL model. Remote Sens., 10.","DOI":"10.3390\/rs10122063"},{"key":"ref_55","unstructured":"Barry, K., and Newnham, G.J. (2012, January 10\u201312). Quantification of chlorophyll and carotenoid pigments in eucalyptus foliage with the radiative transfer model PROSPECT 5 is affected by anthocyanin and epicuticular waxes. Proceedings of the Geospatial Science Research Symposium, Melbourne, Australia."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s13007-018-0291-x","article-title":"Estimation of leaf traits from reflectance measurements: Comparison between methods based on vegetation indices and several versions of the PROSPECT model","volume":"14","author":"Jiang","year":"2018","journal-title":"Plant Methods"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep learning: Methods and applications","volume":"7","author":"Deng","year":"2014","journal-title":"Found. Trends\u00ae Signal Process."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1201\/9781420050646.ptb6"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112308","DOI":"10.1016\/j.rse.2021.112308","article-title":"Change detection using deep learning approach with object-based image analysis\u2014ScienceDirect","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.agrformet.2018.11.035","article-title":"Wavelength selection of the multispectral lidar system for estimating leaf chlorophyll and water contents through the PROSPECT model","volume":"266\u2013267","author":"Sun","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"112176","DOI":"10.1016\/j.rse.2020.112176","article-title":"Spectral subdomains and prior estimation of leaf structure improves PROSPECT inversion on reflectance or transmittance alone","volume":"252","author":"Spafford","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_65","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017, January 4\u20139). Self-normalizing neural networks. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5304","DOI":"10.1109\/TGRS.2019.2963262","article-title":"Estimation of Surface Shortwave Radiation From Himawari-8 Satellite Data Based on a Combination of Radiative Transfer and Deep Neural Network","volume":"58","author":"Ma","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_68","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_69","unstructured":"Hosgood, B., Jacquemoud, S., Andreoli, G., Verdebout, J., Pedrini, G., and Schmuck, G. (1995). Leaf Optical Properties Experiment 93 (LOPEX93), The European Commission."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.12.013","article-title":"PROCWT: Coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra","volume":"206","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3119","DOI":"10.1109\/TGRS.2018.2791930","article-title":"Improving the PROSPECT Model to Consider Anisotropic Scattering of Leaf Internal Materials and Its Use for Retrieving Leaf Biomass in Fresh Leaves","volume":"56","author":"Qiu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","first-page":"1","article-title":"A 21-Year Time Series of Global Leaf Chlorophyll Content Maps From MODIS Imagery","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3280","DOI":"10.3390\/rs5073280","article-title":"Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model","volume":"5","author":"Rivera","year":"2013","journal-title":"Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Hu, W., Zhang, Y., and Li, L. (2019). Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images. Sensors, 19.","DOI":"10.3390\/s19163584"},{"key":"ref_75","unstructured":"Zhou, X., Liu, H., Shi, C., and Liu, J. (2022). Deep Learning on Edge Computing Devices, Elsevier."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"101060","DOI":"10.1016\/j.aei.2020.101060","article-title":"Automated text classification of near-misses from safety reports: An improved deep learning approach","volume":"44","author":"Fang","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_77","unstructured":"Zhu, J., Xia, Y., Wu, L., He, D., Qin, T., Zhou, W., Li, H., and Liu, T.-Y. (2020). Incorporating bert into neural machine translation. arXiv."},{"key":"ref_78","unstructured":"Amatriain, X. (2023). Transformer models: An introduction and catalog. arXiv."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.neucom.2022.09.136","article-title":"A survey of transformer-based multimodal pre-trained modals","volume":"515","author":"Han","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_80","unstructured":"Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Moutik, O., Sekkat, H., Tigani, S., Chehri, A., Saadane, R., Tchakoucht, T.A., and Paul, A. (2023). Convolutional neural networks or vision transformers: Who will win the race for action recognitions in visual data?. Sensors, 23.","DOI":"10.3390\/s23020734"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"121007","DOI":"10.1016\/j.eswa.2023.121007","article-title":"Remote sensing image instance segmentation network with transformer and multi-scale feature representation","volume":"234","author":"Ye","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_83","unstructured":"Guo, K., Zeng, S., Yu, J., Wang, Y., and Yang, H. (2017). A Survey of FPGA Based Neural Network Accelerator. arXiv."},{"key":"ref_84","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv."},{"key":"ref_85","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3377454","article-title":"A survey on distributed machine learning","volume":"53","author":"Verbraeken","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_87","unstructured":"Chen, T., Moreau, T., Jiang, Z., Zheng, L., Yan, E., Shen, H., Cowan, M., Wang, L., Hu, Y., and Ceze, L. (2018, January 8\u201310). {TVM}: An automated {End-to-End} optimizing compiler for deep learning. Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), Carlsbad, CA, USA."},{"key":"ref_88","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_89","unstructured":"Atzberger, C. (2000). A Decade of Trans, Proceedings of the 20th EARSeL Symposium, Dresden, Germany, 14\u201316 June 2000, The European Commission."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/4997\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:08:32Z","timestamp":1760130512000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/4997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,17]]},"references-count":89,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15204997"],"URL":"https:\/\/doi.org\/10.3390\/rs15204997","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,17]]}}}