{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:29:03Z","timestamp":1773264543150,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CNR DIPARTIMENTO DI INGEGNERIA, ICT E TECNOLOGIE PER L\u2019ENERGIA E I TRASPORTI project","award":["DIT.AD022.207\/STRIVE"],"award-info":[{"award-number":["DIT.AD022.207\/STRIVE"]}]},{"name":"CNR DIPARTIMENTO DI INGEGNERIA, ICT E TECNOLOGIE PER L\u2019ENERGIA E I TRASPORTI project","award":["PRR.AP002.005"],"award-info":[{"award-number":["PRR.AP002.005"]}]},{"name":"smart management of agricultural systems and their environmental impact (AGRITECH)","award":["DIT.AD022.207\/STRIVE"],"award-info":[{"award-number":["DIT.AD022.207\/STRIVE"]}]},{"name":"smart management of agricultural systems and their environmental impact (AGRITECH)","award":["PRR.AP002.005"],"award-info":[{"award-number":["PRR.AP002.005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration.<\/jats:p>","DOI":"10.3390\/rs15194765","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T05:48:13Z","timestamp":1695966493000},"page":"4765","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7076-8328","authenticated-orcid":false,"given":"Ignazio","family":"Gallo","sequence":"first","affiliation":[{"name":"Department of Theoretical and Applied Science, University of Insubria, 21100 Varese, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2156-4166","authenticated-orcid":false,"given":"Mirco","family":"Boschetti","sequence":"additional","affiliation":[{"name":"Institute for Remote Sensing of Environment, Consiglio Nazionale delle Ricerche, 20133 Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9384-8988","authenticated-orcid":false,"given":"Anwar Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Theoretical and Applied Science, University of Insubria, 21100 Varese, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5270-071X","authenticated-orcid":false,"given":"Gabriele","family":"Candiani","sequence":"additional","affiliation":[{"name":"Institute for Remote Sensing of Environment, Consiglio Nazionale delle Ricerche, 20133 Milano, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s11119-019-09659-5","article-title":"Assessment of maize yield and phenology by drone-mounted superspectral camera","volume":"21","author":"Herrmann","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, Q., Chen, L., Zhang, S., Gu, X., Zhou, J., Gu, L., and Zhen, W. (2023, May 01). Estimation of Canopy Nitrogen Density of Lodging Maize Via UAV-Based Hyperspectral Images. SSRN 4364605. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4364605.","DOI":"10.2139\/ssrn.4364605"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Sobeih, T., Lappin, L., Lee, M.A., Howard, A., and Kisdi, A. (2022). The Self-Supervised Spectral\u2013Spatial Vision Transformer Network for Accurate Prediction of Wheat Nitrogen Status from UAV Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14061400"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Herrmann, I., and Berger, K. (2021). Remote and proximal assessment of plant traits.","DOI":"10.3390\/rs13101893"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.fcr.2018.01.007","article-title":"Do crop sensors promote improved nitrogen management in grain crops?","volume":"218","author":"Bramley","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/bs.agron.2019.08.001","article-title":"Site-specific seeding using multi-sensor and data fusion techniques: A review","volume":"161","author":"Munnaf","year":"2020","journal-title":"Adv. Agron."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, J., Shen, C., Liu, N., Jin, X., Fan, X., Dong, C., and Xu, Y. (2017). Non-destructive evaluation of the leaf nitrogen concentration by in-field visible\/near-infrared spectroscopy in pear orchards. Sensors, 17.","DOI":"10.3390\/s17030538"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111758","DOI":"10.1016\/j.rse.2020.111758","article-title":"Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions","volume":"242","author":"Berger","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.inffus.2020.01.007","article-title":"An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges","volume":"59","author":"Imani","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105860","DOI":"10.1016\/j.compag.2020.105860","article-title":"Rice nitrogen nutrition estimation with RGB images and machine learning methods","volume":"180","author":"Shi","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106421","DOI":"10.1016\/j.compag.2021.106421","article-title":"Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms","volume":"189","author":"Qiu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"112173","DOI":"10.1016\/j.rse.2020.112173","article-title":"PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents","volume":"252","author":"Berger","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_17","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 21\u201327). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, PMLR, Vienna, Austria."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106510","DOI":"10.1016\/j.compag.2021.106510","article-title":"Self-supervised contrastive learning on agricultural images","volume":"191","author":"Nalpantidis","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chiu, M.T., Xu, X., Wei, Y., Huang, Z., Schwing, A.G., Brunner, R., Khachatrian, H., Karapetyan, H., Dozier, I., and Rose, G. (2020, January 13\u201319). Agriculture-vision: A large aerial image database for agricultural pattern analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR2020, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00290"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1038\/s41598-018-38343-3","article-title":"DeepWeeds: A multiclass weed species image dataset for deep learning","volume":"9","author":"Olsen","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Marszalek, M.L., Saux, B.L., Mathieu, P.P., Nowakowski, A., and Springer, D. (2022). Self-supervised learning\u2013A way to minimize time and effort for precision agriculture?. arXiv.","DOI":"10.5194\/isprs-archives-XLIII-B3-2022-1327-2022"},{"key":"ref_23","first-page":"1","article-title":"Self-supervised learning with adaptive distillation for hyperspectral image classification","volume":"60","author":"Yue","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3510373","article-title":"Deep self-supervised hyperspectral image reconstruction","volume":"18","author":"Liu","year":"2022","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, J., Chen, Y., Yu, H., and Qin, T. (2022). Adaptive memory networks with self-supervised learning for unsupervised anomaly detection. IEEE Trans. Knowl. Data Eng.","DOI":"10.1109\/TKDE.2021.3139916"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108929","DOI":"10.1016\/j.fcr.2023.108929","article-title":"Improving chlorophyll content detection to suit maize dynamic growth effects by deep features of hyperspectral data","volume":"297","author":"Zhao","year":"2023","journal-title":"Field Crop. Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yin, C., Lv, X., Zhang, L., Ma, L., Wang, H., Zhang, L., and Zhang, Z. (2022). Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops. Remote Sens., 14.","DOI":"10.3390\/rs14112576"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, X., Yang, N., Liu, E., Gu, W., Zhang, J., Zhao, S., Sun, G., and Wang, J. (2023). Tree Species Classification Based on Self-Supervised Learning with Multisource Remote Sensing Images. Appl. Sci., 13.","DOI":"10.3390\/app13031928"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xie, X., Wang, Y., and Li, Q. (2022, January 18\u201322). S 3 R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022: 25th International Conference, Singapore. Proceedings, Part II.","DOI":"10.1007\/978-3-031-16434-7_5"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Candiani, G., Tagliabue, G., Panigada, C., Verrelst, J., Picchi, V., Rivera Caicedo, J.P., and Boschetti, M. (2022). Evaluation of hybrid models to estimate chlorophyll and nitrogen content of maize crops in the framework of the future CHIME mission. Remote Sens., 14.","DOI":"10.3390\/rs14081792"},{"key":"ref_31","unstructured":"Bank, D., Koenigstein, N., and Giryes, R. (2020). Autoencoders. arXiv."},{"key":"ref_32","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An introduction to convolutional neural networks. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2006.872529","article-title":"Validation of global moderate-resolution LAI products: A framework proposed within the CEOS land product validation subgroup","volume":"44","author":"Morisette","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography","volume":"121","author":"Jonckheere","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4673","DOI":"10.1111\/gcb.13017","article-title":"Sun-induced fluorescence\u2013a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant","volume":"21","author":"Rascher","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1002\/2014GL062943","article-title":"Red and far red Sun-induced chlorophyll fluorescence as a measure of plant photosynthesis","volume":"42","author":"Rossini","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.rse.2015.03.027","article-title":"Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems","volume":"164","author":"Cogliati","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Siegmann, B., Alonso, L., Celesti, M., Cogliati, S., Colombo, R., Damm, A., Douglas, S., Guanter, L., Hanu\u0161, J., and Kataja, K. (2019). The high-performance airborne imaging spectrometer HyPlant\u2014From raw images to top-of-canopy reflectance and fluorescence products: Introduction of an automatized processing chain. Remote Sens., 11.","DOI":"10.3390\/rs11232760"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/TGRS.2007.895844","article-title":"Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies","volume":"45","author":"Verhoef","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","unstructured":"Weiss, M., and Baret, F. (2016). S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER, Institut National de la Recherche Agronomique (INRA). [v1.1 ed.]."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ranghetti, M., Boschetti, M., Ranghetti, L., Tagliabue, G., Panigada, C., Gianinetto, M., Verrelst, J., and Candiani, G. (2022). Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling. Eur. J. Remote. Sens., 1\u201317.","DOI":"10.1080\/22797254.2022.2117650"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4765\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:01:28Z","timestamp":1760130088000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4765"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"references-count":43,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194765"],"URL":"https:\/\/doi.org\/10.3390\/rs15194765","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}