{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:27:51Z","timestamp":1774121271389,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["226-2022-00217"],"award-info":[{"award-number":["226-2022-00217"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2021C02057"],"award-info":[{"award-number":["2021C02057"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["188170+194452208\/005"],"award-info":[{"award-number":["188170+194452208\/005"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key R&amp;D Program of Zhejiang Province","award":["226-2022-00217"],"award-info":[{"award-number":["226-2022-00217"]}]},{"name":"Key R&amp;D Program of Zhejiang Province","award":["2021C02057"],"award-info":[{"award-number":["2021C02057"]}]},{"name":"Key R&amp;D Program of Zhejiang Province","award":["188170+194452208\/005"],"award-info":[{"award-number":["188170+194452208\/005"]}]},{"DOI":"10.13039\/501100004835","name":"Zhejiang University Global Partnership Fund","doi-asserted-by":"publisher","award":["226-2022-00217"],"award-info":[{"award-number":["226-2022-00217"]}],"id":[{"id":"10.13039\/501100004835","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004835","name":"Zhejiang University Global Partnership Fund","doi-asserted-by":"publisher","award":["2021C02057"],"award-info":[{"award-number":["2021C02057"]}],"id":[{"id":"10.13039\/501100004835","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004835","name":"Zhejiang University Global Partnership Fund","doi-asserted-by":"publisher","award":["188170+194452208\/005"],"award-info":[{"award-number":["188170+194452208\/005"]}],"id":[{"id":"10.13039\/501100004835","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing-based techniques have been widely used for chlorophyll content (Cab) estimations, while they are challenging when transferred across different species. Sun-induced chlorophyll fluorescence (SIF) provides a new approach to address these issues. This research explores whether SIF has transferability for Cab estimation and to enhance between-species transferability. Here, three rice datasets and a rapeseed dataset were collected. Initially, direct transfer models were constructed using partial least squares regression (PLSR) based on SIF yield (SIFY) and reflectance, respectively. Subsequently, methods were employed within the rice datasets to improve the models\u2019 transferability. Finally, the between-species transferability of two data sources was validated in the rapeseed dataset. Direct transfer models indicated that the reflectance-based model exhibited a higher accuracy in predicting Cab when the training dataset acquired sufficient features, whereas the SIFY-based model showed better performance with fewer features. Spectral preprocessing methods can enhance the transferability, especially for SIFY-based models. In addition, supplementing 10% of out-of-sample data significantly improved the transferability. The proposed methods only require a small amount of new data to extend the original model for predicting Cab in other species. Specifically, the new method reduced the average RMSE based on SIFY and reflectance models by 23.59% and 35.51%, respectively.<\/jats:p>","DOI":"10.3390\/rs16111869","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T03:46:56Z","timestamp":1716522416000},"page":"1869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimized Transfer Learning for Chlorophyll Content Estimations across Datasets of Different Species Using Sun-Induced Chlorophyll Fluorescence and Reflectance"],"prefix":"10.3390","volume":"16","author":[{"given":"Yu-an","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zichen","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"The Rural Development Academy & Agricultural Experiment Station, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijun","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Cen","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2014.01.004","article-title":"Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production","volume":"144","author":"Gitelson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1046\/j.0028-646X.2001.00289.x","article-title":"An evaluation of noninvasive methods to estimate foliar chlorophyll content","volume":"153","author":"Richardson","year":"2002","journal-title":"New Phytol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Andrianto, H., and Faizal, A. (2017, January 23\u201324). Measurement of chlorophyll content to determine nutrition deficiency in plants: A systematic literature review. Proceedings of the 2017 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia.","DOI":"10.1109\/ICITSI.2017.8267976"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kior, A., Sukhov, V., and Sukhova, E. (2021). Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors. Photonics, 8.","DOI":"10.3390\/photonics8120582"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2011.11.002","article-title":"Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3","volume":"118","author":"Verrelst","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0378-1127(03)00004-5","article-title":"Nondestructive and rapid estimation of hardwood foliar nitrogen status using the SPAD-502 chlorophyll meter","volume":"181","author":"Chang","year":"2003","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.jphotobiol.2014.03.010","article-title":"Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset","volume":"134","author":"Verrelst","year":"2014","journal-title":"J. Photochem. Photobiol. B"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"278","DOI":"10.2307\/2657019","article-title":"Estimating near-infrared leaf reflectance from leaf structural characteristics","volume":"88","author":"Slaton","year":"2001","journal-title":"Am. J. Bot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5265","DOI":"10.1080\/01431160802036359","article-title":"Effects of leaf structure on reflectance estimates of chlorophyll content","volume":"29","author":"Serrano","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s00468-012-0790-8","article-title":"Salt crystal deposition as a reversible mechanism to enhance photoprotection in black mangrove","volume":"27","author":"Esteban","year":"2012","journal-title":"Trees"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"693521","DOI":"10.3389\/fpls.2021.693521","article-title":"Hyperspectral Imaging Combined with Deep Transfer Learning for Rice Disease Detection","volume":"12","author":"Feng","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_13","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_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":"108254","DOI":"10.1016\/j.fcr.2021.108254","article-title":"On the needs for combining physiological principles and mathematics to improve crop models","volume":"271","author":"Yin","year":"2021","journal-title":"Field Crops Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"179","DOI":"10.2478\/fcds-2020-0010","article-title":"Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets","volume":"45","author":"Brodzicki","year":"2020","journal-title":"Found. Comput. Decis. Sci."},{"key":"ref_17","first-page":"026028","article-title":"Deep convolutional recurrent neural network with transfer learning for hyperspectral image classification","volume":"12","author":"Wan","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1080\/87559129.2021.1935999","article-title":"Calibration Maintenance Application of Near-infrared Spectrometric Model in Food Analysis","volume":"39","author":"Qiao","year":"2021","journal-title":"Food Rev. Int."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9813841","DOI":"10.34133\/2022\/9813841","article-title":"Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves","volume":"2022","author":"Xiao","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112826","DOI":"10.1016\/j.rse.2021.112826","article-title":"Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets","volume":"269","author":"Wan","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111176","DOI":"10.1016\/j.rse.2019.04.029","article-title":"High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity","volume":"231","author":"Montes","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1093\/aob\/mcz171","article-title":"Photosynthesis: Basics, history and modelling","volume":"126","author":"Stirbet","year":"2020","journal-title":"Ann. Bot."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1111\/gcb.13136","article-title":"Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence","volume":"22","author":"Guan","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5595","DOI":"10.1093\/jxb\/erv272","article-title":"Estimating chlorophyll content and photochemical yield of photosystem II (PhiPSII) using solar-induced chlorophyll fluorescence measurements at different growing stages of attached leaves","volume":"66","author":"Tubuxin","year":"2015","journal-title":"J. Exp. Bot."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jia, M., Zhu, J., Ma, C., Alonso, L., Li, D., Cheng, T., Tian, Y., Zhu, Y., Yao, X., and Cao, W. (2018). Difference and Potential of the Upward and Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Concentration in Wheat. Remote Sens., 10.","DOI":"10.3390\/rs10081315"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1093\/jxb\/eraa537","article-title":"The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: Benefits for field phenotyping","volume":"72","author":"Fu","year":"2021","journal-title":"J. Exp. Bot."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1029\/2019JG005029","article-title":"Disentangling Changes in the Spectral Shape of Chlorophyll Fluorescence: Implications for Remote Sensing of Photosynthesis","volume":"124","author":"Magney","year":"2019","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.envpol.2012.10.003","article-title":"Upward and downward solar-induced chlorophyll fluorescence yield indices of four tree species as indicators of traffic pollution in Valencia","volume":"173","author":"Alonso","year":"2013","journal-title":"Environ. Pollut."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.rse.2014.11.012","article-title":"Bidirectional sun-induced chlorophyll fluorescence emission is influenced by leaf structure and light scattering properties\u2014A bottom-up approach","volume":"158","author":"Alonso","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108357","DOI":"10.1016\/j.compag.2023.108357","article-title":"Early diagnosis and mechanistic understanding of citrus Huanglongbing via sun-induced chlorophyll fluorescence","volume":"215","author":"Chen","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1002\/cem.2736","article-title":"Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation","volume":"29","author":"Platikanov","year":"2015","journal-title":"J. Chemom."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.tifs.2006.09.003","article-title":"Theory and application of near infrared reflectance spectroscopy in determination of food quality","volume":"18","author":"Cen","year":"2007","journal-title":"Trends Food Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11120-007-9187-8","article-title":"Variability and application of the chlorophyll fluorescence emission ratio red\/far-red of leaves","volume":"92","author":"Buschmann","year":"2007","journal-title":"Photosynth. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, S., Liu, Z., Han, S., Chen, Z., He, X., Zhao, H., and Ren, S. (2023). Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought. Remote Sens., 15.","DOI":"10.3390\/rs15041077"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, J., He, W., Yao, J., Yu, Q., Xu, C., Huang, H., Mhae, B., and Jandug, C. (2020). Spectral Reflectance Characteristics and Chlorophyll Content Estimation Model of Quercus aquifolioides Leaves at Different Altitudes in Sejila Mountain. Appl. Sci., 10.","DOI":"10.3390\/app10103636"},{"key":"ref_37","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_38","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_39","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1366\/0003702854248656","article-title":"Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat","volume":"39","author":"Geladi","year":"1985","journal-title":"Appl. Spectrosc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1366\/0003702884429869","article-title":"The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy","volume":"42","author":"Isaksson","year":"1988","journal-title":"Appl. Spectrosc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1111\/j.1469-8137.2010.03536.x","article-title":"Sources of variability in canopy reflectance and the convergent properties of plants","volume":"189","author":"Ollinger","year":"2011","journal-title":"New Phytol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1038\/s41477-021-00980-4","article-title":"Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science","volume":"7","author":"Malenovsky","year":"2021","journal-title":"Nat. Plants"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11120-014-0009-5","article-title":"Fluorescence F 0 of photosystems II and I in developing C3 and C 4 leaves, and implications on regulation of excitation balance","volume":"122","author":"Peterson","year":"2014","journal-title":"Photosynth. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/01431168308948546","article-title":"The red edge of plant leaf reflectance","volume":"4","author":"Horler","year":"1983","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_46","unstructured":"Wagner, E.P., Merz, J., and Townsend, P.A. (2019, January 9\u201313). EcoSIS: A spectral library and the tools to use it. Proceedings of the AGU Fall Meeting, San Francisco, CA, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1869\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:47:39Z","timestamp":1760107659000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":46,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111869"],"URL":"https:\/\/doi.org\/10.3390\/rs16111869","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,23]]}}}