{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T21:44:45Z","timestamp":1778967885121,"version":"3.51.4"},"reference-count":98,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T00:00:00Z","timestamp":1700870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["101086622"],"award-info":[{"award-number":["101086622"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The advent of high-spatial-resolution hyperspectral imagery from unmanned aerial vehicles (UAVs) made a breakthrough in the detailed retrieval of crop traits for precision crop-growth monitoring systems. Here, a hybrid approach of radiative transfer modelling combined with a machine learning (ML) algorithm is proposed for the retrieval of the leaf area index (LAI) and canopy chlorophyll content (CCC) of wheat cropland at the experimental farms of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India. A hyperspectral image captured from a UAV platform with spatial resolution of 4 cm and 269 spectral bands ranging from 400 to 1000 nm was processed for the retrieval of the LAI and CCC of wheat cropland. The radiative transfer model PROSAIL was used for simulating spectral data, and eight machine learning algorithms were evaluated for hybrid model development. The ML Gaussian process regression (GPR) algorithm was selected for the retrieval of crop traits due to its superior accuracy and lower associated uncertainty. Simulated spectra were sampled for training GPR models for LAI and CCC retrieval using dimensionality reduction and active learning techniques. LAI and CCC biophysical maps were generated from pre-processed hyperspectral data using trained GPR models and validated against in situ measurements, yielding R2 values of 0.889 and 0.656, suggesting high retrieval accuracy. The normalised root mean square error (NRMSE) values reported for LAI and CCC retrieval are 8.579% and 14.842%, respectively. The study concludes with the development of optimized GPR models tailored for UAV-borne hyperspectral data for the near-real-time retrieval of wheat traits. This workflow can be upscaled to farmers\u2019 fields, facilitating efficient crop monitoring and management.<\/jats:p>","DOI":"10.3390\/rs15235496","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T03:35:06Z","timestamp":1701056106000},"page":"5496","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Optimizing the Retrieval of Wheat Crop Traits from UAV-Borne Hyperspectral Image with Radiative Transfer Modelling Using Gaussian Process Regression"],"prefix":"10.3390","volume":"15","author":[{"given":"Rabi N.","family":"Sahoo","sequence":"first","affiliation":[{"name":"Division of Agricultural Physics, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shalini","family":"Gakhar","sequence":"additional","affiliation":[{"name":"Division of Agricultural Physics, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajan G.","family":"Rejith","sequence":"additional","affiliation":[{"name":"Division of Agricultural Physics, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2233-9147","authenticated-orcid":false,"given":"Rajeev","family":"Ranjan","sequence":"additional","affiliation":[{"name":"Division of Agricultural Physics, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarun","family":"Kondraju","sequence":"additional","affiliation":[{"name":"Division of Agricultural Physics, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahesh C.","family":"Meena","sequence":"additional","affiliation":[{"name":"Division of Soil Science & Agricultural Chemistry, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joydeep","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Division of Agricultural Physics, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anchal","family":"Daas","sequence":"additional","affiliation":[{"name":"Division of Agronomy, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1089-7435","authenticated-orcid":false,"given":"Sudhir","family":"Kumar","sequence":"additional","affiliation":[{"name":"Division of Plant Physiology, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Division of Plant Physiology, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raju","family":"Dhandapani","sequence":"additional","affiliation":[{"name":"Division of Plant Physiology, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viswanathan","family":"Chinnusamy","sequence":"additional","affiliation":[{"name":"Division of Plant Physiology, Indian Council of Agricultural Research (ICAR)\u2014Indian Agricultural Research Institute (IARI), Pusa, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote Sensing for Agricultural Applications: A Meta-Review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.foreco.2008.05.032","article-title":"Methodology Comparison for Slope Correction in Canopy Leaf Area Index Estimation Using Hemispherical Photography","volume":"256","author":"Gonsamo","year":"2008","journal-title":"For. Ecol. Manage."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S0034-4257(99)00056-5","article-title":"Direct and Indirect Estimation of Leaf Area Index, FAPAR, and Net Primary Production of Terrestrial Ecosystems","volume":"70","author":"Gower","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liang, L., Geng, D., Yan, J., Qiu, S., Di, L., Wang, S., Xu, L., Wang, L., Kang, J., and Li, L. (2020). Estimating Crop Lai Using Spectral Feature Extraction and the Hybrid Inversion Method. Remote Sens., 12.","DOI":"10.3390\/rs12213534"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.agrformet.2018.11.033","article-title":"Review of Indirect Optical Measurements of Leaf Area Index: Recent Advances, Challenges, and Perspectives","volume":"265","author":"Yan","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.agrformet.2009.11.009","article-title":"An indirect method of estimating leaf area index in Jatropha curcas L. using LAI-2000 Plant Canopy Analyzer","volume":"150","author":"Behera","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1111\/avsc.12181","article-title":"Estimating Leaf Area Index in Tree Species Using the Pocket LAI Smart App","volume":"18","author":"Orlando","year":"2015","journal-title":"Appl. Veg. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.fcr.2013.09.024","article-title":"Comparison of Leaf Area Index Estimates by Ceptometer and PocketLAI Smart App in Canopies with Different Structures","volume":"155","author":"Francone","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, X., Yang, Q., Chen, X., and Ding, Z. (2023). An Approach for Joint Estimation of Grassland Leaf Area Index and Leaf Chlorophyll Content from UAV Hyperspectral Data. Remote Sens., 15.","DOI":"10.3390\/rs15102525"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"126664","DOI":"10.1016\/j.eja.2022.126664","article-title":"A Comparison of Methods to Estimate Leaf Area Index Using Either Crop-Specific or Generic Proximal Hyperspectral Datasets","volume":"142","author":"Nie","year":"2023","journal-title":"Eur. J. Agron."},{"key":"ref_11","first-page":"554","article-title":"Spectral Band Selection for Vegetation Properties Retrieval Using Gaussian Processes Regression","volume":"52","author":"Verrelst","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","first-page":"380","article-title":"Retrieval of Leaf Area Index Using IRS-P6, LISS-III Data and Validation of MODIS LAI Product (MOD15 V5) over Trans Gangetic Plains of India","volume":"83","author":"Tripathi","year":"2013","journal-title":"Indian J. Agric. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"376","DOI":"10.9755\/ejfa.v25i5.11580","article-title":"Developing Vegetation Health Index from Biophysical Variables Derived Using MODIS Satellite Data in the Trans-Gangetic Plains of India","volume":"25","author":"Tripathi","year":"2013","journal-title":"Emirates J. Food Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1093\/jexbot\/51.suppl_1.329","article-title":"Five Ways to Stay Green","volume":"51","author":"Thomas","year":"2000","journal-title":"J. Exp. Bot."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1271\/bbb.61.2025","article-title":"New Physiological Effects of 5-Aminolevulinic Acid in Plants: The Increase of Photosynthesis, Chlorophyll Content, and Plant Growth","volume":"61","author":"Hotta","year":"1997","journal-title":"Biosci. Biotechnol. Biochem."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shibghatallah, M.A.H., Khotimah, S.N., Suhandono, S., Viridi, S., and Kesuma, T. (2013, January 7\u20139). Measuring Leaf Chlorophyll Concentration from Its Color: A Way in Monitoring Environment Change to Plantations. Proceedings of the AIP Conference, Kabupaten Sumedang, Indonesia.","DOI":"10.1063\/1.4820322"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2508","DOI":"10.1111\/pce.12324","article-title":"In Situ Measurement of Leaf Chlorophyll Concentration: Analysis of the Optical\/Absolute Relationship","volume":"37","author":"Parry","year":"2014","journal-title":"Plant Cell Environ."},{"key":"ref_19","first-page":"33","article-title":"Chlorophyll and Nitrogen Estimation Techniques: A Review","volume":"2","author":"Patane","year":"2014","journal-title":"Int. J. Eng. Res. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5621","DOI":"10.1080\/01431161.2010.507257","article-title":"Retrieval of Forest Chlorophyll Content Using Canopy Structure Parameters Derived from Multi-Angle Data: The Measurement Concept of Combining Nadir Hyperspectral and off-Nadir Multispectral Data","volume":"32","author":"Simic","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.3389\/fpls.2019.01047","article-title":"Estimation of Corn Canopy Chlorophyll Content Using Derivative Spectra in the O2\u2013A Absorption Band","volume":"10","author":"Zhang","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_22","unstructured":"Haboudane, D., Tremblay, N., Miller, J.R., and Vigneault, P. (2008). IEEE Transactions on Geoscience and Remote Sensing, IEEE."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.fcr.2010.01.010","article-title":"Measuring and Predicting Canopy Nitrogen Nutrition in Wheat Using a Spectral Index-The Canopy Chlorophyll Content Index (CCCI)","volume":"116","author":"Fitzgerald","year":"2010","journal-title":"Field Crops Res."},{"key":"ref_24","first-page":"103","article-title":"A Visible Band Index for Remote Sensing Leaf Chlorophyll Content at the Canopy Scale","volume":"21","author":"Hunt","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.rse.2004.01.017","article-title":"Hyperspectral Indices and Model Simulation for Chlorophyll Estimation in Open-Canopy Tree Crops","volume":"90","author":"Miller","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Grahn, H.F., and Geladi, P. (2007). Techniques and Applications of Hyperspectral Image Analysis, John Wiley & Sons.","DOI":"10.1002\/9780470010884"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11947-016-1817-8","article-title":"Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products","volume":"10","author":"Ravikanth","year":"2017","journal-title":"Food Bioprocess Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"837200","DOI":"10.3389\/fpls.2022.837200","article-title":"Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review","volume":"13","author":"Ma","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105621","DOI":"10.1016\/j.compag.2020.105621","article-title":"Hyperspectral Imaging and 3D Technologies for Plant Phenotyping: From Satellite to Close-Range Sensing","volume":"175","author":"Liu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Negash, L., Kim, H.Y., and Choi, H.L. (2019, January 1\u20133). Emerging UAV Applications in Agriculture. Proceedings of the 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA 2019), Daejeon, Republic of Korea.","DOI":"10.1109\/RITAPP.2019.8932853"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1108\/AEAT-01-2018-0056","article-title":"UAV Application for Precision Agriculture","volume":"91","author":"Perz","year":"2019","journal-title":"Aircr. Eng. Aerosp. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107148","DOI":"10.1016\/j.comnet.2020.107148","article-title":"A Compilation of UAV Applications for Precision Agriculture","volume":"172","author":"Sarigiannidis","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11676-015-0088-y","article-title":"Drone Remote Sensing for Forestry Research and Practices","volume":"26","author":"Tang","year":"2015","journal-title":"J. For. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"de Castro, A.I., Shi, Y., Maja, J.M., and Pe\u00f1a, J.M. (2021). Uavs for Vegetation Monitoring: Overview and Recent Scientific Contributions. Remote Sens., 13.","DOI":"10.3390\/rs13112139"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s12524-011-0129-8","article-title":"Inversion of PROSAIL Model for Retrieval of Plant Biophysical Parameters","volume":"40","author":"Tripathi","year":"2012","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_37","first-page":"107","article-title":"Inversion of Radiative Transfer Model for Retrieval of Wheat Biophysical Parameters from Broadband Reflectance Measurements","volume":"3","author":"Sehgal","year":"2016","journal-title":"Inf. Process. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5504","DOI":"10.1007\/s10668-020-00827-6","article-title":"Sensitivity Analysis of Artificial Neural Network for Chlorophyll Prediction Using Hyperspectral Data","volume":"23","author":"Srivastava","year":"2021","journal-title":"Environ. Dev. Sustain."},{"key":"ref_39","first-page":"139","article-title":"Wheat Leaf Area Index Inversion with Hyperspectral Remote Sensing Based on Support Vector Regression Algorithm","volume":"29","author":"Lin","year":"2013","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1515\/intag-2015-0019","article-title":"Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters","volume":"29","author":"Mridha","year":"2015","journal-title":"Int. Agrophys."},{"key":"ref_41","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_42","first-page":"102242","article-title":"Mapping Leaf Area Index in a Mixed Temperate Forest Using Fenix Airborne Hyperspectral Data and Gaussian Processes Regression","volume":"95","author":"Xie","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2013.2238242","article-title":"Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions","volume":"52","author":"Verrelst","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","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_45","first-page":"12","article-title":"Inversion of the PROSAIL Model to Estimate Leaf Area Index of Maize, Potato, and Sunflower Fields from Unmanned Aerial Vehicle Hyperspectral Data","volume":"26","author":"Duan","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","first-page":"103128","article-title":"Improving Leaf Chlorophyll Content Estimation through Constrained PROSAIL Model from Airborne Hyperspectral and LiDAR Data","volume":"115","author":"Xu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/JSTARS.2014.2298752","article-title":"Toward a Semiautomatic Machine Learning Retrieval of Biophysical Parameters","volume":"7","author":"Caicedo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","first-page":"102027","article-title":"Estimation of Leaf Area Index Using PROSAIL Based LUT Inversion, MLRA-GPR and Empirical Models: Case Study of Tropical Deciduous Forest Plantation, North India","volume":"86","author":"Sinha","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_50","first-page":"102454","article-title":"Estimating the Phenological Dynamics of Irrigated Rice Leaf Area Index Using the Combination of PROSAIL and Gaussian Process Regression","volume":"102","author":"Adeluyi","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.isprsjprs.2013.09.012","article-title":"Gaussian Processes Uncertainty Estimates in Experimental Sentinel-2 LAI and Leaf Chlorophyll Content Retrieval","volume":"86","author":"Verrelst","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1109\/LGRS.2013.2279695","article-title":"Retrieval of Biophysical Parameters with Heteroscedastic Gaussian Processes","volume":"11","author":"Titsias","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.isprsjprs.2021.06.017","article-title":"Mapping Landscape Canopy Nitrogen Content from Space Using PRISMA Data","volume":"178","author":"Verrelst","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2017.08.012","article-title":"Hyperspectral Dimensionality Reduction for Biophysical Variable Statistical Retrieval","volume":"132","author":"Verrelst","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Berger, K., Hank, T., Halabuk, A., Rivera-Caicedo, J.P., Wocher, M., Mojses, M., Gerh\u00e1tov\u00e1, K., Tagliabue, G., Dolz, M.M., and Venteo, A.B.P. (2021). Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224711"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Pascual-Venteo, A.B., Portal\u00e9s, E., Berger, K., Tagliabue, G., Garcia, J.L., P\u00e9rez-Suay, A., Rivera-Caicedo, J.P., and Verrelst, J. (2022). Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data. Remote Sens., 14.","DOI":"10.3390\/rs14102448"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2022.09.003","article-title":"Retrieval of Carbon Content and Biomass from Hyperspectral Imagery over Cultivated Areas","volume":"193","author":"Wocher","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Abdelbaki, A., Schlerf, M., Retzlaff, R., Machwitz, M., Verrelst, J., and Udelhoven, T. (2021). Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging. Remote Sens., 13.","DOI":"10.3390\/rs13091748"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"108449","DOI":"10.1016\/j.fcr.2022.108449","article-title":"Radiative Transfer Model Inversion Using High-Resolution Hyperspectral Airborne Imagery\u2014Retrieving Maize LAI to Access Biomass and Grain Yield","volume":"282","author":"Kayad","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Chakhvashvili, E., Siegmann, B., Muller, O., Verrelst, J., Bendig, J., Kraska, T., and Rascher, U. (2022). Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy. Remote Sens., 14.","DOI":"10.3390\/rs14051247"},{"key":"ref_61","first-page":"150","article-title":"Radiative Transfer Models (RTMs) for Field Phenotyping Inversion of Rice Based on UAV Hyperspectral Remote Sensing","volume":"10","author":"Yu","year":"2017","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1104\/pp.24.1.1","article-title":"Copper Enzymes in Isolated Chloroplasts. Polyphenoloxidase in Beta vulgaris","volume":"24","author":"Arnon","year":"1949","journal-title":"Plant Physiol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/24705357.2021.1938255","article-title":"Velocity Uncertainty Quantification Based on Riparian Vegetation Indices in Open Channels Colonized by Phragmites Australis","volume":"7","author":"Lama","year":"2022","journal-title":"J. Ecohydraulics"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote Estimation of Canopy Chlorophyll Content in Crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e6926","DOI":"10.7717\/peerj.6926","article-title":"Combining UAV-Based Hyperspectral Imagery and Machine Learning Algorithms for Soil Moisture Content Monitoring","volume":"7","author":"Ge","year":"2019","journal-title":"PeerJ"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"107","DOI":"10.14358\/PERS.22-00089R2","article-title":"Unmanned Aerial Vehicle (UAV)\u2013Based Imaging Spectroscopy for Predicting Wheat Leaf Nitrogen","volume":"89","author":"Sahoo","year":"2023","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chancia, R., Bates, T., Heuvel, J.V., and van Aardt, J. (2021). Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13214489"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wei, L., Yu, M., Liang, Y., Yuan, Z., Huang, C., Li, R., and Yu, Y. (2019). Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11172011"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s12518-014-0149-5","article-title":"Systematic Approach towards Extracting Endmember Spectra from Hyperspectral Image Using PPI and SMACC and Its Evaluation Using Spectral Library","volume":"7","author":"Aggarwal","year":"2015","journal-title":"Appl. Geomatics"},{"key":"ref_70","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_71","unstructured":"Mridha, N. (2014). Assessing Crop Biophysical Parameters from Hyper-Spectral and Multispectral Remote Sensing and Multispectral Remote Sensing Data through Radiative Transfer Modeling, Indian Agricultural Research Institute."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s12524-014-0411-7","article-title":"Study of the Anisotropic Reflectance Behaviour of Wheat Canopy to Evaluate the Performance of Radiative Transfer Model PROSAIL5B","volume":"43","author":"Chakraborty","year":"2015","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s12524-010-0010-1","article-title":"Relationship of Bidirectional Reflectance of Wheat with Biophysical Parameters and Its Radiative Transfer Modeling Using Prosail","volume":"38","author":"Barman","year":"2010","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_74","first-page":"W3","article-title":"Remote Sensing Derived Composite Vegetation Health Index Through Inversion of Prosail for Monitoring of Wheat Growth in Trans Gangetic Plains of India","volume":"38","author":"Tripathi","year":"2009","journal-title":"ISPRS Arch. XXXVIII-8\/W3 Work. Proc. Impact Clim. Chang. Agric."},{"key":"ref_75","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"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Verrelst, J., Romijn, E., and Kooistra, L. (2012). Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS\/PROBA Data. Remote Sens., 4.","DOI":"10.3390\/rs4092866"},{"key":"ref_77","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Est\u00e9vez, J., Berger, K., Vicent, J., Rivera-Caicedo, J.P., Wocher, M., and Verrelst, J. (2021). Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow. Remote Sens., 13.","DOI":"10.3390\/rs13081589"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s11104-022-05506-1","article-title":"Improving the Remote Estimation of Soil Organic Carbon in Complex Ecosystems with Sentinel-2 and GIS Using Gaussian Processes Regression","volume":"479","author":"Delegido","year":"2022","journal-title":"Plant Soil"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Reyes-Mu\u00f1oz, P., Pipia, L., Salinero-Delgado, M., De Grave, C., Est\u00e9vez, J., Belda, S., and Verrelst, J. (2021, January 11\u201316). Mapping essential vegetation variables over europe using gaussian process regression and sentinel-3 data in Google Earth Engine. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553717"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, J., and Du, L. (2021). Analyzing the Effects of Hyperspectral Zhuhai-1 Band Combinations on Lai Estimation Based on the Prosail Model. Sensors, 21.","DOI":"10.3390\/s21051869"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Pipia, L., Amin, E., Belda, S., Salinero-Delgado, M., and Verrelst, J. (2021). Green Lai Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13030403"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Berger, K., Rivera Caicedo, J.P., Martino, L., Wocher, M., Hank, T., 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_84","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":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/LGRS.2016.2560799","article-title":"Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval","volume":"13","author":"Verrelst","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_86","first-page":"423","article-title":"A Hybrid Active Learning and Progressive Sampling Algorithm","volume":"8","author":"ElRafey","year":"2018","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.chemolab.2011.07.007","article-title":"A Two-Stage Regression Approach for Spectroscopic Quantitative Analysis","volume":"109","author":"Douak","year":"2011","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.apenergy.2012.09.055","article-title":"Kernel Ridge Regression with Active Learning for Wind Speed Prediction","volume":"103","author":"Douak","year":"2013","journal-title":"Appl. Energy"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5636","DOI":"10.1080\/01431161.2021.2024912","article-title":"Quantifying Mangrove Leaf Area Index from Sentinel-2 Imagery Using Hybrid Models and Active Learning","volume":"43","author":"Binh","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"112958","DOI":"10.1016\/j.rse.2022.112958","article-title":"Gaussian Processes Retrieval of Crop Traits in Google Earth Engine Based on Sentinel-2 Top-of-Atmosphere Data","volume":"273","author":"Berger","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Salinero-Delgado, M., Est\u00e9vez, J., Pipia, L., Belda, S., Berger, K., G\u00f3mez, V.P., and Verrelst, J. (2022). Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression. Remote Sens., 14.","DOI":"10.3390\/rs14010146"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.isprsjprs.2020.07.004","article-title":"Gaussian Processes Retrieval of LAI from Sentinel-2 Top-of-Atmosphere Radiance Data","volume":"167","author":"Vicent","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/JSTARS.2012.2222356","article-title":"Gaussian Process Retrieval of Chlorophyll Content from Imaging Spectroscopy Data","volume":"6","author":"Verrelst","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/JSTSP.2011.2139193","article-title":"A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification","volume":"5","author":"Tuia","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Shen, X.J., Wu, H.X., and Zhu, Q. (2012, January 9\u201311). Training Support Vector Machine through Redundant Data Reduction. Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, Wuhan, China. ACM International Conference Proceeding Series.","DOI":"10.1145\/2382336.2382344"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Candiani, G., Tagliabue, G., Panigada, C., Verrelst, J., Picchi, V., Caicedo, J.P.R., 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_97","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1007\/s11119-019-09698-y","article-title":"Mapping Within-Field Leaf Chlorophyll Content in Agricultural Crops for Nitrogen Management Using Landsat-8 Imagery","volume":"21","author":"Croft","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2015.04.013","article-title":"Experimental Sentinel-2 LAI Estimation Using Parametric, Non-Parametric and Physical Retrieval Methods\u2014A Comparison","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5496\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:29:56Z","timestamp":1760131796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5496"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,25]]},"references-count":98,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235496"],"URL":"https:\/\/doi.org\/10.3390\/rs15235496","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,25]]}}}