{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:27:27Z","timestamp":1771655247008,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Ministry of Food and Agriculture (BMEL)","award":["28DE114F18\/28DE114F22"],"award-info":[{"award-number":["28DE114F18\/28DE114F22"]}]},{"name":"Federal Office for Agriculture and Food (BLE)","award":["28DE114F18\/28DE114F22"],"award-info":[{"award-number":["28DE114F18\/28DE114F22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of \u2019MISPEL\u2019, a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area index (LAI) models for winter wheat are developed based on a hyperspectral (MISPELFR) and a simulated Sentinel-2 (MISPELS2) SpecLib applying four nonparametric methods. Finally, an independent Sentinel-2 model evaluation at the DEMMIN test site in Germany is conducted, including a comparison with the commonly used SNAP-LAI product. To date, MISPEL comprises a set of 1411 spectra of ten crops and more than 6800 associated reference measurements. Cross-validations of winter wheat LAI models revealed that Elastic-net generalized linear model (GLMNET) and Gaussian process (GP) regressions outperformed partial least squares (PLS) and random forest (RF) regressions, showing RSQ values up to 0.86 and a minimal NRMSE of 0.21 using MISPELFR. GLMNET and GP models based on MISPELS2 further outperformed SNAP-based LAI estimates derived for the external validation site. Thus, it is concluded that the presented SpecLib \u2019MISPEL\u2019 and applied methodology have a very high potential for deriving diverse crop traits of multiple crops in view of most recent and future multi-, super-, and hyperspectral satellite missions.<\/jats:p>","DOI":"10.3390\/rs15143664","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T01:12:28Z","timestamp":1690161148000},"page":"3664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0780-5667","authenticated-orcid":false,"given":"Peter","family":"Borrmann","sequence":"first","affiliation":[{"name":"Julius K\u00fchn Institute (JKI)\u2013Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4167-5437","authenticated-orcid":false,"given":"Patric","family":"Brandt","sequence":"additional","affiliation":[{"name":"Julius K\u00fchn Institute (JKI)\u2013Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5042-8548","authenticated-orcid":false,"given":"Heike","family":"Gerighausen","sequence":"additional","affiliation":[{"name":"Julius K\u00fchn Institute (JKI)\u2013Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107609","DOI":"10.1016\/j.agrformet.2019.06.008","article-title":"Assimilation of remote sensing into crop growth models: Current status and perspectives","volume":"276\u2013277","author":"Huang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2007.12.003","article-title":"Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland","volume":"112","author":"Darvishzadeh","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.3390\/s90402719","article-title":"Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors","volume":"9","author":"Zheng","year":"2009","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_6","unstructured":"Gerighausen, H., Lilienthal, H., Jarmer, T., and Siegmann, B. (2015, January 14\u201316). Evaluation of leaf area index and dry matter predictions for crop growth modelling and yield esti-mation based on field reflectance measurements. Proceedings of the 9th EARSeL Imaging Spectroscopy Workshop, Luxembourg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4519","DOI":"10.1080\/01431161.2015.1084438","article-title":"Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data","volume":"36","author":"Siegmann","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","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."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Upreti, D., Huang, W., Kong, W., Pascucci, S., Pignatti, S., Zhou, X., Ye, H., and Casa, R. (2019). A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2. Remote Sens., 11.","DOI":"10.3390\/rs11050481"},{"key":"ref_10","first-page":"187","article-title":"Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery","volume":"80","author":"Xie","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1080\/22797254.2020.1839359","article-title":"Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria","volume":"54","author":"Kamenova","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Caballero, G., Pezzola, A., Winschel, C., Casella, A., Angonova, P.S., Rivera-Caicedo, J.P., Berger, K., Verrelst, J., and Delegido, J. (2022). Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14184531"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/JSTARS.2011.2176468","article-title":"Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content","volume":"5","author":"Clevers","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liang, L., Di, L., Huang, T., Wang, J., Lin, L., Wang, L., and Yang, M. (2018). Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm. Remote Sens., 10.","DOI":"10.3390\/rs10121940"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Perich, G., Aasen, H., Verrelst, J., Argento, F., Walter, A., and Liebisch, F. (2021). Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data. Remote Sens., 13.","DOI":"10.3390\/rs13122404"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., and Xu, B. (2017). Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Remote Sens., 9.","DOI":"10.3390\/rs9070708"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.isprsjprs.2019.01.023","article-title":"Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat","volume":"149","author":"Prey","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"617009","DOI":"10.3389\/fsufs.2020.617009","article-title":"Food Security and the Dynamics of Wheat and Maize Value Chains in Africa and Asia","volume":"4","author":"Grote","year":"2021","journal-title":"Front. Sustain. Food Syst."},{"key":"ref_21","unstructured":"FAO (2023). Crops and Livestock Products, FAO."},{"key":"ref_22","unstructured":"Bundesministerium f\u00fcr Ern\u00e4hrung und Landwirtschaft (2022). Daten und Fakten: Land-, Forst- und Ern\u00e4hrungswirtschaft mit Fischerei und Wein- und Gartenbau, Bundesministerium f\u00fcr Ern\u00e4hrung und Landwirtschaft."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.rse.2015.06.012","article-title":"An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities","volume":"167","author":"Lee","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.actaastro.2009.03.077","article-title":"The PRISMA payload optomechanical design, a high performance instrument for a new hyperspectral mission","volume":"65","author":"Labate","year":"2009","journal-title":"Acta Astronaut."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nieke, J., and Rast, M. (2018, January 22\u201327). Towards the Copernicus Hyperspectral Imaging Mission For The Environment (CHIME). Proceedings of the IGARSS, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518384"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Teucher, M., Th\u00fcrkow, D., Alb, P., and Conrad, C. (2022). Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture\u2014Progress towards Digital Agriculture. Remote Sens., 14.","DOI":"10.3390\/rs14020393"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/01431160701241779","article-title":"Development of a crop\u2013specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery","volume":"29","author":"Rao","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.rse.2008.11.007","article-title":"The ASTER spectral library version 2.0","volume":"113","author":"Baldridge","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.cageo.2008.03.015","article-title":"The spectral database SPECCHIO for improved long-term usability and data sharing","volume":"35","author":"Hueni","year":"2009","journal-title":"Comput. Geosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1016\/j.jenvman.2007.06.028","article-title":"Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing","volume":"90","author":"Zomer","year":"2009","journal-title":"J. Environ. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1080\/10106049.2011.623792","article-title":"Existence of characteristic spectral signatures for agricultural crops\u2014potential for automated crop mapping by hyperspectral imaging","volume":"27","author":"Nidamanuri","year":"2012","journal-title":"Geocarto Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.isprsjprs.2014.05.005","article-title":"Derivation of an urban materials spectral library through emittance and reflectance spectroscopy","volume":"94","author":"Kotthaus","year":"2014","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2472","DOI":"10.3390\/ijgi4042472","article-title":"Towards a Standard Plant Species Spectral Library Protocol for Vegetation Mapping: A Case Study in the Shrubland of Do\u00f1ana National Park","volume":"4","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1007\/s11629-016-4101-5","article-title":"Lithological mapping with multispectral data\u2014setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan","volume":"14","author":"Fuchs","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2020.04.008","article-title":"Construction of a plant spectral library based on an optimised feature selection method","volume":"195","author":"Zhang","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"de Peppo, M., Taramelli, A., Boschetti, M., Mantino, A., Volpi, I., Filipponi, F., Tornato, A., Valentini, E., and Ragaglini, G. (2021). Non-Parametric Statistical Approaches for Leaf Area Index Estimation from Sentinel-2 Data: A Multi-Crop Assessment. Remote Sens., 13.","DOI":"10.3390\/rs13142841"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., Delegido, J., van Wittenberghe, S., Rinaldi, M., and Moreno, J. (2019). Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI). Sensors, 19.","DOI":"10.3390\/s19040904"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal Component Analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial Least-Squares Regression: A Tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2013.10.010","article-title":"Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements","volume":"100","author":"Fu","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.03.033","article-title":"Multi-method ensemble selection of spectral bands related to leaf biochemistry","volume":"164","author":"Feilhauer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1007\/s11119-016-9455-8","article-title":"Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression","volume":"18","author":"Foster","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, B., Chen, J., Ju, W., Qiu, F., Zhang, Q., Fang, M., and Chen, F. (2017). Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation. Remote Sens., 9.","DOI":"10.3390\/rs9030291"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/00401706.1970.10488635","article-title":"Ridge Regression: Applications to Nonorthogonal Problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. Methodol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1139\/X10-180","article-title":"Penalized regression techniques for prediction: A case study for predicting tree mortality using remotely sensed vegetation indicesThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time","volume":"41","author":"Lazaridis","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2014.11.007","article-title":"Quantifying dwarf shrub biomass in an arid environment: Comparing empirical methods in a high dimensional setting","volume":"158","author":"Zandler","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","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_53","first-page":"2013","article-title":"Retrieval of Biophysical Parameters with Heteroscedastic Gaussian Processes","volume":"11","author":"Titsias","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2014.12.011","article-title":"Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A\/1B CCD images and recurrent neural network","volume":"102","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.isprsjprs.2016.07.001","article-title":"Retrieval of leaf area index in different plant species using thermal hyperspectral data","volume":"119","author":"Neinavaz","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(02)00035-4","article-title":"Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem","volume":"84","author":"Combal","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., D\u2019Urso, G., Mauser, W., Vuolo, F., and Hank, T. (2018). Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sens., 10.","DOI":"10.3390\/rs10010085"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1051\/agro:2000105","article-title":"Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data","volume":"20","author":"Weiss","year":"2000","journal-title":"Agronomie"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"10321","DOI":"10.3390\/rs70810321","article-title":"Retrieval of Seasonal Leaf Area Index from Simulated EnMAP Data through Optimized LUT-Based Inversion of the PROSAIL Model","volume":"7","author":"Locherer","year":"2015","journal-title":"Remote Sens."},{"key":"ref_61","unstructured":"Weiss, M., Baret, F., and Jay, S. (2020). S2ToolBox Level 2 Products LAI, FAPAR, FCOVER, HAL Open Science."},{"key":"ref_62","unstructured":"Rebenstorf, R.W. (2009). DEMMIN\u2014Teststandort zur Kalibrierung und Validierung von Fernerkundungsmissionen, Self-published."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"955","DOI":"10.2136\/vzj2010.0139","article-title":"A Network of Terrestrial Environmental Observatories in Germany","volume":"10","author":"Zacharias","year":"2011","journal-title":"Vadose Zone J."},{"key":"ref_64","first-page":"41","article-title":"The BBCH system to coding the phenological growth stages of plants\u2013history and publications","volume":"61","author":"Meier","year":"2009","journal-title":"J. Kult."},{"key":"ref_65","unstructured":"VDLUFA (1991). Das VDLUFA Methodenbuch, VDLUFA."},{"key":"ref_66","unstructured":"VDLUFA (1991). Das VDLUFA Methodenbuch, VDLUFA."},{"key":"ref_67","unstructured":"LI-COR Biosciences (2023, July 14). FV2200. Available online: https:\/\/www.licor.com\/env\/products\/leaf_area\/LAI-2200C\/software."},{"key":"ref_68","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Core Team."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v089.i12","article-title":"Hyperspectral Data Analysis in R: The hsdar Package","volume":"89","author":"Lehnert","year":"2019","journal-title":"J. Stat. Softw."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.21105\/joss.01686","article-title":"Welcome to the Tidyverse","volume":"4","author":"Wickham","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization Paths for Generalized Linear Models via Coordinate Descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Bousquet, O., von Luxburg, U., and R\u00e4tsch, G. (2003). Advanced Lectures on Machine Learning, Springer.","DOI":"10.1007\/b100712"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"kernlab-an S4 package for kernel methods in R","volume":"11","author":"Karatzoglou","year":"2004","journal-title":"J. Stat. Softw."},{"key":"ref_74","unstructured":"Liland, K.H., Mevik, B.H., and Wehrens, R. (2023, July 14). pls: Partial Least Squares and Principal Component Regression. Available online: https:\/\/cran.r-project.org\/web\/packages\/pls\/index.html."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_77","unstructured":"Kuhn, M. (2021). caret: Classification and Regression Training."},{"key":"ref_78","unstructured":"CODE-DE (2023, July 14). CODE-DE: The German Access to Copernicus Data. Available online: https:\/\/code-de.org\/en\/."},{"key":"ref_79","unstructured":"Hijmans, R.J. (2023, July 14). terra: Spatial Data Analysis. Available online: https:\/\/CRAN.R-project.org\/package=terra."},{"key":"ref_80","unstructured":"Robinson, D. (2023, July 14). fuzzyjoin: Join Tables Together on Inexact Matching. Available online: https:\/\/cran.r-project.org\/package=fuzzyjoin."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"107062","DOI":"10.1016\/j.ecolind.2020.107062","article-title":"The retrieval of plant functional traits from canopy spectra through RTM-inversions and statistical models are both critically affected by plant phenology","volume":"121","author":"Schiefer","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_82","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":"Estevez","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"111425","DOI":"10.1016\/j.rse.2019.111425","article-title":"Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product","volume":"235","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2017.10.046","article-title":"Spatio-temporal fusion for daily Sentinel-2 images","volume":"204","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"025003","DOI":"10.1088\/1748-9326\/aa572e","article-title":"Relationships between hyperspectral data and components of vegetation biomass in Low Arctic tundra communities at Ivotuk, Alaska","volume":"12","author":"Bratsch","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Frantz, D. (2019). FORCE\u2014Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sens., 11.","DOI":"10.3390\/rs11091124"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Y, G.D., Nair, N.G., Satpathy, P., and Christopher, J. (2019, January 18\u201320). Covariate Shift: A Review and Analysis on Classifiers. Proceedings of the Global Conference for Advancement in Technology (GCAT), Bangalore, India.","DOI":"10.1109\/GCAT47503.2019.8978471"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Segarra, J., Buchaillot, M.L., Araus, J.L., and Kefauver, S.C. (2020). Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy, 10.","DOI":"10.3390\/agronomy10050641"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2019.04.005","article-title":"Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach","volume":"228","author":"Kang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_90","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_91","doi-asserted-by":"crossref","first-page":"113580","DOI":"10.1016\/j.rse.2023.113580","article-title":"From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data","volume":"292","author":"Cherif","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"111673","DOI":"10.1016\/j.rse.2020.111673","article-title":"Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery","volume":"240","author":"Preidl","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"112831","DOI":"10.1016\/j.rse.2021.112795","article-title":"Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany","volume":"269","author":"Schwieder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"112708","DOI":"10.1016\/j.rse.2021.112708","article-title":"From parcel to continental scale\u2014A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations","volume":"266","author":"Verhegghen","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Dhillon, M.S., Dahms, T., K\u00fcbert-Flock, C., Liepa, A., Rummler, T., Arnault, J., Steffan-Dewenter, I., and Ullmann, T. (2023). Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany. Remote Sens., 15.","DOI":"10.3390\/rs15061651"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Tewes, A., Hoffmann, H., Krauss, G., Sch\u00e4fer, F., Kerkhoff, C., and Gaiser, T. (2020). New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations. Agronomy, 10.","DOI":"10.3390\/agronomy10030446"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3664\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:17:14Z","timestamp":1760127434000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,22]]},"references-count":96,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143664"],"URL":"https:\/\/doi.org\/10.3390\/rs15143664","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,22]]}}}