{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:18:06Z","timestamp":1774505886999,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T00:00:00Z","timestamp":1516665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use\/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland\u2013Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model\u2019s accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5\u20134.9 g\u00b7kg\u22121; ratio of performance to deviation (RPD) = 1.4\u20131.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within- and between-field SOC variability.<\/jats:p>","DOI":"10.3390\/rs10020153","type":"journal-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T13:06:51Z","timestamp":1516712811000},"page":"153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database"],"prefix":"10.3390","volume":"10","author":[{"given":"Fabio","family":"Castaldi","sequence":"first","affiliation":[{"name":"Georges Lema\u00eetre Centre for Earth and Climate, Earth and Life Institute, Universite Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8600-5168","authenticated-orcid":false,"given":"Sabine","family":"Chabrillat","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Potsdam\u2014Deutsches GeoForschungsZentrum GFZ, 14473 Potsdam, Germany"}]},{"given":"Arwyn","family":"Jones","sequence":"additional","affiliation":[{"name":"European Commission, Directorate General Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"given":"Kristin","family":"Vreys","sequence":"additional","affiliation":[{"name":"Flemish Institute for Technological Research, VITO, 2400 Mol, Belgium"}]},{"given":"Bart","family":"Bomans","sequence":"additional","affiliation":[{"name":"Flemish Institute for Technological Research, VITO, 2400 Mol, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4007-0241","authenticated-orcid":false,"given":"Bas","family":"Van Wesemael","sequence":"additional","affiliation":[{"name":"Georges Lema\u00eetre Centre for Earth and Climate, Earth and Life Institute, Universite Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/S0034-4257(02)00060-3","article-title":"Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution","volume":"82","author":"Chabrillat","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15561","DOI":"10.3390\/rs71115561","article-title":"Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data","volume":"7","author":"Castaldi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(96)00120-4","article-title":"The Reflectance Spectra of Organic Matter in the Visible Near-Infrared and Short Wave Infrared Region (400\u20132500 nm) during a Controlled Decomposition Process","volume":"61","author":"Inbar","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11806-009-0160-x","article-title":"Spectral features of soil organic matter","volume":"12","author":"He","year":"2009","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.still.2006.03.009","article-title":"On-line measurement of some selected soil properties using a VIS\u2013NIR sensor","volume":"93","author":"Mouazen","year":"2007","journal-title":"Soil Tillage Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.geoderma.2009.12.025","article-title":"Using data mining to model and interpret soil diffuse reflectance spectra","volume":"158","author":"Rossel","year":"2010","journal-title":"Geoderma"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1016\/j.rse.2008.09.019","article-title":"Using Imaging Spectroscopy to study soil properties","volume":"113","author":"Chabrillat","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_8","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":"Chemom. Intell. Lab. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.025","article-title":"Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon","volume":"179","author":"Castaldi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.geoderma.2006.03.050","article-title":"High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures","volume":"136","author":"Selige","year":"2006","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.geoderma.2007.12.009","article-title":"Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils","volume":"144","author":"Stevens","year":"2008","journal-title":"Geoderma"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.geoderma.2012.05.023","article-title":"Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis\u2013NIR data","volume":"189","author":"Gomez","year":"2012","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.geoderma.2012.01.017","article-title":"Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale","volume":"175","author":"Hbirkou","year":"2012","journal-title":"Geoderma"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.2136\/sssaj2012.0054","article-title":"Soil Organic Carbon Predictions by Airborne Imaging Spectroscopy: Comparing Cross-Validation and Validation","volume":"76","author":"Stevens","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1111\/ejss.12203","article-title":"Estimation of soil organic carbon from airborne hyperspectral thermal infrared data: A case study","volume":"65","author":"Pascucci","year":"2014","journal-title":"Eur. J. Soil Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Steinberg, A., Chabrillat, S., Stevens, A., Segl, K., and Foerster, S. (2016). Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution. Remote Sens., 8.","DOI":"10.3390\/rs8070613"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, H., Shi, T., Chen, Y., Wang, J., Fei, T., and Wu, G. (2017). Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression. Remote Sens., 9.","DOI":"10.3390\/rs9010029"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1109\/TGRS.2003.812908","article-title":"Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping","volume":"41","author":"Kruse","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1111\/ejss.12202","article-title":"Estimation of soil properties at the field scale from satellite data: A comparison between spatial and non-spatial techniques","volume":"65","author":"Castaldi","year":"2014","journal-title":"Eur. J. Soil Sci."},{"key":"ref_20","first-page":"19","article-title":"Limitations of Hyperspectral Earth Observation on Small Satellites","volume":"1","author":"Villafranca","year":"2012","journal-title":"J. Small Satell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Casa, R., Castaldi, F., Pascucci, S., Basso, B., and Pignatti, S. (2013). Geophysical and Hyperspectral Data Fusion Techniques for In-Field Estimation of Soil Properties. Vadose Zone J., 12.","DOI":"10.2136\/vzj2012.0201"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.geoderma.2012.12.016","article-title":"A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing","volume":"197","author":"Casa","year":"2013","journal-title":"Geoderma"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"073587","DOI":"10.1117\/1.JRS.7.073587","article-title":"Estimation of agricultural soil properties with imaging and laboratory spectroscopy","volume":"7","author":"Zhang","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Pignatti, S., Acito, N., Amato, U., Casa, R., Castaldi, F., Coluzzi, R., De Bonis, R., Diani, M., Imbrenda, V., and Laneve, G. (2015, January 26\u201331). Environmental products overview of the Italian hyperspectral prisma mission: The SAP4PRISMA project. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326701"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Houborg, R., Anderson, M., Gao, F., Schull, M., and Cammalleri, C. (2012, January 22\u201327). Monitoring water and carbon fluxes at fine spatial scales using HyspIRI-like measurements. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351975"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tanii, J., Iwasaki, A., Kawashima, T., and Inada, H. (2012, January 22\u201327). Results of evaluation model of Hyperspectral Imager Suite (HISUI). Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351619"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Staenz, K., Mueller, A., and Heiden, U. (2013, January 21\u201326). Overview of terrestrial imaging spectroscopy missions. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium\u2014IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723584"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.geoderma.2008.06.011","article-title":"Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study","volume":"146","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"988","DOI":"10.2136\/sssaj2002.9880","article-title":"Development of Reflectance Spectral Libraries for Characterization of Soil Properties","volume":"66","author":"Shepherd","year":"2002","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Knadel, M., Deng, F., Thomsen, A., and Greve, M. (2012). Development of a Danish national Vis-NIR soil spectral library for soil organic carbon determination. Digital Soil Assessments and Beyond, CRC Press.","DOI":"10.1201\/b12728-79"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1255\/jnirs.1053","article-title":"Predicting soil organic carbon at field scale using a national soil spectral library","volume":"21","author":"Peng","year":"2013","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.still.2015.07.008","article-title":"Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy?","volume":"155","author":"Guerrero","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_34","unstructured":"Garrity, D., and Bindraban, P. (2015). ICRAF A Globally Distributed Soil Spectral Library Visible Near Infrared Diffuse Reflectance Spectra, ICRAF (World Agroforestry Centre)\/ISRIC (World Soil Information) Spectral Library."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7409","DOI":"10.1007\/s10661-013-3109-3","article-title":"The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union","volume":"185","author":"Jones","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.earscirev.2016.01.012","article-title":"A global spectral library to characterize the world\u2019s soil","volume":"155","author":"Behrens","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_37","first-page":"6383","article-title":"Using LUCAS topsoil database to estimate soil organic carbon content in croplands sampled in Belgium and Luxembourg","volume":"19","author":"Castaldi","year":"2017","journal-title":"Eur. J. Soil Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.2135\/cropsci1991.0011183X003100060064x","article-title":"New Standardization and Calibration Procedures for Nirs Analytical Systems","volume":"31","author":"Shenk","year":"1991","journal-title":"Crop Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0924-2031(95)00055-0","article-title":"Standardisation of near-infrared spectrometric instruments: A review","volume":"11","author":"Bouveresse","year":"1996","journal-title":"Vib. Spectrosc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1255\/jnirs.309","article-title":"Standardisation and calibration transfer for near infrared instruments: A review","volume":"9","author":"Fearn","year":"2001","journal-title":"J. Near Infrared Spec."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.geoderma.2007.04.021","article-title":"Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed","volume":"140","author":"Brown","year":"2007","journal-title":"Geoderma"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.geoderma.2011.09.008","article-title":"Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon","volume":"167","author":"Minasny","year":"2011","journal-title":"Geoderma"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1080\/01431161.2016.1148291","article-title":"Normalizing reflectance from different spectrometers and protocols with an internal soil standard","volume":"37","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.geomorph.2010.11.008","article-title":"Linking spatial patterns of soil organic carbon to topography\u2014A case study from south-eastern Spain","volume":"126","author":"Schwanghart","year":"2011","journal-title":"Geomorphology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.geoderma.2017.03.011","article-title":"Clay content mapping from airborne hyperspectral Vis-NIR data by transferring a laboratory regression model","volume":"298","author":"Nouri","year":"2017","journal-title":"Geoderma"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.geoderma.2009.11.032","article-title":"Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy","volume":"158","author":"Stevens","year":"2010","journal-title":"Geoderma"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.geoderma.2007.06.013","article-title":"Regional assessment of soil organic carbon changes under agriculture in Southern Belgium (1955\u20132005)","volume":"141","author":"Goidts","year":"2007","journal-title":"Geoderma"},{"key":"ref_48","first-page":"299","article-title":"Inorganic Carbon Analysis by Modified Pressure-Calcimeter Method","volume":"66","author":"Sherrod","year":"2002","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"100","DOI":"10.2307\/2346830","article-title":"Algorithm AS 136: A K-Means Clustering Algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"Appl. Stat."},{"key":"ref_50","first-page":"5","article-title":"Data acquisition with the APEX hyperspectral sensor","volume":"20","author":"Vreys","year":"2016","journal-title":"Misc. Geogr."},{"key":"ref_51","unstructured":"Biesemans, J., Sterckx, S., Knaeps, E., Vreys, K., Adriaensen, S., Hooy-berghs, J., Meuleman, K., Kempeneers, P., Deronde, B., and Everaerts, J. (2007, January 23\u201325). Image processing workflows for airborne remote sensing. Proceedings of the 5th EARSeL Workshop on Imaging Spectroscopy, Bruges, Belgium."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.isprsjprs.2009.01.006","article-title":"Calibration facility for airborne imaging spectrometers","volume":"64","author":"Gege","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","first-page":"11","article-title":"Geometric correction of APEX hyperspectral data","volume":"20","author":"Vreys","year":"2016","journal-title":"Misc. Geogr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(91)90046-9","article-title":"Removal of atmospheric influences on satellite-borne imagery: A radiative transfer approach","volume":"37","author":"Hovenier","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_55","unstructured":"Toolkit, I. (1996). Remote sensing algorithm development. Operationalization of Atmospheric Correction Methods for Tidal and Inland Waters, Netherlands Remote Sensing Board (BCRS)."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2010.12.018","article-title":"The use of remote sensing in soil and terrain mapping\u2014A review","volume":"162","author":"Mulder","year":"2011","journal-title":"Geoderma"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.soilbio.2013.10.022","article-title":"Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach","volume":"68","author":"Nocita","year":"2014","journal-title":"Soil Biol. Biochem."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.geoderma.2016.04.021","article-title":"National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy","volume":"276","author":"Clairotte","year":"2016","journal-title":"Geoderma"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barth\u00e8s, B., Ben Dor, E., Brown, D.J., Clairotte, M., and Csorba, A. (2015). Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring, Academic Press Inc.","DOI":"10.1016\/bs.agron.2015.02.002"},{"key":"ref_61","unstructured":"Woodcock, C.E. (2006). Uncertainty in Remote Sensing. Uncertainty in Remote Sensing and GIS, John Wiley & Sons, Ltd."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bradley, K.C., Bowen, S., Gross, K.C., Marciniak, M.A., and Perram, G.P. (2009, January 7\u201314). Imaging Fourier transform spectrometry of jet engine exhaust with the telops FIRST-MWE. Proceedings of the 2009 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2009.4839444"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kanning, M., Siegmann, B., and Jarmer, T. (2016). Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations. Remote Sens., 8.","DOI":"10.3390\/rs8110927"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/153\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:52:12Z","timestamp":1760194332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,23]]},"references-count":63,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["rs10020153"],"URL":"https:\/\/doi.org\/10.3390\/rs10020153","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,23]]}}}