{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:55:33Z","timestamp":1775192133258,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,27]],"date-time":"2019-07-27T00:00:00Z","timestamp":1564185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA","award":["80NSSC18K0336"],"award-info":[{"award-number":["80NSSC18K0336"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3\u20135 days) at moderate spatial resolution (10\u201330 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA\u2019s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016\u20132018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t\/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.<\/jats:p>","DOI":"10.3390\/rs11151768","type":"journal-article","created":{"date-parts":[[2019,7,29]],"date-time":"2019-07-29T03:06:58Z","timestamp":1564369618000},"page":"1768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9039-0174","authenticated-orcid":false,"given":"Sergii","family":"Skakun","sequence":"first","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"},{"name":"College of Information Studies (iSchool), University of Maryland, College Park, MD 20742, USA"},{"name":"NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]},{"given":"Eric","family":"Vermote","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]},{"given":"Belen","family":"Franch","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"},{"name":"NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3119-1175","authenticated-orcid":false,"given":"Jean-Claude","family":"Roger","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"},{"name":"NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]},{"given":"Nataliia","family":"Kussul","sequence":"additional","affiliation":[{"name":"Space Research Institute NAS Ukraine &amp; SSA Ukraine, 03680 Kyiv, Ukraine"}]},{"given":"Junchang","family":"Ju","sequence":"additional","affiliation":[{"name":"Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD 20742, USA"},{"name":"NASA Goddard Space Flight Center Code 618, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]},{"given":"Jeffrey","family":"Masek","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center Code 618, 8800 Greenbelt Road, Greenbelt, MD 20771, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"902","DOI":"10.3390\/rs9090902","article-title":"A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring","volume":"9","author":"Li","year":"2017","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1109\/36.701075","article-title":"The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research","volume":"36","author":"Justice","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9753","DOI":"10.1002\/jgrd.50771","article-title":"Land and cryosphere products from Suomi NPP VIIRS: Overview and status","volume":"118","author":"Justice","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.3390\/rs2061589","article-title":"Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project","volume":"2","author":"Justice","year":"2010","journal-title":"Remote Sens."},{"key":"ref_5","first-page":"65","article-title":"A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products","volume":"52","author":"Johnson","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1109\/TGRS.2015.2466438","article-title":"Evaluating NDVI data continuity between SPOT-VEGETATION and PROBA-V missions for operational yield forecasting in North African countries","volume":"54","author":"Meroni","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2015.02.014","article-title":"Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information","volume":"161","author":"Franch","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"192","article-title":"Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models","volume":"23","author":"Kogan","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.3390\/rs70201482","article-title":"Meeting earth observation requirements for global agricultural monitoring: An evaluation of the revisit capabilities of current and planned moderate resolution optical earth observing missions","volume":"7","author":"Whitcraft","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2014.10.009","article-title":"Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations","volume":"156","author":"Whitcraft","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"112","article-title":"Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine","volume":"76","author":"Franch","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5194\/isprsarchives-XL-7-W3-39-2015","article-title":"Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine","volume":"40","author":"Kolotii","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.agrformet.2015.02.021","article-title":"Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level","volume":"206","author":"Duveiller","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.agrformet.2010.11.012","article-title":"Crop yield forecasting on the Canadian Prairies using MODIS NDVI data","volume":"151","author":"Mkhabela","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2017.04.014","article-title":"Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries","volume":"202","author":"Azzari","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.rse.2018.02.020","article-title":"Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA","volume":"210","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gao, F., Anderson, M., Daughtry, C., and Johnson, D. (2018). Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091489"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1016\/j.rse.2011.04.018","article-title":"LAI assessment of wheat and potato crops by VEN\u03bcS and Sentinel-2 bands","volume":"115","author":"Herrmann","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"163","DOI":"10.3934\/geosci.2017.2.163","article-title":"Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale","volume":"3","author":"Skakun","year":"2017","journal-title":"AIMS Geosci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.rse.2018.06.036","article-title":"Estimating smallholder crops production at village level from Sentinel-2 time series in Mali\u2019s cotton belt","volume":"216","author":"Lambert","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","first-page":"99","article-title":"An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI","volume":"72","author":"Lai","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.rse.2003.11.006","article-title":"Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan","volume":"89","author":"Henebry","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.rse.2005.10.022","article-title":"Green leaf phenology at Landsat resolution: Scaling from the field to the satellite","volume":"100","author":"Fisher","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1016\/j.rse.2010.04.005","article-title":"Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product","volume":"114","author":"Ganguly","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"J\u00f6nsson, P., Cai, Z., Melaas, E., Friedl, M.A., and Eklundh, L. (2018). A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10040635"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Roy, D.P., and Yan, L. (2019). Robust Landsat-based crop time series modelling. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2018.06.038"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring vegetation phenology using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"G04017","DOI":"10.1029\/2006JG000217","article-title":"Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements","volume":"111","author":"Zhang","year":"2006","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_33","first-page":"22","article-title":"Efficiency assessment of using satellite data for crop area estimation in Ukraine","volume":"29","author":"Gallego","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Doxani, G., Vermote, E., Roger, J.C., Gascon, F., Adriaensen, S., Frantz, D., Hagolle, O., Hollstein, A., Kirches, G., and Li, F. (2018). Atmospheric correction inter-comparison exercise. Remote Sens., 10.","DOI":"10.3390\/rs10020352"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens Environ."},{"key":"ref_41","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.5194\/gmd-8-1339-2015","article-title":"Development of the GEOS-5 atmospheric general circulation model: Evolution from MERRA to MERRA2","volume":"8","author":"Molod","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Santamaria-Artigas, A.E., Franch, B., Guillevic, P., Roger, J.-C., Vermote, E.F., and Skakun, S. (2019). Evaluation of Near-Surface Air Temperature from Reanalysis Over the United States and Ukraine: Application to Winter Wheat Yield Forecasting. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/IGARSS.2018.8518644"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.wace.2015.08.001","article-title":"Temperature extremes: Effect on plant growth and development","volume":"10","author":"Hatfield","year":"2015","journal-title":"Weather Clim. Extrem."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2017.04.026","article-title":"Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model","volume":"195","author":"Skakun","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lavreniuk, M., Kussul, N., Skakun, S., Shelestov, A., and Yailymov, B. (2015, January 26\u201331). Regional retrospective high resolution land cover for Ukraine: Methodology and results. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326693"},{"key":"ref_47","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"045002","DOI":"10.1088\/1748-9326\/8\/4\/045002","article-title":"Land surface phenologies and seasonalities using cool earthlight in mid-latitude croplands","volume":"8","author":"Alemu","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/S0168-1923(97)00027-0","article-title":"Growing degree-days: One equation, two interpretations","volume":"87","author":"McMaster","year":"1997","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","unstructured":"Nguyen, L.H., Joshi, D.R., Clay, D.E., and Henebry, G.M. (2018). Characterizing land cover\/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier. Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sun, L., Gao, F., Anderson, M., Kustas, W., Alsina, M., Sanchez, L., Sams, B., McKee, L., Dulaney, W., and White, W. (2017). Daily mapping of 30 m LAI and NDVI for Grape yield prediction in California Vineyards. Remote Sens., 9.","DOI":"10.3390\/rs9040317"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3023","DOI":"10.1080\/01431160110104692","article-title":"Large area operational wheat yield model development and validation based on spectral and meteorological data","volume":"23","author":"Manjunath","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"He, M., Kimball, J., Maneta, M., Maxwell, B., Moreno, A., Beguer\u00eda, S., and Wu, X. (2018). Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data. Remote Sens., 10.","DOI":"10.3390\/rs10030372"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2307\/2401901","article-title":"Solar radiation and productivity in tropical ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Prince, S.D., and Goward, S.N. (1995). Global primary production: A remote sensing approach. J. Biogeogr., 815\u2013835.","DOI":"10.2307\/2845983"},{"key":"ref_56","first-page":"657","article-title":"Relationship of spectral data to grain yield variation","volume":"46","author":"Tucker","year":"1980","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/321105.321114","article-title":"A technique for the numerical solution of certain integral equations of the first kind","volume":"9","author":"Phillips","year":"1962","journal-title":"J. ACM (JACM)"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tikhonov, A.N., Leonov, A.S., and Yagola, A.G. (1998). Nonlinear Ill-Posed Problems, Chapman & Hall.","DOI":"10.1007\/978-94-017-5167-4"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/isprsarchives-XL-7-W3-45-2015","article-title":"Regional scale crop mapping using multi-temporal satellite imagery","volume":"40","author":"Kussul","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1080\/22797254.2017.1324743","article-title":"Biophysical parameters mapping within the SPOT-5 Take 5 initiative","volume":"50","author":"Shelestov","year":"2017","journal-title":"Eur. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1768\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:10:26Z","timestamp":1760188226000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,27]]},"references-count":61,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11151768"],"URL":"https:\/\/doi.org\/10.3390\/rs11151768","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,27]]}}}