{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T17:34:21Z","timestamp":1772213661166,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T00:00:00Z","timestamp":1542067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring global agriculture systems relies on accurate and timely cropland information acquired worldwide. Recently, the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program has produced Global Food Security-support Analysis Data (GFSAD) cropland extent maps at three different spatial resolutions, i.e., GFSAD1km, GFSAD250m, and GFSAD30m. An accuracy assessment and comparison of these three GFSAD cropland extent maps was performed to establish their quality and reliability for monitoring croplands both at global and regional scales. Large area (i.e., global) assessment of GFSAD cropland extent maps was performed by dividing the entire world into regions using a stratification approach and collecting a reference dataset using a simple random sampling design. All three global cropland extent maps were assessed using a total reference dataset of 28,733 samples. The assessment results showed an overall accuracy of 72.3%, 80\u201398%, and 91.7% for GFSAD1km, 250 m (only for four continents), and 30 m maps, respectively. Additionally, a regional comparison of the three GFSAD cropland extent maps was analyzed for nine randomly selected study sites of different agriculture field sizes (i.e., small, medium, and large). The similarity among the three GFSAD cropland extent maps in these nine study sites was represented using a similarity matrix approach and two landscape metrics (i.e., Proportion of Landscape (PLAND) and Per Patch Unit (PPU)), which categorized the crop proportion and the crop pattern. A comparison of the results showed the similarities and differences in the cropland areas and their spatial extent when mapped at the three spatial resolutions and considering the different agriculture field sizes. Finally, specific recommendations were suggested for when to apply each of the three different GFSAD cropland extent maps for agriculture monitoring based on these agriculture field sizes.<\/jats:p>","DOI":"10.3390\/rs10111800","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T02:42:41Z","timestamp":1542163361000},"page":"1800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Accuracy Assessment of Global Food Security-Support Analysis Data (GFSAD) Cropland Extent Maps Produced at Three Different Spatial Resolutions"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7560-8884","authenticated-orcid":false,"given":"Kamini","family":"Yadav","sequence":"first","affiliation":[{"name":"Department of Natural Resources &amp; the Environment, University of New Hampshire, Durham, NH 03824, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3891-2163","authenticated-orcid":false,"given":"Russell G.","family":"Congalton","sequence":"additional","affiliation":[{"name":"Department of Natural Resources &amp; the Environment, University of New Hampshire, Durham, NH 03824, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,13]]},"reference":[{"key":"ref_1","unstructured":"Gong, P., Clinton, N., Yu, L., and Liang, L. (2013, January 25\u201327). The 30 m global land cover products from China: progress and perspectives. Proceedings of the International Symposium on Land Cover Mapping for the African Continent, Nairobi, Kenya."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, CRC Press-Taylor and Francis Group. [2015th ed.].","DOI":"10.1201\/b19322"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1111\/gcb.12838","article-title":"Mapping global cropland and field size","volume":"21","author":"Fritz","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.3390\/rs2071844","article-title":"Estimating global cropland extent with multi-year MODIS data","volume":"2","author":"Pittman","year":"2010","journal-title":"Remote Sens."},{"key":"ref_5","unstructured":"Teluguntla, P.G., Thenkabail, P.S., Xiong, J., Gumma, M.K., Giri, C., Milesi, C., Ozdogan, M., Congalton, R.G., Tilton, J.C., and Sankey, T.T. (2016). Global Food Security Support Analysis Data at Nominal 1 km (GFSAD1km) Derived from Remote Sensing in Support of Food Security in the Twenty-First Century: Current Achievements and Future Possibilities. Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, CRC Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1080\/17538947.2016.1267269","article-title":"Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000\u20132015) data","volume":"10","author":"Teluguntla","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2017.06.033","article-title":"MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types","volume":"198","author":"Massey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2018.10.013","article-title":"Integrating cloud-based workflows in continental-scale cropland extent classification","volume":"219","author":"Massey","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Congalton, R.G., Yadav, K., Dungan, J., Oliphant, A.J., and Poehnelt, J. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30-m Africa: Cropland Extent Product (GFSAD30AFCE)."},{"key":"ref_13","unstructured":"Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Congalton, R.G., Oliphant, A.J., Sankey, T., Poehnelt, J., Yadav, K., and Massey, R. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30-m for Australia, New Zealand, China, and Mongolia: Cropland Extent Product (GFSAD30AUNZCNMOCE)."},{"key":"ref_14","unstructured":"Oliphant, A.J., Thenkabail, P.S., Teluguntla, P., Xiong, J., Congalton, R.G., Yadav, K., Massey, R., Gumma, M.K., and Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30-m for Southeast & Northeast Asia: Cropland Extent Product (GFSAD30SEACE)."},{"key":"ref_15","unstructured":"Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Oliphant, A.J., Xiong, J., Congalton, R.G., Yadav, K., Phalke, A., and Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30-m for South Asia, Afghanistan and Iran: Cropland Extent Product (GFSAD30SAAFGIRCE)."},{"key":"ref_16","unstructured":"Phalke, A., Ozdogan, M., Thenkabail, P.S., Congalton, R.G., Yadav, K., Massey, R., Teluguntla, P., Poehnelt, J., and Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30-m for Europe, Middle-East, Russia and Central Asia: Cropland Extent Product (GFSAD30EUCEARUMECE)."},{"key":"ref_17","unstructured":"Massey, R., Sankey, T.T., Yadav, K., Congalton, R.G., Tilton, J.C., and Thenkabail, P.S. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30 m for North America: Cropland Extent Product (GFSAD30NACE)."},{"key":"ref_18","unstructured":"Zhong, Y., Giri, C., Thenkabail, P.S., Teluguntla, P., Congalton, R.G., Yadav, K., Oliphant, A.J., Xiong, J., Poehnelt, J., and Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30-m for South America: Cropland Extent Product (GFSAD30SACE)."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, J., Cao, X., Peng, S., and Ren, H. (2017). Analysis and Applications of GlobeLand30: A Review. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6080230"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Hoyos, A., Rembold, F., Kerdiles, H., and Gallego, J. (2017). Comparison of global land cover datasets for cropland monitoring. Remote Sens., 9.","DOI":"10.3390\/rs9111118"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.3390\/rs2092305","article-title":"Global croplands and their importance for water and food security in the twenty-first century: Towards an ever green revolution that combines a second green revolution with a blue revolution","volume":"2","author":"Thenkabail","year":"2010","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and interpreting measures of thematic classification accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0034-4257(98)00010-8","article-title":"Design and Analysis for Thematic Map Accuracy Assessment - an application of satellite imagery","volume":"64","author":"Stehman","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9781420048568"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc, M., Bontemps, S., Leroy, M., Achard, F., and Herold, M. (GLOBCOVER - Products Description and Validation Report, 2008). GLOBCOVER - Products Description and Validation Report."},{"key":"ref_28","unstructured":"Fritz, S., Havlik, P., Schneider, U., Schmid, E., and Obersteiner, M. (2009). Uncertainties in Global Land Cover Data and its Implications for Climate Change Mitigation Policies Assessment. Proc. Int. Symp. Remote Sens. Environ., 1\u20134."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1016\/j.rse.2010.05.003","article-title":"Assessing the accuracy of land cover change with imperfect ground reference data","volume":"114","author":"Foody","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1080\/17538947.2013.822574","article-title":"FROM-GC: 30 m global cropland extent derived through multisource data integration","volume":"6","author":"Yu","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.rse.2005.08.009","article-title":"Land cover assessment with MODIS imagery in southern African Miombo ecosystems","volume":"98","author":"Sedano","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Frey, K.E., and Smith, L.C. (2007). How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground-truth database for West Siberia. Glob. Biogeochem. Cycles, 21.","DOI":"10.1029\/2006GB002706"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1080\/01431160310001619607","article-title":"Remote sensing and land cover area estimation","volume":"25","author":"Gallego","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/JSTARS.2013.2250257","article-title":"Using Volunteered Data in Land Cover Map Validation: Mapping West African Forests","volume":"6","author":"Foody","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.09.013","article-title":"Automated annual cropland mapping using knowledge-based temporal features","volume":"110","author":"Waldner","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","unstructured":"(2010). FAO Global Forest Resources Assessment 2010, Food and Agriculture Organization (FAO). FAO Forestry Paper 163."},{"key":"ref_39","unstructured":"Strahler, A.H., Boschetti, L., Foody, G.M., Friedl, M.A., Hansen, M.C., Herold, M., Mayaux, P., Morisette, J.T., Stehman, S.V., and Woodcock, C.E. (2006). Global Land Cover Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps, European Communities."},{"key":"ref_40","first-page":"432","article-title":"Using know map category marginal frequencies to improve estimates of thematic map accuracy","volume":"48","author":"Card","year":"1982","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data, CRC Press.","DOI":"10.1201\/9781420055139"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yadav, K., and Congalton, R.G. (2018). Issues with large area thematic accuracy assessment for mapping cropland extent: A tale of three continents. Remote Sens., 10.","DOI":"10.3390\/rs10010053"},{"key":"ref_43","first-page":"397","article-title":"Accuracy assessment: a user\u2019s perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","first-page":"207","article-title":"Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale","volume":"13","author":"Roujean","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.apgeog.2014.07.002","article-title":"Land surface dynamics and environmental challenges of the Niger Delta, Africa: Remote sensing-based analyses spanning three decades (1986\u20132013)","volume":"53","author":"Kuenzer","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_46","first-page":"30","article-title":"Next generation of global land cover characterization, mapping, and monitoring","volume":"25","author":"Giri","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"10589","DOI":"10.3390\/rs70810589","article-title":"Validation of land cover maps in China using a sampling-based labeling approach","volume":"7","author":"Bai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2004.09.005","article-title":"A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets","volume":"94","author":"Giri","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"044005","DOI":"10.1088\/1748-9326\/6\/4\/044005","article-title":"Highlighting continued uncertainty in global land cover maps for the user community","volume":"6","author":"Fritz","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1002\/2013EO030006","article-title":"The Need for Improved Maps of Global Cropland. EOS Trans","volume":"94","author":"Fritz","year":"2013","journal-title":"Am. Geophys. Union"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bayas, J.C.L., See, L., Perger, C., Justice, C., Nakalembe, C., Dempewolf, J., and Fritz, S. (2017). Validation of automatically generated global and regional cropland data sets: The case of Tanzania. Remote Sens., 9.","DOI":"10.3390\/rs9080815"},{"key":"ref_52","unstructured":"Frohn, R. (1997). Remote Sensing for Landscape Ecology: New Metric Indicators for Monitoring, Modeling, and Assessment of Ecosystems, CRC Press-Taylor and Francis Group."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"12070","DOI":"10.3390\/rs61212070","article-title":"Global land cover mapping: A review and uncertainty analysis","volume":"6","author":"Congalton","year":"2014","journal-title":"Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1080\/01431160802698919","article-title":"Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium","volume":"30","author":"Thenkabail","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/rs5010019","article-title":"Harmonizing and combining existing land cover\/land use datasets for cropland area monitoring at the African continental scale","volume":"5","author":"Vancutsem","year":"2013","journal-title":"Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"15804","DOI":"10.3390\/rs71215804","article-title":"Spatial Accuracy Assessment and Integration of Global Land Cover Datasets","volume":"7","author":"Tsendbazar","year":"2015","journal-title":"Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Fisette, T., Rollin, P., Aly, Z., Campbell, L., Daneshfar, B., Filyer, P., Smith, A., Davidson, A., Shang, J., and Jarvis, I. (2013, January 12\u201316). AAFC annual crop inventory: Status and challenges. Proceedings of the 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Fairfax, VA, USA.","DOI":"10.1109\/Argo-Geoinformatics.2013.6621920"},{"key":"ref_59","unstructured":"Fischer, G., Nachtergaele, F.O., Prieler, S., Teixeira, E., Toth, G., van Velthuizen, H., Verelst, L., and Wiberg, D. (2012). Global Agro-ecological Zones (GAEZ): Model Documentation, International Institute for Applied Systems Analysis (IIASA)."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sun, P. (2018). Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps. Remote Sens., 10.","DOI":"10.3390\/rs10030487"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5768","DOI":"10.1080\/01431161.2012.674230","article-title":"A global land-cover validation data set, part I: fundamental design principles","volume":"33","author":"Olofsson","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/01431161.2010.541950","article-title":"Impact of sample size allocation when using stratified random sampling to estimate accuracy and area of land-cover change","volume":"3","author":"Stehman","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_63","unstructured":"Congalton, R.G., Yadav, K., McDonnell, K., Poehnelt, J., Stevens, B., Gumma, M.K., Teluguntla, P., and Thenkabail, P.S. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30-m: Cropland Extent Validation (GFSAD30VAL)."}],"updated-by":[{"DOI":"10.3390\/rs11060630","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T00:00:00Z","timestamp":1542067200000}}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1800\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T22:51:58Z","timestamp":1754261518000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,13]]},"references-count":63,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111800"],"URL":"https:\/\/doi.org\/10.3390\/rs10111800","relation":{"correction":[{"id-type":"doi","id":"10.3390\/rs11060630","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,13]]}}}