{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:19:41Z","timestamp":1774927181939,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003524","name":"Ministry of Business, Innovation and Employment","doi-asserted-by":"publisher","award":["SSIF"],"award-info":[{"award-number":["SSIF"]}],"id":[{"id":"10.13039\/501100003524","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding the spatial variation of soil pH is critical for many different stakeholders across different fields of science, because it is a master variable that plays a central role in many soil processes. This study documents the first attempt to map soil pH (1:5 H2O) at high resolution (100 m) in New Zealand. The regression framework used follows the paradigm of digital soil mapping, and a limited number of environmental covariates were selected using variable selection, before calibration of a quantile regression forest model. In order to adapt the outcomes of this work to a wide range of different depth supports, a new approach, which includes depth of sampling as a covariate, is proposed. It relies on data augmentation, a process where virtual observations are drawn from statistical populations constructed using the observed data, based on the top and bottom depth of sampling, and including the uncertainty surrounding the soil pH measurement. A single model can then be calibrated and deployed to estimate pH a various depths. Results showed that the data augmentation routine had a beneficial effect on prediction uncertainties, in particular when reference measurement uncertainties are taken into account. Further testing found that the optimal rate of augmentation for this dataset was 3-fold. Inspection of the final model revealed that the most important variables for predicting soil pH distribution in New Zealand were related to land cover and climate, in particular to soil water balance. The evaluation of this approach on those validation sites set aside before modelling showed very good results (R2=0.65, CCC=0.79, RMSE=0.54), that significantly out-performed existing soil pH information for the country.<\/jats:p>","DOI":"10.3390\/rs12182872","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T11:24:24Z","timestamp":1599218664000},"page":"2872","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7431-2603","authenticated-orcid":false,"given":"Pierre","family":"Roudier","sequence":"first","affiliation":[{"name":"Manaaki Whenua\u2014Landcare Research, Private Bag 11052, Manawat\u016b Mail Centre, Palmerston North 4442, New Zealand"},{"name":"Te P\u016bnaha Matatini, A New Zealand Centre of Research Excellence, Private Bag 92019, Auckland 1142, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7719-6695","authenticated-orcid":false,"given":"Olivia R.","family":"Burge","sequence":"additional","affiliation":[{"name":"Manaaki Whenua\u2014Landcare Research, P.O. Box 69040, Lincoln 7640, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4097-0381","authenticated-orcid":false,"given":"Sarah J.","family":"Richardson","sequence":"additional","affiliation":[{"name":"Manaaki Whenua\u2014Landcare Research, P.O. Box 69040, Lincoln 7640, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3060-1678","authenticated-orcid":false,"given":"James K.","family":"McCarthy","sequence":"additional","affiliation":[{"name":"Manaaki Whenua\u2014Landcare Research, P.O. Box 69040, Lincoln 7640, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1625-1253","authenticated-orcid":false,"given":"Gerard\u00a0J.","family":"Grealish","sequence":"additional","affiliation":[{"name":"Manaaki Whenua\u2014Landcare Research, Private Bag 11052, Manawat\u016b Mail Centre, Palmerston North 4442, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8923-0774","authenticated-orcid":false,"given":"Anne-Gaelle","family":"Ausseil","sequence":"additional","affiliation":[{"name":"Manaaki Whenua\u2014Landcare Research, P.O. Box 10, Wellington 6143, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sparks, D.L. (2003). Environmental Soil Chemistry, Elsevier.","DOI":"10.1016\/B978-012656446-4\/50001-3"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5794869","DOI":"10.1155\/2019\/5794869","article-title":"The role of soil pH in plant nutrition and soil remediation","volume":"2019","author":"Neina","year":"2019","journal-title":"Appl. Environ. Soil Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.scitotenv.2019.03.010","article-title":"Cadmium uptake by onions, lettuce and spinach in New Zealand: Implications for management to meet regulatory limits","volume":"668","author":"Cavanagh","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_4","unstructured":"Johnson, P., and Gerbeaux, P. (2004). Wetland Types in New Zealand, Department of Conservation."},{"key":"ref_5","unstructured":"Webb, T., and Wilson, A. (1995). A Manual of Land Characteristics for Evaluation of Rural Land, Manaaki Whenua Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1111\/j.1365-2427.2010.02412.x","article-title":"Applying systematic conservation planning principles to palustrine and inland saline wetlands of New Zealand","volume":"56","author":"Ausseil","year":"2011","journal-title":"Freshw. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.geoderma.2017.10.014","article-title":"Four Pillars of digital land resource mapping to address information and capacity shortages in developing countries","volume":"352","author":"Grealish","year":"2019","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.geodrs.2015.08.005","article-title":"Digital soil assessment of agricultural suitability, versatility and capital in Tasmania, Australia","volume":"6","author":"Kidd","year":"2015","journal-title":"Geoderma Reg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.2134\/jeq2002.1848","article-title":"Soil quality at a national scale in New Zealand","volume":"31","author":"Sparling","year":"2002","journal-title":"J. Environ. Qual."},{"key":"ref_10","unstructured":"Ministry for the Environment, and Statistics New Zealand (2018). New Zealand\u2019s Environmental Reporting Series: Our Land 2018."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.geoderma.2018.02.018","article-title":"A preliminary spatial quantification of the soil security dimensions for Tasmania","volume":"322","author":"Kidd","year":"2018","journal-title":"Geoderma"},{"key":"ref_12","unstructured":"Landcare Research (2020, July 15). New Zealand Fundamental Soil Layers, Soil pH. Available online: https:\/\/lris.scinfo.org.nz\/layer\/48102-fsl-ph\/."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hengl, T., de Jesus, J.M., Heuvelink, G.B., Gonzalez, M.R., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114237","DOI":"10.1016\/j.geoderma.2020.114237","article-title":"Model averaging for mapping topsoil organic carbon in France","volume":"366","author":"Chen","year":"2020","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.grj.2017.06.001","article-title":"Soil legacy data rescue via GlobalSoilMap and other international and national initiatives","volume":"14","author":"Arrouays","year":"2017","journal-title":"GeoResJ"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/S0016-7061(99)00003-8","article-title":"Modelling soil attribute depth functions with equal-area quadratic smoothing splines","volume":"91","author":"Bishop","year":"1999","journal-title":"Geoderma"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1016\/S2095-3119(17)61762-3","article-title":"Recent progress and future prospect of digital soil mapping: A review","volume":"16","author":"Zhang","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jenny, H. (1941). Factors of Soil Formation: A System of Quantitative Pedology, McGraw-Hill.","DOI":"10.1097\/00010694-194111000-00009"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1071\/SR05136","article-title":"Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley","volume":"44","author":"Minasny","year":"2006","journal-title":"Soil Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.geoderma.2011.07.014","article-title":"Peak functions for modeling high resolution soil profile data","volume":"166","author":"Myers","year":"2011","journal-title":"Geoderma"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.geoderma.2009.10.007","article-title":"Mapping continuous depth functions of soil carbon storage and available water capacity","volume":"154","author":"Malone","year":"2009","journal-title":"Geoderma"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.geoderma.2015.08.013","article-title":"A one-step approach for modelling and mapping soil properties based on profile data sampled over varying depth intervals","volume":"262","author":"Orton","year":"2016","journal-title":"Geoderma"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cageo.2018.05.008","article-title":"Sparse regression interaction models for spatial prediction of soil properties in 3D","volume":"118","author":"Heuvelink","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"186","DOI":"10.2136\/sssaj2017.04.0122","article-title":"Soil property and class maps of the conterminous United States at 100-meter spatial resolution","volume":"82","author":"Ramcharan","year":"2018","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.catena.2016.02.016","article-title":"Three-dimensional geostatistical modeling of soil organic carbon: A case study in the Qilian Mountains, China","volume":"141","author":"Brus","year":"2016","journal-title":"Catena"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.geoderma.2019.03.037","article-title":"Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data","volume":"347","author":"Nauman","year":"2019","journal-title":"Geoderma"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.geoderma.2015.07.017","article-title":"Digital soil mapping: A brief history and some lessons","volume":"264","author":"Minasny","year":"2016","journal-title":"Geoderma"},{"key":"ref_28","unstructured":"Manaaki Whenua\u2014Landcare Research (2020, July 15). National Soils Data Repository. Available online: https:\/\/doi.org\/10.26060\/95m4-cz25."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.geoderma.2013.08.019","article-title":"Converting pH 1:1 H2O and 1:2 CaCl2 to 1:5 H2O to contribute to a harmonized global soil database","volume":"213","author":"Libohova","year":"2014","journal-title":"Geoderma"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1007\/s10021-016-0084-x","article-title":"Nationally representative plot network reveals contrasting drivers of net biomass change in secondary and old-growth forests","volume":"20","author":"Holdaway","year":"2017","journal-title":"Ecosystems"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1111\/j.1541-0420.2011.01699.x","article-title":"Spatially balanced sampling through the pivotal method","volume":"68","author":"Schelin","year":"2012","journal-title":"Biometrics"},{"key":"ref_32","unstructured":"Grafstr\u00f6m, A., and Lisic, J. (2020, July 15). BalancedSampling: Balanced and Spatially Balanced Sampling, Available online: https:\/\/CRAN.R-project.org\/package=BalancedSampling."},{"key":"ref_33","first-page":"293","article-title":"Geomorphometry in SAGA","volume":"33","author":"Olaya","year":"2009","journal-title":"Dev. Soil Sci."},{"key":"ref_34","unstructured":"Neteler, M., and Mitasova, H. (2013). Open Source GIS: A GRASS GIS Approach, Springer Science  Business Media."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Running, S.W., Thornton, P.E., Nemani, R., and Glassy, J.M. (2000). Global terrestrial gross and net primary productivity from the earth observing system. Methods in Ecosystem Science, Springer.","DOI":"10.1007\/978-1-4612-1224-9_4"},{"key":"ref_36","unstructured":"Leathwick, J., Morgan, F., Wilson, G., Rutledge, D., McLeod, M., and Johnston, K. (2002). Land Environments of New Zealand: A Technical Guide, Manaaki Whenua Press."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1002\/joc.1350","article-title":"Thin plate smoothing spline interpolation of daily rainfall for New Zealand using a climatological rainfall surface","volume":"26","author":"Tait","year":"2006","journal-title":"Int. J. Climatol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/00288233.1994.9513047","article-title":"Calibration and performance of the single-layer soil water balance model for pasture sites","volume":"37","author":"Porteous","year":"1994","journal-title":"N. Z. J. Agric. Res."},{"key":"ref_39","unstructured":"Hofierka, J., and Suri, M. (2002, January 11\u201313). The solar radiation model for Open source GIS: Implementation and applications. Proceedings of the Open Source GIS-GRASS Users Conference, Trento, Italy."},{"key":"ref_40","unstructured":"Landcare Research (2020, July 15). LCDB v4.1\u2014Landcover Database Version 4.1. Available online: https:\/\/lris.scinfo.org.nz\/layer\/48423-lcdb-v41-land-cover-database-version-41-mainland-new-zealand\/."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1080\/0028825X.2001.9512748","article-title":"New Zealand\u2019s potential forest pattern as predicted from current species-environment relationships","volume":"39","author":"Leathwick","year":"2001","journal-title":"N. Z. J. Bot."},{"key":"ref_42","unstructured":"Landcare Research (2020, July 15). NZDEM\u2014New Zealand Digital Elevation Model. Available online: https:\/\/lris.scinfo.org.nz\/layer\/48131-nzdem-north-island-25-metre\/."},{"key":"ref_43","unstructured":"(2020, July 15). Available online: https:\/\/lris.scinfo.org.nz\/layer\/48127-nzdem-south-island-25-metre\/."},{"key":"ref_44","unstructured":"Landcare Research (2020, July 15). New Zealand Land Resource Inventory. Available online: https:\/\/lris.scinfo.org.nz\/layer\/48065-nzlri-rock\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"102870","DOI":"10.1016\/j.earscirev.2019.05.014","article-title":"Digital mapping of peatlands\u2014A critical review","volume":"196","author":"Minasny","year":"2019","journal-title":"Earth Sci. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1080\/01621459.1987.10478458","article-title":"The calculation of posterior distributions by data augmentation","volume":"82","author":"Tanner","year":"1987","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., and Le Quoc, V. (2019, January 15). AutoAugment: Learning augmentation strategies from data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_50","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_51","first-page":"983","article-title":"Quantile regression forests","volume":"7","author":"Meinshausen","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.geoderma.2016.12.017","article-title":"Using quantile regression forest to estimate uncertainty of digital soil mapping products","volume":"291","author":"Vaysse","year":"2017","journal-title":"Geoderma"},{"key":"ref_53","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.envsoft.2014.03.004","article-title":"Holistic environmental soil-landscape modeling of soil organic carbon","volume":"57","author":"Xiong","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"108815","DOI":"10.1016\/j.ecolmodel.2019.108815","article-title":"Importance of spatial predictor variable selection in machine learning applications\u2013Moving from data reproduction to spatial prediction","volume":"411","author":"Meyer","year":"2019","journal-title":"Ecol. Model."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_57","unstructured":"Genuer, R., Poggi, J.M., and Tuleau-Malot, C. (2020, July 15). VSURF: Variable Selection Using Random Forests; R Package Version 1.1.0; 2019. Available online: https:\/\/CRAN.R-project.org\/package=VSURF\/."},{"key":"ref_58","unstructured":"Meyer, H. (2020, July 15). CAST: \u2019caret\u2019 Applications for Spatial-Temporal Models; R Package Version 0.3.2; 2019. Available online: https:\/\/CRAN.R-project.org\/package=CAST\/."},{"key":"ref_59","unstructured":"New Zealand Soil Bureau (1968). Soils of New Zealand, Part 2 (Soil Bureau Bulletin 26 (2))."},{"key":"ref_60","unstructured":"Molloy, L. (1988). Soils in the New Zealand Landscape: The Living Mantle, Mallinson Rendel Publishers Ltd.. [2nd ed.]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.cageo.2012.10.020","article-title":"Algorithms for quantitative pedology: A toolkit for soil scientists","volume":"52","author":"Beaudette","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1002\/ldr.1116","article-title":"Land use\/land cover change analysis and its impact on soil properties in the northern part of Gadarif region, Sudan","volume":"24","author":"Biro","year":"2013","journal-title":"Land Degrad. Dev."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1038\/nature20139","article-title":"Water balance creates a threshold in soil pH at the global scale","volume":"540","author":"Slessarev","year":"2016","journal-title":"Nature"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1111\/ejss.12909","article-title":"A note on knowledge discovery and machine learning in digital soil mapping","volume":"71","author":"Wadoux","year":"2020","journal-title":"Eur. J. Soil Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.2307\/3071796","article-title":"Local plant diversity patterns and evolutionary history at the regional scale","volume":"83","year":"2002","journal-title":"Ecology"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1080\/0028825X.1987.10410175","article-title":"The distribution of plants in relation to pH and salinity on inland saline\/alkaline soils in Central Otago, New Zealand","volume":"35","author":"Allen","year":"1997","journal-title":"N. Z. J. Bot."},{"key":"ref_67","first-page":"119","article-title":"New Zealand\u2019s historically rare terrestrial ecosystems set in a physical and physiognomic framework","volume":"31","author":"Williams","year":"2007","journal-title":"N. Z. J. Ecol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1071\/SR14366","article-title":"The Australian three-dimensional soil grid: Australia\u2019s contribution to the GlobalSoilMap project","volume":"53","author":"Chen","year":"2015","journal-title":"Soil Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/soil-4-1-2018","article-title":"Evaluation of digital soil mapping approaches with large sets of environmental covariates","volume":"4","author":"Nussbaum","year":"2018","journal-title":"Soil"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2872\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:50Z","timestamp":1760177210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,4]]},"references-count":69,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182872"],"URL":"https:\/\/doi.org\/10.3390\/rs12182872","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,4]]}}}