{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:45:50Z","timestamp":1762177550221,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agricultural Greenhouse Gases Program-Agriculture and Agri-Food Canada","award":["AGGP-015"],"award-info":[{"award-number":["AGGP-015"]}]},{"name":"the Canada First Research Excellence Fund","award":["AGGP-015"],"award-info":[{"award-number":["AGGP-015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO2 sequestration. Publicly available, large field-scale georeferenced datasets are often limited in number and design to serve these purposes. This study provides the first publicly accessible dataset of georeferenced topsoil SOC measurements (n = 840) over a 26-hectare (ha) agricultural field located in southern Ontario, Canada, with a sampling density of ~32 points per ha. As SOC is usually influenced by site topography (i.e., slope and landscape position), each point of the database is associated with a wide range of remote sensing topographic derivatives; as well as with normalized difference vegetation index (NDVI) based value. The NDVI data were extracted from remote sensing Sentinel-2 imagery from over a five-year period (2017\u20132021). In this paper, the methodology for topsoil sampling, SOC measurement in the lab, as well as producing the suite of topographic derivatives is described. We discuss the opportunities that the database offers in terms of spatially explicit and continuous soil information to support international efforts in digital soil mapping (i.e., SoilGrids250m) as well as other potential applications detailed in the discussion section. We believe that the database with very dense point location measurements can help in conducting carbon stocks and sequestration studies. Such information can be used to help bridge the gap between ground data and remotely sensed datasets or data-derived products from modeling approaches intended to evaluate field-scale rates of agricultural carbon accumulation. The generated topsoil database in this study is archived and publicly available on the Zenodo open-access repository.<\/jats:p>","DOI":"10.3390\/rs14215519","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T03:53:07Z","timestamp":1667447587000},"page":"5519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-5025","authenticated-orcid":false,"given":"Ahmed","family":"Laamrani","sequence":"first","affiliation":[{"name":"Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir 43150, Morocco"},{"name":"Department of Geography, Environment & Geomatics, University of Guelph, Guelph, ON 1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul R.","family":"Voroney","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1971-1238","authenticated-orcid":false,"given":"Daniel D.","family":"Saurette","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada"},{"name":"Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), Guelph, ON N1G 4Y2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8438-5662","authenticated-orcid":false,"given":"Aaron A.","family":"Berg","sequence":"additional","affiliation":[{"name":"Department of Geography, Environment & Geomatics, University of Guelph, Guelph, ON 1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Line","family":"Blackburn","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam W.","family":"Gillespie","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralph C.","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Plant Agriculture, University of Guelph, Guelph, ON 1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.ecolind.2019.04.027","article-title":"How does soil organic carbon mediate trade-offs between ecosystem services and agricultural production?","volume":"103","author":"Villarino","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1126\/science.1097396","article-title":"Soil carbon sequestration impacts on global climate change and food security","volume":"304","author":"Lal","year":"2004","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Laamrani, A., Voroney, P.R., Gillespie, A.W., and Chehbouni, A. (2021). Development of a Land Use Carbon Inventory for Agricultural Soils in the Canadian Province of Ontario. Land, 10.","DOI":"10.3390\/land10070765"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tziolas, N., Tsakiridis, N., Chabrillat, S., Dematt\u00ea, J.A.M., Ben-Dor, E., Gholizadeh, A., Zalidis, G., and van Wesemael, B. (2021). Earth Observation Data-Driven Cropland Soil Monitoring: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13214439"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1038\/s41467-022-31540-9","article-title":"Global stocks and capacity of mineral-associated soil organic carbon","volume":"13","author":"Georgiou","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115567","DOI":"10.1016\/j.geoderma.2021.115567","article-title":"Digital Mapping of GlobalSoilMap Soil Properties at a Broad Scale: A Review","volume":"409","author":"Chen","year":"2022","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Laamrani, A., Voroney, P.R., Berg, A.A., Gillespie, A.W., March, M., Deen, B., and Martin, R.C. (2020). Temporal Change of Soil Carbon on a Long-Term Experimental Site with Variable Crop Rotations and Tillage Systems. Agronomy, 10.","DOI":"10.3390\/agronomy10060840"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lal, R. (2021). Raising Soil Organic Matter to Improve Productivity and Nutritional Quality of Food Crops in India. Soil Organic Matter and Feeding the Future: Environmental and Agronomic Impacts, CRC Press. [1st ed.].","DOI":"10.1201\/9781003102762"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.geoderma.2011.01.010","article-title":"Three-dimensional mapping of soil organic matter content using soil type\u2013specific depth functions","volume":"162","author":"Kempen","year":"2011","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"McBratney, A.B., Stockmann, U., Angers, D.A., Minasny, B., and Field, D.J. (2014). Challenges for Soil Organic Carbon Research. Soil Carbon, Springer.","DOI":"10.1007\/978-3-319-04084-4_1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fathizad, H., Taghizadeh-Mehrjardi, R., Hakimzadeh Ardakani, M.A., Zeraatpisheh, M., Heung, B., and Scholten, T. (2022). Spatiotemporal Assessment of Soil Organic Carbon Change Using Machine-Learning in Arid Regions. Agronomy, 12.","DOI":"10.3390\/agronomy12030628"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.ecolind.2013.08.009","article-title":"Estimation of Soil Organic Matter by Geostatistical Methods: Use of Auxiliary Information in Agriculturaland Environmental Assessment","volume":"36","author":"Piccini","year":"2014","journal-title":"Ecol. Ind."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., and Bochtis, D. (2019). Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11060676"},{"key":"ref_15","unstructured":"Laamrani, A., Voroney, P., Saurette, D.D., Berg, A., Blackburn, L., Gillespie, A., and Martin, R.C. (2022). Large dataset of soil organic carbon and topographic derivatives [Data set]. Zenodo."},{"key":"ref_16","unstructured":"Statistics Canada (2022, August 22). Census of Agriculture, Available online: https:\/\/www150.statcan.gc.ca\/n1\/daily-quotidien\/170510\/dq170510a-eng.htm."},{"key":"ref_17","unstructured":"Hoffman, D.W., Matthews, B.C., and Wicklund, R.E. (2022, August 22). Soil Associations of Southern Ontario, Available online: https:\/\/sis.agr.gc.ca\/cansis\/publications\/surveys\/on\/on30\/on30_report.pdf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"180214","DOI":"10.1038\/sdata.2018.214","article-title":"Present and future k\u00f6ppen-geiger climate classification maps at 1-km resolution","volume":"5","author":"Beck","year":"2018","journal-title":"Sci. Data"},{"key":"ref_19","unstructured":"Environment Canada (2022, August 22). Canadian Climate Normals 1981\u20132010: Fergus Shand Dam Weather Station, Available online: http:\/\/climate.weather.gc.ca\/climate_normals\/index_e.html."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Laamrani, A., Berg, A.A., Voroney, P., Feilhauer, H., Blackburn, L., March, M., Dao, P.D., He, Y., and Martin, R.C. (2019). Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada. Remote Sens., 11.","DOI":"10.3390\/rs11111298"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/00103629809369925","article-title":"Direct measurement of organic carbon content in soils by the Leco CR-12 carbon analyzer","volume":"29","author":"Wang","year":"1998","journal-title":"Commun. Soil Sci Plan."},{"key":"ref_22","unstructured":"Ontario Ministry of Agriculture, Food and Rural Affairs (2022, August 22). Ontario Classified Point Cloud (Lidar-Derived), Available online: https:\/\/geohub.lio.gov.on.ca\/datasets\/mnrf::ontario-classified-point-cloud-lidar-derived."},{"key":"ref_23","unstructured":"Ontario Ministry of Natural Resources and Forestry (2022, August 22). Ontario Elevation Mapping Program, Available online: https:\/\/geohub.lio.gov.on.ca\/pages\/ontario-elevation-mapping-program."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116054","DOI":"10.1016\/j.geoderma.2022.116054","article-title":"Effects of sample size and covariate resolution on field-scale predictive digital mapping of soil carbon","volume":"425","author":"Saurette","year":"2022","journal-title":"Geoderma"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.cageo.2016.07.003","article-title":"Whitebox GAT: A case study in geomorphometric analysis","volume":"95","author":"Lindsay","year":"2016","journal-title":"Comput. Geosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.geomorph.2018.04.003","article-title":"Evaluating metrics of local topographic position for multiscale geomorphometric analysis","volume":"312","author":"Newman","year":"2018","journal-title":"Geomorphology"},{"key":"ref_27","unstructured":"Wu, Q. (2022, August 22). Whitebox: \u201cWhiteboxTools\u201d R Frontend. Available online: https:\/\/R-Forge.R-project.org\/projects\/whitebox."},{"key":"ref_28","unstructured":"Team, R. (2013). Core. R: A Language and Environment for Statistical Computing, R foundation for Statistical Computing."},{"key":"ref_29","unstructured":"Lindsay, J. (2022, August 22). WhiteboxTools User Manual. User Manual, University of Guelph. Available online: https:\/\/www.uoguelph.ca\/~hydrogeo\/WhiteboxTools\/index.html."},{"key":"ref_30","first-page":"2271","article-title":"System for Automated Geoscientific Analyses (SAGA) v. 2.1.4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_31","unstructured":"Brenning, A., Bangs, D., and Becker, M. (2022, August 22). RSAGA: SAGA geoprocessing and terrain analysis. R package version 1.3.0. Available online: https:\/\/CRAN.R-project.org\/package=RSAGA."},{"key":"ref_32","unstructured":"(2022, August 22). Esri\u2014ArcGIS Pro 2.9. How Kriging works?. Available online: https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/spatial-analyst\/how-kriging-works.htm#GUID-08AA4C59-A05E-4F9F-A18D-E30B36C7523A."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02693799008941549","article-title":"Kriging: A Method of Interpolation for Geographical Information Systems","volume":"4","author":"Oliver","year":"1990","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Eyre, R., Lindsay, J., Laamrani, A., and Berg, A. (2021). Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models. Remote Sens., 13.","DOI":"10.3390\/rs13204152"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10643389.2020.1811590","article-title":"Impact of Agricultural Management Practices on Soil Carbon Sequestration and Its Monitoring through Simulation Models and Remote Sensing Techniques: A Review","volume":"52","author":"Mandal","year":"2020","journal-title":"Crit. Rev. Environ. Sci. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Laamrani, A., Joosse, P., McNairn, H., Berg, A.A., Hagerman, J., Powell, K., and Berry, M. (2020). Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques. Remote Sens., 12.","DOI":"10.3390\/rs12091397"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mzid, N., Castaldi, F., Tolomio, M., Pascucci, S., Casa, R., and Pignatti, S. (2022). Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data. Remote Sens., 14.","DOI":"10.3390\/rs14030714"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.2136\/sssaj2016.11.0376","article-title":"More data or a better model? Figuring out what matters most for the spatial prediction of soil carbon","volume":"81","author":"Somarathna","year":"2017","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.apm.2019.12.016","article-title":"Selecting appropriate machine learning methods for digital soil mapping","volume":"81","author":"Khaledian","year":"2019","journal-title":"Appl. Math. Model."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vaudour, E., Gholizadeh, A., Castaldi, F., Saberioon, M., Bor\u016fvka, L., Urbina-Salazar, D., Fouad, Y., Arrouays, D., Richer-de-Forges, A.C., and Biney, J. (2022). Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview. Remote Sens., 14.","DOI":"10.3390\/rs14122917"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., and Gong, P. (2021). Progress and Trends in the Application of Google Earth and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13183778"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.1080\/01431161.2011.636081","article-title":"Google Earth as a virtual globe tool for Earth science applications at the global scale: Progress and perspectives","volume":"33","author":"Yu","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","unstructured":"Freden, S.C., Mercanti, E.P., and Becker, M. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite\u20131 Syposium, Technical Presentations, NASA SP-351."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5519\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:09:22Z","timestamp":1760144962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,2]]},"references-count":44,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215519"],"URL":"https:\/\/doi.org\/10.3390\/rs14215519","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,2]]}}}