{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:36Z","timestamp":1773354096248,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"North Dakota Department of Agriculture"},{"name":"Colorado Office of Economic Development and International Trade"},{"name":"Perennial Climate Incorporated in Boulder, Colorado"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Increases in organic carbon within agricultural soils are widely recognized as a \u201cnegative emission\u201d that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0\u201330 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0\u201330 cm SOC content based on soil samples from 0\u20135 (surface), 5\u201330 (subsurface), and 0\u201330 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020\u20132022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010\u20132011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0\u201330 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of \u22120.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0\u201330 cm SOC content in agricultural soils over the continental United States (CONUS).<\/jats:p>","DOI":"10.3390\/rs16122217","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T04:21:28Z","timestamp":1718770888000},"page":"2217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Accurate Quantification of 0\u201330 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Peng","family":"Fu","sequence":"first","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"},{"name":"Center for Advanced Agriculture and Sustainability, Harrisburg University, Harrisburg, PA 17101, USA"}]},{"given":"Christian","family":"Clanton","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"given":"Kirk M.","family":"Demuth","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"given":"Verena","family":"Goodman","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"given":"Lauren","family":"Griffith","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"given":"Mage","family":"Khim-Young","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"given":"Julia","family":"Maddalena","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"given":"Kenny","family":"LaMarca","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"given":"Logan A.","family":"Wright","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2476-3494","authenticated-orcid":false,"given":"David W.","family":"Schurman","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"}]},{"given":"James R.","family":"Kellner","sequence":"additional","affiliation":[{"name":"Perennial Climate Inc., Boulder, CO 80301, USA"},{"name":"Institute at Brown for Environment and Society, Brown University, Providence, RI 02912, USA"},{"name":"Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, RI 02912, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e02290","DOI":"10.1002\/eap.2290","article-title":"Soil Organic Carbon Is Not Just for Soil Scientists: Measurement Recommendations for Diverse Practitioners","volume":"31","author":"Billings","year":"2021","journal-title":"Ecol. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1098\/rstb.2007.2185","article-title":"Carbon Sequestration","volume":"363","author":"Lal","year":"2008","journal-title":"Phil. Trans. R. Soc. B"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1002\/ldr.3270","article-title":"Soil Organic Carbon Stock as an Indicator for Monitoring Land and Soil Degradation in Relation to U Nited N Ations\u2019 S Ustainable D Evelopment G Oals","volume":"30","author":"Lorenz","year":"2019","journal-title":"Land Degrad Dev"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5427","DOI":"10.1038\/s41467-020-18887-7","article-title":"Towards a Global-Scale Soil Climate Mitigation Strategy","volume":"11","author":"Amelung","year":"2020","journal-title":"Nat Commun"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2017.01.002","article-title":"Soil Carbon 4 per Mille","volume":"292","author":"Minasny","year":"2017","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/d41586-018-07587-4","article-title":"Put More Carbon in Soils to Meet Paris Climate Pledges","volume":"564","author":"Rumpel","year":"2018","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/s13593-022-00835-y","article-title":"Framing the Future of the Koronivia Joint Work on Agriculture from Science-Based Evidence. A Review","volume":"42","author":"Ramifehiarivo","year":"2022","journal-title":"Agron. Sustain. Dev."},{"key":"ref_8","unstructured":"Rietra, R., Lesschen, J., and Porre, R. (2021). Recarbonizing Global Soils: A Technical Manual of Recommended Management Practices: Volume 3-Cropland, Grassland, Integrated Systems and Farming Approaches-Practices Overview, FAO."},{"key":"ref_9","unstructured":"Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, IPCC."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1111\/gcb.14815","article-title":"How to Measure, Report and Verify Soil Carbon Change to Realize the Potential of Soil Carbon Sequestration for Atmospheric Greenhouse Gas Removal","volume":"26","author":"Smith","year":"2020","journal-title":"Glob. Change Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/B978-0-12-405942-9.00001-3","article-title":"Digital Mapping of Soil Carbon","volume":"Volume 118","author":"Minasny","year":"2013","journal-title":"Advances in Agronomy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0016-7061(03)00223-4","article-title":"On Digital Soil Mapping","volume":"117","author":"McBratney","year":"2003","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1134\/S1064229312040047","article-title":"The Dokuchaev Hypothesis as a Basis for Predictive Digital Soil Mapping (on the 125th Anniversary of Its Publication)","volume":"45","author":"Florinsky","year":"2012","journal-title":"Eurasian Soil Sc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.geoderma.2013.07.002","article-title":"High Resolution 3D Mapping of Soil Organic Carbon in a Heterogeneous Agricultural Landscape","volume":"213","author":"Lacoste","year":"2014","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.geoderma.2008.05.008","article-title":"Soil Organic Carbon Concentrations and Stocks on Barro Colorado Island\u2014Digital Soil Mapping Using Random Forests Analysis","volume":"146","author":"Grimm","year":"2008","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.geoderma.2015.08.034","article-title":"Mapping Soil Carbon Stocks across Scotland Using a Neural Network Model","volume":"262","author":"Aitkenhead","year":"2016","journal-title":"Geoderma"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes de Jesus, J., Heuvelink, G.B.M., Ruiperez Gonzalez, M., 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_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5194\/soil-5-79-2019","article-title":"Using Deep Learning for Digital Soil Mapping","volume":"5","author":"Padarian","year":"2019","journal-title":"SOIL"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/B978-0-12-800137-0.00003-0","article-title":"GlobalSoilMap","volume":"Volume 125","author":"Arrouays","year":"2014","journal-title":"Advances in Agronomy"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.scitotenv.2014.02.061","article-title":"Landscape Scale Estimation of Soil Carbon Stock Using 3D Modelling","volume":"487","author":"Veronesi","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.geoderma.2014.05.004","article-title":"National Scale 3D Modelling of Soil Organic Carbon Stocks with Uncertainty Propagation\u2014An Example from Scotland","volume":"232\u2013234","author":"Poggio","year":"2014","journal-title":"Geoderma"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115402","DOI":"10.1016\/j.geoderma.2021.115402","article-title":"Large Scale Mapping of Soil Organic Carbon Concentration with 3D Machine Learning and Satellite Observations","volume":"405","author":"Sothe","year":"2022","journal-title":"Geoderma"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.geoderma.2015.05.013","article-title":"A Similarity-Based Method for Three-Dimensional Prediction of Soil Organic Matter Concentration","volume":"263","author":"Liu","year":"2016","journal-title":"Geoderma"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"e20062","DOI":"10.1002\/vzj2.20062","article-title":"3D Mapping of Soil Organic Carbon Content and Soil Moisture with Multiple Geophysical Sensors and Machine Learning","volume":"19","author":"Rentschler","year":"2020","journal-title":"Vadose Zone J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G.B., and Walsh, M.G. (2014). SoilGrids1km\u2014Global Soil Information Based on Automated Mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105992"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"114094","DOI":"10.1016\/j.geoderma.2019.114094","article-title":"Increment-Averaged Kriging for 3-D Modelling and Mapping Soil Properties: Combining Machine Learning and Geostatistical Methods","volume":"361","author":"Orton","year":"2020","journal-title":"Geoderma"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"103359","DOI":"10.1016\/j.earscirev.2020.103359","article-title":"Machine Learning for Digital Soil Mapping: Applications, Challenges and Suggested Solutions","volume":"210","author":"Wadoux","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1111\/ejss.12916","article-title":"Mapping Soil Profile Depth, Bulk Density and Carbon Stock in Scotland Using Remote Sensing and Spatial Covariates","volume":"71","author":"Aitkenhead","year":"2020","journal-title":"Eur. J. Soil Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"114794","DOI":"10.1016\/j.geoderma.2020.114794","article-title":"Predicting Soil Properties in 3D: Should Depth Be a Covariate?","volume":"383","author":"Ma","year":"2021","journal-title":"Geoderma"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Hartemink, A.E., and Minasny, B. (2016). Measuring and Modelling Soil Depth Functions. Digital Soil Morphometrics, Springer International Publishing. Progress in Soil Science.","DOI":"10.1007\/978-3-319-28295-4"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.still.2015.05.014","article-title":"High Resolution Characterization of the Soil Organic Carbon Depth Profile in a Soil Landscape Affected by Erosion","volume":"156","author":"Sommer","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"614","DOI":"10.2136\/sssaj2007.0410","article-title":"Predicting Soil Organic Carbon Stock Using Profile Depth Distribution Functions and Ordinary Kriging","volume":"73","author":"Mishra","year":"2009","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"McBratney, A.B., Minasny, B., and Stockmann, U. (2018). Digital Mapping of Soil Classes and Continuous Soil Properties. Pedometrics, Springer International Publishing. Progress in Soil Science.","DOI":"10.1007\/978-3-319-63439-5"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, C., Hu, K., Li, H., Yun, A., and Li, B. (2015). Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0129038"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1038\/s41893-019-0431-y","article-title":"Soil Carbon Science for Policy and Practice","volume":"2","author":"Bradford","year":"2019","journal-title":"Nat. Sustain."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/climate.2007.58","article-title":"Policing the Voluntary Carbon Market","volume":"1","author":"Gillenwater","year":"2007","journal-title":"Nat. Clim. Change"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"965","DOI":"10.2136\/sssaj1988.03615995005200040012x","article-title":"Determination of Organic and Carbonate Carbon in Calcareous Soils Using Dry Combustion","volume":"52","author":"Rabenhorst","year":"1988","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_43","unstructured":"Hartemink, A.E., and McSweeney, K. (2014). Overview of the U.S. Rapid Carbon Assessment Project: Sampling Design, Initial Summary and Uncertainty Estimates. Soil Carbon, Springer International Publishing."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"055002","DOI":"10.1088\/1748-9326\/aabe1c","article-title":"A Global Map of Mangrove Forest Soil Carbon at 30 m Spatial Resolution","volume":"13","author":"Sanderman","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1002\/joc.5086","article-title":"WorldClim 2: New 1-km Spatial Resolution Climate Surfaces for Global Land Areas","volume":"37","author":"Fick","year":"2017","journal-title":"Int. J. Clim."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1175\/2010BAMS3001.1","article-title":"The NCEP Climate Forecast System Reanalysis","volume":"91","author":"Saha","year":"2010","journal-title":"Bull. Amer. Meteor. Soc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1175\/JCLI-D-12-00823.1","article-title":"The NCEP Climate Forecast System Version 2","volume":"27","author":"Saha","year":"2014","journal-title":"J. Clim."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Stoker, J., and Miller, B. (2022). The Accuracy and Consistency of 3D Elevation Program Data: A Systematic Analysis. Remote Sens., 14.","DOI":"10.3390\/rs14040940"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2019). Sentinel-1 GRD Preprocessing Workflow. Proceedings, 18.","DOI":"10.3390\/ECRS-3-06201"},{"key":"ref_51","unstructured":"Bruzzone, L., Bovolo, F., and Benediktsson, J.A. (2017). Sen2Cor for Sentinel-2. Proceedings of the Image and Signal Processing for Remote Sensing XXIII, SPIE."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112990","DOI":"10.1016\/j.rse.2022.112990","article-title":"Cloud Mask Intercomparison eXercise (CMIX): An Evaluation of Cloud Masking Algorithms for Landsat 8 and Sentinel-2","volume":"274","author":"Skakun","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New Refinements and Validation of the Collection-6 MODIS Land-Surface Temperature\/Emissivity Product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"145292","DOI":"10.1016\/j.scitotenv.2021.145292","article-title":"Patterns and Driving Factors of Biomass Carbon and Soil Organic Carbon Stock in the Indian Himalayan Region","volume":"770","author":"Ahirwal","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ecolind.2014.12.028","article-title":"A Comparative Assessment of Support Vector Regression, Artificial Neural Networks, and Random Forests for Predicting and Mapping Soil Organic Carbon Stocks across an Afromontane Landscape","volume":"52","author":"Were","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_57","unstructured":"Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter, F. (2017, January 20\u201322). Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. Proceedings of the Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/2.485891","article-title":"Artificial Neural Networks: A Tutorial","volume":"29","author":"Jain","year":"1996","journal-title":"Computer"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1080\/014311698214433","article-title":"Review Article: Attributes of Neural Networks for Extracting Continuous Vegetation Variables from Optical and Radar Measurements","volume":"19","author":"Kimes","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","first-page":"297","article-title":"Generalized Additive Models","volume":"1","author":"Hastie","year":"1986","journal-title":"Stat. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1198\/jasa.2009.ap07058","article-title":"Modeling Spatiotemporal Forest Health Monitoring Data","volume":"104","author":"Augustin","year":"2009","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"377","DOI":"10.5194\/soil-7-377-2021","article-title":"Predicting the Spatial Distribution of Soil Organic Carbon Stock in Swedish Forests Using a Group of Covariates and Site-Specific Data","volume":"7","author":"Hounkpatin","year":"2021","journal-title":"SOIL"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1071\/SR13081","article-title":"Estimating Change in Soil Organic Carbon Using Legacy Data as the Baseline: Issues, Approaches and Lessons to Learn","volume":"52","author":"Karunaratne","year":"2014","journal-title":"Soil Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"115874","DOI":"10.1016\/j.geoderma.2022.115874","article-title":"Accessing and Assessing Legacy Soil Information, an Example from Two Provinces of Zambia","volume":"420","author":"Mukumbuta","year":"2022","journal-title":"Geoderma"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Odeh, I.O., Leenaars, J., Hartemink, A., and Amapu, I. (2012). The Challenges of Collating Legacy Data for Digital Mapping of Nigerian Soils. Digit Soil Assess. Beyond, 453\u2013458.","DOI":"10.1201\/b12728-88"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.catena.2018.12.015","article-title":"A Simple Pipeline for the Assessment of Legacy Soil Datasets: An Example and Test with Soil Organic Carbon from a Highly Variable Area","volume":"175","author":"Schillaci","year":"2019","journal-title":"Catena"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"114819","DOI":"10.1016\/j.geoderma.2020.114819","article-title":"Harmonization of a Large-Scale National Soil Database with the World Reference Base for Soil Resources 2014","volume":"384","year":"2021","journal-title":"Geoderma"},{"key":"ref_69","first-page":"43","article-title":"Selecting Representative Data Sets","volume":"12","author":"Borovicka","year":"2012","journal-title":"Adv. Data Min. Knowl. Discov. Appl."},{"key":"ref_70","unstructured":"Lodder, P. (2013). To Impute or Not Impute: That\u2019s the Question. Advising on Research Methods: Selected Topics 2013, Johannes van Kessel Publishing."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"530","DOI":"10.2111\/05-201R.1","article-title":"Remote Sensing for Grassland Management in the Arid Southwest","volume":"59","author":"Marsett","year":"2006","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Diek, S., Fornallaz, F., and Schaepman, M.E. (2017). Rogier De Jong Barest Pixel Composite for Agricultural Areas Using Landsat Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9121245"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2018.04.047","article-title":"Geospatial Soil Sensing System (GEOS3): A Powerful Data Mining Procedure to Retrieve Soil Spectral Reflectance from Satellite Images","volume":"212","author":"Fongaro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_76","first-page":"87","article-title":"Using Thematic Mapper Data to Identify Contrasting Soil Plains and Tillage Practices","volume":"63","author":"Ward","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.iswcr.2020.10.001","article-title":"The Use of Remote Sensing to Detect the Consequences of Erosion in Gypsiferous Soils","volume":"8","author":"Marques","year":"2020","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3987","DOI":"10.1080\/01431160802575653","article-title":"Land Surface Water Index (LSWI) Response to Rainfall and NDVI Using the MODIS Vegetation Index Product","volume":"31","author":"Chandrasekar","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"4038","DOI":"10.1109\/JSTARS.2019.2938388","article-title":"Derivation of Tasseled Cap Transformation Coefficients for Sentinel-2 MSI At-Sensor Reflectance Data","volume":"12","author":"Shi","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. 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