{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T14:04:57Z","timestamp":1783346697512,"version":"3.54.6"},"reference-count":56,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T00:00:00Z","timestamp":1585612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M660782"],"award-info":[{"award-number":["2019M660782"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0\u201330 cm) samples (10.32 kg m\u22122 (\u00b10.53) for SOC, 1.21 kg m\u22122 (\u00b10.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m\u22122 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m\u22122 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.<\/jats:p>","DOI":"10.3390\/rs12071115","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9263-4219","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"first","affiliation":[{"name":"College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, Liaoning Province, China"},{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4536-9851","authenticated-orcid":false,"given":"Qianlai","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, Liaoning Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijiao","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, Liaoning Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, Liaoning Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1111\/j.1365-2389.1996.tb01386.x","article-title":"Total carbon and nitrogen in the soils of the world","volume":"47","author":"Batjes","year":"1996","journal-title":"Eur. J. Soil Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1890\/02-0251","article-title":"Carbon sequestration in ecosystems: The role of stoichiometry","volume":"85","author":"Hessen","year":"2004","journal-title":"Ecology"},{"key":"ref_3","first-page":"431","article-title":"Carbon and nitrogen interactions in the terrestrial biosphere: Anthropogenic effects","volume":"2","author":"Melillo","year":"1996","journal-title":"Glob. Chang. Terr. Ecosyst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"335","DOI":"10.2136\/sssaj2003.3350","article-title":"Carbon and nitrogen in Danish forest soils\u2014Contents and distribution determined by soil order","volume":"67","author":"Vejre","year":"2003","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.foreco.2005.08.015","article-title":"Forest soils and carbon sequestration","volume":"220","author":"Lal","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.scitotenv.2016.02.131","article-title":"Climate change and its impacts on vegetation distribution and net primary productivity of the alpine ecosystem in the Qinghai-Tibetan Plateau","volume":"554","author":"Gao","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7423","DOI":"10.5194\/bg-10-7423-2013","article-title":"Variability of above-ground litter inputs alters soil physicochemical and biological processes: A meta-analysis of litterfall-manipulation experiments","volume":"10","author":"Xu","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1111\/j.1747-0765.2007.00133.x","article-title":"Quantifying greenhouse gas emissions from soils: Scientific basis and modeling approach","volume":"53","author":"Li","year":"2007","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.geoderma.2017.05.048","article-title":"Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China","volume":"305","author":"Wang","year":"2017","journal-title":"Geoderma"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Jenny, H. (1941). Factors of Soil Formation, McGraw-Hill.","DOI":"10.1097\/00010694-194111000-00009"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.5194\/bg-8-1053-2011","article-title":"Spatial distribution of soil organic carbon stocks in France","volume":"8","author":"Martin","year":"2011","journal-title":"Biogeosciences"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1071\/SR15100","article-title":"Predictive mapping of soil organic carbon stocks in South Australia\u2019s agricultural zone","volume":"53","author":"Liddicoat","year":"2015","journal-title":"Soil Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.catena.2018.03.023","article-title":"Mapping total soil nitrogen from a site in northeastern China","volume":"166","author":"Wang","year":"2018","journal-title":"Catena"},{"key":"ref_16","first-page":"405","article-title":"Implications of alternative field-sampling designs on Landsat-based mapping of stand age and carbon stocks in Oregon forests","volume":"56","author":"Duane","year":"2010","journal-title":"For. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/00380768.2011.557769","article-title":"Estimating soil carbon stocks in an upland area of Tokachi District, Hokkaido, Japan, by satellite remote sensing","volume":"57","author":"Niwa","year":"2011","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.scitotenv.2016.03.085","article-title":"Assessment of soil organic carbon stocks under future climate and land cover changes in Europe","volume":"557","author":"Yigini","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","unstructured":"Wang, S., Gao, J., Zhuang, Q., Lu, Y., Gu, H., and Jin, X. (2020). Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China. Remote Sens., 12.","DOI":"10.3390\/rs12030393"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/j.geoderma.2012.05.022","article-title":"A geographically weighted regression kriging approach for mapping soil organic carbon stock","volume":"189","author":"Kumar","year":"2012","journal-title":"Geoderma"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.geoderma.2012.07.020","article-title":"Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy","volume":"199","author":"Nocita","year":"2013","journal-title":"Geoderma"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.jenvman.2006.09.020","article-title":"Regional patterns of soil organic carbon stocks in China","volume":"85","author":"Yu","year":"2007","journal-title":"J. Environ. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hartemink, A.E. (2008). Purposive sampling for digital soil mapping for areas with limited data. Digital Soil Mapping with Limited Data, Springer.","DOI":"10.1007\/978-1-4020-8592-5"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/0734-189X(84)90107-5","article-title":"A new approach to removing cloud cover from satellite imagery","volume":"25","author":"Liu","year":"1984","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Odebiri, O., Mutanga, O., Odindi, J., Peerbhay, K., and Dovey, S. (2020). Predicting soil organic carbon stocks under commercial forest plantations in KwaZulu-Natal province, South Africa using remotely sensed data. GIScience Remote Sens., 1\u201314.","DOI":"10.1080\/15481603.2020.1731108"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Perkins, T., Adlergolden, S., Matthew, M., Berk, A., Anderson, G., and Gardner, J. (2005). Retrieval of atmospheric properties from hyper and multispectral imagery with the FLAASH atmospheric correction algorithm. Remote Sensing of Clouds & the Atmosphere X, International Society for Optics and Photonics.","DOI":"10.1117\/12.626526"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pimple, U., Sitthi, A., Simonetti, D., Pungkul, S., Leadprathom, K., and Chidthaisong, A. (2017). Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand. Sustainability, 9.","DOI":"10.3390\/su9020258"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0034-4257(91)90017-Z","article-title":"Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer","volume":"35","author":"Goward","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0034-4257(02)00048-2","article-title":"A generalized soil-adjusted vegetation index","volume":"82","author":"Gilabert","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","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_34","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1080\/01431169008955053","article-title":"A ratio vegetation index adjusted for soil brightness","volume":"11","author":"Major","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"385","DOI":"10.13031\/2013.16057","article-title":"Comparison of eleven vegetation indices for estimating plant height of alfalfa and grass","volume":"20","author":"Payero","year":"2004","journal-title":"Appl. Eng. Agric."},{"key":"ref_37","first-page":"431","article-title":"Geographically weighted regression","volume":"47","author":"Brunsdon","year":"1998","journal-title":"R. Stat. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.apgeog.2013.04.002","article-title":"Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging","volume":"42","author":"Wang","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1287\/mnsc.32.7.878","article-title":"An adaptive filter for estimating spatially-varying parameters: Application to modeling police hours spent in response to calls for service","volume":"32","author":"Foster","year":"1986","journal-title":"Manag. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.geoderma.2009.01.021","article-title":"Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China","volume":"150","author":"Wang","year":"2009","journal-title":"Geoderma"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.jhazmat.2013.07.065","article-title":"Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils","volume":"261","author":"Lv","year":"2013","journal-title":"J. Hazard. Mater."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.jbiosc.2014.01.013","article-title":"Efficient folding\/assembly in Chinese hamster ovary cells is critical for high quality (low aggregate content) of secreted trastuzumab as well as for high production: Stepwise multivariate regression analyses","volume":"118","author":"Ishii","year":"2014","journal-title":"J. Biosci. Bioeng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e71","DOI":"10.7717\/peerj.71","article-title":"Predicting and mapping soil available water capacity in Korea","volume":"1","author":"Hong","year":"2013","journal-title":"PeerJ"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhuang, Q., Yang, Z., Yu, N., and Jin, X. (2019). Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China. Forests, 10.","DOI":"10.3390\/f10111023"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A concordance correlation coefficient to evaluate reproducibility","volume":"45","author":"Lin","year":"1989","journal-title":"Biometrics"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"746","DOI":"10.2136\/sssaj2000.642746x","article-title":"Field-scale mapping of surface soil organic carbon using remotely sensed imagery","volume":"64","author":"Chen","year":"2000","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.ecolind.2018.01.049","article-title":"Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia","volume":"88","author":"Wang","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.foreco.2006.05.055","article-title":"Soil property variations in relation to topographic aspect and vegetation community in the south-eastern highlands of Ethiopia","volume":"232","author":"Yimer","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"076801","DOI":"10.1088\/0256-307X\/36\/7\/076801","article-title":"Experimental realization of an intrinsic magnetic topological insulator","volume":"36","author":"Gong","year":"2019","journal-title":"Chin. Phys. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Qi, L., Wang, S., Zhuang, Q., Yang, Z., Bai, S., Jin, X., and Lei, G. (2019). Spatial-temporal changes in soil organic carbon and pH in the Liaoning Province of China: A modeling analysis based on observational data. Sustainability, 11.","DOI":"10.3390\/su11133569"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yang, R., Rossiter, D.G., Liu, F., Lu, Y., Yang, F., Yang, F., and Zhang, G. (2015). Predictive mapping of topsoil organic carbon in an alpine environment aided by Landsat TM. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0139042"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.catena.2018.03.007","article-title":"Examining soil organic carbon distribution and dynamic change in a hickory plantation region with Landsat and ancillary data","volume":"165","author":"Lu","year":"2018","journal-title":"Catena"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.geoderma.2008.06.011","article-title":"Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study","volume":"146","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.geoderma.2019.02.019","article-title":"Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging","volume":"342","author":"Pouladi","year":"2019","journal-title":"Geoderma"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1115\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:13:54Z","timestamp":1760174034000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,31]]},"references-count":56,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071115"],"URL":"https:\/\/doi.org\/10.3390\/rs12071115","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,31]]}}}