{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:44:29Z","timestamp":1778755469451,"version":"3.51.4"},"reference-count":69,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371227"],"award-info":[{"award-number":["41371227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Key R&amp;D Program of China","award":["2017YFC0506200"],"award-info":[{"award-number":["2017YFC0506200"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["the Fundamental Research Funds for the Central Universities"],"award-info":[{"award-number":["the Fundamental Research Funds for the Central Universities"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate digital mapping of soil organic carbon (SOC) is important in understanding the global carbon cycle and its implications in mitigating climate change. Visible and near-infrared hyperspectral imaging technology provides an alternative for mapping SOC efficiently and accurately, especially at regional and global scales. However, there is a lack of understanding of the impacts of spatial resolution of hyperspectral images and spatial autocorrelation of spectral information on the accuracy of SOC retrievals. In this study, the hyperspectral images (380\u20131700 nm) with a spatial resolution of 1 m were acquired by Headwall Micro-Hyperspec airborne sensors. Then, hyperspectral images were resampled into three different spatial resolutions of 10 m, 30 m, and 60 m by near neighbor (NN), bilinear interpolation (BI), and cubic convolution (CC) resampling methods. The geographically weighted regression (GWR) model was used to explore the role of spatial autocorrelation in predicting SOC contrast with the partial least squares regression (PLSR) model. Results showed that (1) the hyperspectral images can be used to predict SOC and the spatial autocorrelation can improve the prediction accuracy, as the ratio of performance to interquartile range (RPIQ) values of PLSR and GWR were 1.957 and 2.003; (2) The SOC prediction accuracy decreased with the degradation of spatial resolution, and the RPIQ values of PLSR were from 1.957 to 1.134, and of GWR were from 2.003 to 1.136; (3) Three resampling methods had a much weaker influence than spatial resolution on SOC predictions because the differences of RPIQ values of NN, BI, and CC resampling methods were 0.146, 0.175, and 0.025 in the spatial resolutions of 10 m, 30 m, and 60 m, respectively; (4) Finally, the Global Moran\u2019s I and the Anselin Local Moran\u2019s I proved the existence of the spatial autocorrelation in SOC maps. We hope that this study can offer valuable information for digital soil mapping by satellite hyperspectral images in the near future.<\/jats:p>","DOI":"10.3390\/rs11091032","type":"journal-article","created":{"date-parts":[[2019,5,2]],"date-time":"2019-05-02T03:15:22Z","timestamp":1556766922000},"page":"1032","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Exploring the Influence of Spatial Resolution on the Digital Mapping of Soil Organic Carbon by Airborne Hyperspectral VNIR Imaging"],"prefix":"10.3390","volume":"11","author":[{"given":"Long","family":"Guo","sequence":"first","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiezhu","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Linderman","sequence":"additional","affiliation":[{"name":"Geographical and Sustainability Sciences, The University of Iowa, Iowa City, IA 52246, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-3239","authenticated-orcid":false,"given":"Yiyun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1080\/713610854","article-title":"Global potential of soil carbon sequestration to mitigate the greenhouse effect","volume":"22","author":"Lal","year":"2003","journal-title":"Crit. Rev. Plant Sci."},{"key":"ref_2","first-page":"257","article-title":"Role of soil survey in obtaining a global carbon budget","volume":"21","author":"Arnold","year":"1995","journal-title":"Soils Glob. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.2136\/sssaj2011.0424","article-title":"Efficiency comparison of conventional and digital soil mapping for updating soil maps","volume":"76","author":"Kempen","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K., Welp, G., and Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: A comparison of machine learning and multiple linear regression models. PloS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.geoderma.2018.09.006","article-title":"Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran","volume":"338","author":"Zeraatpisheh","year":"2019","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1038\/nature04514","article-title":"Temperature sensitivity of soil carbon decomposition and feedbacks to climate change","volume":"440","author":"Davidson","year":"2006","journal-title":"Nature"},{"key":"ref_7","first-page":"1","article-title":"Tools for proximal soil sensing","volume":"Volume 6","author":"Ditzler","year":"2015","journal-title":"Soil Survey Manua"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2953","DOI":"10.1111\/gcb.12569","article-title":"Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change","volume":"20","author":"Webster","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","unstructured":"Dhawale, N.M., Adamchuk, V.I., Prasher, S.O., Rossel, R.A.V., Ismail, A.A., Whalen, J.K., and Louargant, M. (2014, January 13\u201316). Comparing Visible\/NIR and MIR Hyperspectrometry for Measuring Soil Physical Properties. Proceedings of the International Conference of American Society of Biological Engineers, Montreal, QC, Canada."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.geoderma.2018.08.005","article-title":"Spectral fusion by Outer Product Analysis (OPA) to improve predictions of soil organic C","volume":"335","author":"Terra","year":"2019","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1111\/ejss.12733","article-title":"Estimating soil texture from vis\u2013NIR spectra","volume":"70","author":"Hobley","year":"2019","journal-title":"Eur. J.Soil Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kawamura, K., Tsujimoto, Y., Nishigaki, T., Andriamananjara, A., Rabenarivo, M., Asai, H., Rakotoson, T., and Razafimbelo, T. (2019). Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar. Remote Sens., 11.","DOI":"10.3390\/rs11050506"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1366\/13-07294","article-title":"Soil Organic Carbon Content Estimation with Laboratory-Based Visible-Near-Infrared Reflectance Spectroscopy: Feature Selection","volume":"68","author":"Shi","year":"2014","journal-title":"Appl. Spectrosc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1111\/j.1365-2389.2012.01495.x","article-title":"Predicting soil properties from the Australian soil visible-near infrared spectroscopic database","volume":"63","author":"Webster","year":"2012","journal-title":"Eur. J. Soil Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1155\/2018\/3168974","article-title":"Visible and near-infrared reflectance spectroscopy for investigating soil mineralogy: a review","volume":"2018","author":"Fang","year":"2018","journal-title":"J. Spectrosc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.geoderma.2015.07.007","article-title":"Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field","volume":"261","author":"Cambou","year":"2016","journal-title":"Geoderma"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ji, W.J., Shi, Z., Huang, J.Y., and Li, S. (2014). In Situ Measurement of Some Soil Properties in Paddy Soil Using Visible and Near-Infrared Spectroscopy. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105708"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1080\/10106049.2014.894585","article-title":"Prediction of the presence of topsoil nitrogen from spaceborne hyperspectral data","volume":"30","author":"Gopal","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","article-title":"Three decades of hyperspectral remote sensing of the Earth: A personal view","volume":"113","author":"Goetz","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.2136\/sssaj2012.0054","article-title":"Soil Organic Carbon Predictions by Airborne Imaging Spectroscopy: Comparing Cross-Validation and Validation","volume":"76","author":"Stevens","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1071\/WF16122","article-title":"Evaluation of the spectral characteristics of five hyperspectral and multispectral sensors for soil organic carbon estimation in burned areas","volume":"26","author":"Peon","year":"2017","journal-title":"Int. J. Wildland Fire"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4034","DOI":"10.1016\/j.rse.2008.01.022","article-title":"Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem","volume":"112","author":"Hestir","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.geoderma.2006.07.004","article-title":"Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy","volume":"137","author":"McGlynn","year":"2006","journal-title":"Geoderma"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.geoderma.2009.12.025","article-title":"Using data mining to model and interpret soil diffuse reflectance spectra","volume":"158","author":"Behrens","year":"2010","journal-title":"Geoderma"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Y., Pan, X., Wang, C., Li, Y., and Shi, R. (2015). Predicting Soil Salinity with Vis\u2013NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140688"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.geoderma.2013.06.005","article-title":"Combining Vis\u2013NIR hyperspectral imagery and legacy measured soil profiles to map subsurface soil properties in a Mediterranean area (Cap-Bon, Tunisia)","volume":"209","author":"Lagacherie","year":"2013","journal-title":"Geoderma"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2018.03.025","article-title":"Local modeling approaches for estimating soil properties in selected Indian soils using diffuse reflectance data over visible to near-infrared region","volume":"325","author":"Gupta","year":"2018","journal-title":"Geoderma"},{"key":"ref_29","unstructured":"Farrukh, N. (2011). Evaluation of the eo-1heperion Image for Assessment of Soil Organic Carbon in Faizabad Watershed. [Master\u2019s Thesis, University of Central Asia]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.geoderma.2017.11.006","article-title":"A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra","volume":"314","author":"Dotto","year":"2018","journal-title":"Geoderma"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.cageo.2017.04.008","article-title":"Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation","volume":"104","author":"Vasat","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1590\/S0100-06832014000600014","article-title":"Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and PH at different moisture content levels using visible and near-infrared spectroscopy","volume":"38","author":"Tekin","year":"2014","journal-title":"Revista Brasileira de Ci\u00eancia do Solo"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.geoderma.2016.10.010","article-title":"Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology","volume":"285","author":"Guo","year":"2017","journal-title":"Geoderma"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.rse.2015.06.012","article-title":"An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities","volume":"167","author":"Lee","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_36","unstructured":"Ben-Dor, E., Kafri, A., and Varacalli, G. (2014, January 13\u201318). SHALOM: An Italian\u2013Israeli hyperspectral orbital mission\u2014Update. Proceedings of the International Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_37","first-page":"409","article-title":"A novel chlorophyll-a inversion model in turbid water for GF-5 satellite hyperspectral sensor\u2014A case in Poyang Lake","volume":"03","author":"Deng","year":"2018","journal-title":"J. Cent. China Normal Univ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.02.019","article-title":"Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR\/SWIR imagery","volume":"164","author":"Gomez","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.034","article-title":"Incorporating spatial information in spectral unmixing: A review","volume":"149","author":"Shi","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Griffith, D.A., and Chun, Y. (2016). Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data. Remote Sens., 8.","DOI":"10.3390\/rs8070535"},{"key":"ref_41","first-page":"312","article-title":"Vis-NIR spectral inversion for prediction of soil total nitrogencontent in laboratory based on locally weighted regression","volume":"52","author":"Chen","year":"2015","journal-title":"Acta Pedol. Sin."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.geoderma.2018.09.004","article-title":"Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: A case study with West African soils","volume":"337","author":"Heil","year":"2019","journal-title":"Geoderma"},{"key":"ref_43","first-page":"2157","article-title":"Study of spatial interpolation of soil cd contents in sewage irrigated area based on soil spectral information assistance","volume":"33","author":"Chen","year":"2013","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_44","unstructured":"Burt, R., and Staff, S. (2014). Kellogg soil survey laboratory methods manual. Natural Resources Conservation Services, National Soil Survey Center."},{"key":"ref_45","unstructured":"ISO10694, ISO (1995). Soil Quality\u2014Determination of Organic and Total Carbon After Dry Combustion (Elementary Analysis), ISO."},{"key":"ref_46","first-page":"38","article-title":"Peirce\u2019s criterion for the elimination of suspect experimental data","volume":"20","author":"Ross","year":"2003","journal-title":"J. Eng. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.geoderma.2018.09.003","article-title":"Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images","volume":"337","author":"Guo","year":"2019","journal-title":"Geoderma"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Guo, L., Linderman, M., Shi, T.Z., Chen, Y.Y., Duan, L.J., and Zhang, H.T. (2018). Exploring the Sensitivity of Sampling Density in Digital Mapping of Soil Organic Carbon and Its Application in Soil Sampling. Remote Sens., 10.","DOI":"10.3390\/rs10060888"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1109\/42.816070","article-title":"Survey: Interpolation methods in medical image processing","volume":"18","author":"Lehmann","year":"1999","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1109\/83.503915","article-title":"Extraction of high-resolution frames from video sequences","volume":"5","author":"Schultz","year":"1996","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image-processing","volume":"29","author":"Keys","year":"1981","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression: a tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.geoderma.2014.12.012","article-title":"Evaluation of soil quality for agricultural production using visible-near-infrared spectroscopy","volume":"243","author":"Askari","year":"2015","journal-title":"Geoderma"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.catena.2016.05.001","article-title":"Methods of evaluating soil bulk density: Impact on estimating large scale soil organic carbon storage","volume":"144","author":"Xu","year":"2016","journal-title":"Catena"},{"key":"ref_55","first-page":"61","article-title":"Multivariate calibration","volume":"1158","author":"Harald","year":"1991","journal-title":"Technometrics"},{"key":"ref_56","first-page":"431","article-title":"Geographically weighted regression","volume":"47","author":"Brunsdon","year":"1998","journal-title":"J. R. Stat. Soc. Ser. D (The Statistician)"},{"key":"ref_57","unstructured":"Charlton, M., Fotheringham, S., and Brunsdon, C. (2009). Geographically weighted regression. White Paper. National Centre for Geocomputation, National University of Ireland Maynooth."},{"key":"ref_58","unstructured":"Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2002). Geographically Weighted Regression, Wiley."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2013.07.016","article-title":"Which strategy is best to predict soil properties of a local site from a national Vis\u2013NIR database?","volume":"213","author":"Gomez","year":"2014","journal-title":"Geoderma"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.trac.2010.05.006","article-title":"Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy","volume":"29","author":"Palagos","year":"2010","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_61","unstructured":"Li, D., and Chen, Y. (2012). Computer and Computing Technologies in Agriculture: 5th IFIP TC 5, SIG 5.1 International Conference, CCTA 2011, Beijing, China, 29\u201331 October 2011, Proceedings, Springer Science & Business Media."},{"key":"ref_62","unstructured":"Landgrebe, D.A. (2005). Signal Theory Methods in Multispectral Remote Sensing, John Wiley & Sons."},{"key":"ref_63","unstructured":"Schowengerdt, R.A. (2006). Remote Sensing: Models and Methods for Image Processing, Academic Press."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.025","article-title":"Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon","volume":"179","author":"Castaldi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1190\/1.1440951","article-title":"Near-infrared (1.3\u20132.4) \u03bcm spectra of alteration minerals\u2014Potential for use in remote sensing","volume":"44","author":"Hunt","year":"1979","journal-title":"Geophysics"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"772409","DOI":"10.1117\/12.854365","article-title":"Near real-time endmember extraction from remotely sensed hyperspectral data using NVidia GPUs","volume":"Volume 7724","author":"Plaza","year":"2010","journal-title":"Real-Time Image and Video Processing 2010"},{"key":"ref_67","unstructured":"Wold, S., Johansson, E., and Cocchi, M. (2002). Partial Least Squares Projections to Latent Structures, Wiley & Sons."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.still.2014.12.002","article-title":"Comparing geospatial techniques to predict SOC stocks","volume":"148","author":"Liu","year":"2015","journal-title":"Soil Tillage Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11355-013-0236-5","article-title":"Small-scale spatial variability of soil properties in a Korean swamp","volume":"11","author":"Yoon","year":"2015","journal-title":"Landsc. Ecol. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1032\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:48:26Z","timestamp":1760186906000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,1]]},"references-count":69,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11091032"],"URL":"https:\/\/doi.org\/10.3390\/rs11091032","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,1]]}}}