{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T08:17:30Z","timestamp":1775204250921,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National key research and development program (Intergovernmental cooperation in interna-tional science and technology innovation of the Ministry of science and technology)","award":["2021YFE0102000"],"award-info":[{"award-number":["2021YFE0102000"]}]},{"name":"Scientific research project of the National Natural Science Foundation of China","award":["41601311"],"award-info":[{"award-number":["41601311"]}]},{"name":"Key projects of Science &amp; Technology Department of Sichuan Province","award":["17ZA0308"],"award-info":[{"award-number":["17ZA0308"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400\u20132450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg\/kg; 0.7440, 6.6910 mg\/kg; and 0.9009, 3.2103 g\/kg, respectively, and their corresponding calibration methods were (FD-LGR)\/SPA\/PLSR, (FD-LGR)\/SPA\/PLSR, and SG+MSC\/CARS\/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture.<\/jats:p>","DOI":"10.3390\/rs13194000","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"4000","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3692-0453","authenticated-orcid":false,"given":"Peng","family":"Guo","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"given":"Ting","family":"Li","sequence":"additional","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 611130, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1633-6652","authenticated-orcid":false,"given":"Han","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"given":"Xiuwan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8970-0123","authenticated-orcid":false,"given":"Yifeng","family":"Cui","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yanru","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10311-008-0166-x","article-title":"Evaluation of Soil Fertility Using Infrared Spectroscopy: A Review","volume":"7","author":"Du","year":"2009","journal-title":"Environ. Chem. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.geoderma.2005.03.028","article-title":"Advancing the frontiers of soil science towards a geoscience","volume":"131","author":"Wilding","year":"2006","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.agee.2015.02.010","article-title":"Nutrient recycling in organic farming is related to diversity in farm types at the local level","volume":"204","author":"Nowak","year":"2015","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1111\/nph.15361","article-title":"Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon","volume":"221","author":"Sokol","year":"2018","journal-title":"New Phytol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.still.2017.09.006","article-title":"Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data","volume":"175","author":"Qi","year":"2018","journal-title":"Soil Tillage Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Song, Y., Zhao, X., Su, H., Li, B., Hu, Y., and Cui, X. (2018). Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale. Sensors, 18.","DOI":"10.3390\/s18093086"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.agee.2015.11.024","article-title":"Soil nutrient build-up, input interaction effects and plot level N and P balances under long-term addition of compost and NP fertilizer","volume":"218","author":"Bedada","year":"2016","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.geoderma.2018.11.048","article-title":"Pedometrics timeline","volume":"338","author":"McBratney","year":"2019","journal-title":"Geoderma"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"104589","DOI":"10.1016\/j.still.2020.104589","article-title":"Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest","volume":"199","author":"Hong","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_11","unstructured":"Lillesand, T.M., and Kiefer, R.W. (1994). Remote Sensing and Image Interpretation, John Wiley & Sons. [3rd ed.]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.geoderma.2019.01.006","article-title":"Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study","volume":"341","author":"Ji","year":"2019","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"118553","DOI":"10.1016\/j.saa.2020.118553","article-title":"Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra","volume":"240","author":"Zhang","year":"2020","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2010.05.008","article-title":"Diffuse reflectance spectroscopy in soil science and land resource assessment","volume":"158","author":"Guerrero","year":"2010","journal-title":"Geoderma"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1111\/ejss.12907","article-title":"Assessment of a soil fertility index using visible and near-infrared spectroscopy in the rice paddy region of southern China","volume":"71","author":"Yang","year":"2020","journal-title":"Eur. J. Soil Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"117863","DOI":"10.1016\/j.saa.2019.117863","article-title":"Application of Vis-NIR spectroscopy for determination the content of organic matter in saline-alkali soils","volume":"229","author":"Ba","year":"2020","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.biosystemseng.2016.04.018","article-title":"Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using vis-nir spectroscopy","volume":"152","author":"Morellos","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.still.2015.07.021","article-title":"Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy","volume":"155","author":"Nawar","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hong, Y., Chen, Y., Yu, L., Liu, Y., Liu, Y., Zhang, Y., Liu, Y., and Cheng, H. (2018). Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS\u2013NIR Spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10030479"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, P., Liu, Z., Hu, Y., Shi, Z., Pan, Y., Wang, L., and Wang, G. (2019). Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data. Sustainability, 11.","DOI":"10.3390\/su11020419"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.geoderma.2017.09.013","article-title":"Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis\u2013NIR spectroscopy","volume":"310","author":"Xu","year":"2018","journal-title":"Geoderma"},{"key":"ref_23","first-page":"1033","article-title":"Research Progress and Prospect on Soil Nutrients Monitoring with Remote Sensing","volume":"30","author":"Wang","year":"2015","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ji, W., Shi, Z., Huang, J., 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_25","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"Trac-Trends Anal. Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1007\/s11368-017-1766-5","article-title":"Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties","volume":"18","author":"Conforti","year":"2017","journal-title":"J. Soils Sediments"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.chemolab.2007.06.006","article-title":"ParLeS: Software for chemometric analysis of spectroscopic data","volume":"90","author":"Rossel","year":"2008","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1366\/0003702894202201","article-title":"Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra","volume":"43","author":"Barnes","year":"1989","journal-title":"Appl. Spectrosc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"141282","DOI":"10.1016\/j.scitotenv.2020.141282","article-title":"Water-based measured-value fuzzification improves the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy","volume":"749","author":"Lin","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, S., Chen, Y., Wang, M., Zhao, Y., and Li, J. (2019). SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions. Remote Sens., 11.","DOI":"10.3390\/rs11080967"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.geoderma.2008.04.007","article-title":"Comparison of multivariate methods for inferential modeling of soil carbon using visible\/near-infrared spectra","volume":"146","author":"Vasques","year":"2008","journal-title":"Geoderma"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.geoderma.2018.09.051","article-title":"Quantification of organic carbon concentrations and stocks of tidal marsh sediments via mid-infrared spectroscopy","volume":"337","author":"Govers","year":"2019","journal-title":"Geoderma"},{"key":"ref_34","first-page":"171","article-title":"A spectral soil quality index (SSQI) for characterizing soil function in areas of changed land use","volume":"230\u2013231","author":"Shachak","year":"2014","journal-title":"Geoderma"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/0169-7439(95)80098-T","article-title":"Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data","volume":"29","author":"Helland","year":"1995","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s12665-012-1955-x","article-title":"Soil moisture retrieving using hyperspectral data with the application of wavelet analysis","volume":"69","author":"Peng","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Zhu, C., Zhang, Z., Wang, H., Wang, J., and Yang, S. (2020). Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions. Sensors, 20.","DOI":"10.3390\/s20061795"},{"key":"ref_39","first-page":"560","article-title":"Spectral Characteristics of Soil Salinity Based on Different Pre-processing Methods","volume":"48","author":"Zhu","year":"2017","journal-title":"Chin. J. Soil Sci."},{"key":"ref_40","first-page":"103","article-title":"Hyperspectral estimation of soil organic matter content based on partial least squares regression","volume":"31","author":"Yu","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.aca.2010.03.048","article-title":"Variables selection methods in near-infrared spectroscopy","volume":"667","author":"Zou","year":"2010","journal-title":"Anal. Chim. Acta"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.geoderma.2018.01.017","article-title":"Effects of image pansharpening on soil total nitrogen prediction models in South India","volume":"320","author":"Xu","year":"2018","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1080\/03650340.2020.1802013","article-title":"Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis\u2013NIR spectroscopy","volume":"67","author":"Cheng","year":"2020","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_45","first-page":"1","article-title":"Rapid evaluation of soil fertility in tea plantation based on near-infrared spectroscopy","volume":"51","author":"Ning","year":"2019","journal-title":"Spectrosc. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"117191","DOI":"10.1016\/j.saa.2019.117191","article-title":"Hyperspectral indirect inversion of heavy-metal copper in reclaimed soil of iron ore area","volume":"222","author":"Shen","year":"2019","journal-title":"Spectroc. Acta Part A Mol. Biomol. Spectosc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"114568","DOI":"10.1016\/j.geoderma.2020.114568","article-title":"Evaluating the characteristics of soil vis-NIR spectra after the removal of moisture effect using external parameter orthogonalization","volume":"376","author":"Liu","year":"2020","journal-title":"Geoderma"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.aca.2009.06.046","article-title":"Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration","volume":"648","author":"Li","year":"2009","journal-title":"Anal. Chim. Acta."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00175354","article-title":"A genetic algorithm tutorial","volume":"4","author":"Whitley","year":"1994","journal-title":"Stat. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0169-7439(01)00119-8","article-title":"The successive projections algorithm for variable selection in spectroscopic multicomponent analysis","volume":"57","author":"Saldanha","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2007.02.005","article-title":"Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)","volume":"110","author":"Farifteh","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1016\/j.ecolind.2011.03.025","article-title":"Using hyperspectral vegetation indices as a proxy to monitor soil salinity","volume":"11","author":"Zhang","year":"2011","journal-title":"Ecol. Indic."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jin, X., Li, S., Zhang, W., Zhu, J., and Sun, J. (2020). Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms. Appl. Sci., 10.","DOI":"10.3390\/app10041520"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geoderma.2010.03.001","article-title":"Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy","volume":"158","author":"Mouazen","year":"2010","journal-title":"Geoderma"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2013.04.012","article-title":"Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data","volume":"82","author":"Ramoelo","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Jia, S., Li, H., Wang, Y., Tong, R., and Li, Q. (2017). Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen. Sensors, 17.","DOI":"10.3390\/s17102252"},{"key":"ref_57","first-page":"26","article-title":"Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data","volume":"58","author":"Pullanagari","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, H., Jia, S., and Le, Z. (2019). Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine. Sensors, 19.","DOI":"10.3390\/s19204355"},{"key":"ref_59","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_60","doi-asserted-by":"crossref","first-page":"104296","DOI":"10.1016\/j.catena.2019.104296","article-title":"The Hongqiaocun Site: The earliest evidence of ancient flood sedimentation of the water conservancy facilities in the Chengdu Plain, China","volume":"185","author":"Huang","year":"2020","journal-title":"Catena"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.geoderma.2019.07.010","article-title":"A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database","volume":"353","author":"Ward","year":"2019","journal-title":"Geoderma"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1097\/00010694-193401000-00003","article-title":"An examination of Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method","volume":"37","author":"Walkley","year":"1934","journal-title":"Soil Sci."},{"key":"ref_63","unstructured":"Lu, R.K. (2000). Methods of Soil and Agrochemistry Analysis, China Agricultural Science and Technology Press."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0065-2113(10)07005-7","article-title":"Visible and near infrared spectroscopy in soil science","volume":"107","author":"Stenberg","year":"2010","journal-title":"Adv. Agron."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2017.11.029","article-title":"Assessment of important soil properties related to Chinese Soil Taxonomy based on vis\u2013NIR reflectance spectroscopy","volume":"144","author":"Xu","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.3390\/rs6042699","article-title":"Estimating Soil Organic Carbon Using VIS\/NIR Spectroscopy with SVMR and combining Methods","volume":"6","author":"Peng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.chemolab.2018.03.003","article-title":"libPLS: An integrated library for partial least squares regression and linear discriminant analysis","volume":"176","author":"Li","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.03.033","article-title":"Multi-method ensemble selection of spectral bands related to leaf biochemistry","volume":"164","author":"Feilhauer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1137\/0905052","article-title":"The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses","volume":"5","author":"Wold","year":"1984","journal-title":"SIAM J. Sci. Stat. Comput."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression: A tutorial. Anal","volume":"185","author":"Geladi","year":"1986","journal-title":"Chim. Acta"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.geoderma.2011.08.001","article-title":"Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy","volume":"166","author":"Vohland","year":"2011","journal-title":"Geoderma"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"176","DOI":"10.2136\/sssaj2008.0015","article-title":"Modeling of Soil Organic Carbon Fractions Using Visible\u2013Near-Infrared Spectroscopy","volume":"73","author":"Vasques","year":"2009","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4265","DOI":"10.1080\/01431161.2017.1317941","article-title":"A probabilistic SVM approach for hyperspectral image classification using spectral and texture features","volume":"38","author":"Ghassemian","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.geoderma.2018.10.015","article-title":"Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions","volume":"337","author":"Dalmolin","year":"2019","journal-title":"Geoderma"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.biosystemseng.2005.05.001","article-title":"Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy","volume":"91","author":"Saeys","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1071\/SR10098","article-title":"Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy","volume":"49","author":"Shao","year":"2011","journal-title":"Soil Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-infrared reflectance spectroscopy\u2013principal components regression analyses of soil properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_79","unstructured":"Nielsen, D.R., and Bouma, J. (1985). Spatial variability: It\u2019s documentation, accommodation and implication to soil surveys. Soil Spatial Variability, Las Vegas NV, 30 November\u20141 December 1984, Pudoc."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.2136\/sssaj2018.03.0099","article-title":"Transferability of Vis-NIR models for Soil Organic Carbon Estimation between Two Study Areas by using Spiking","volume":"82","author":"Hong","year":"2018","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.geoderma.2013.03.018","article-title":"Laboratory assessment of three quantitative methods for estimating the organic matter content of soils in China based on visible\/near-infrared reflectance spectra","volume":"202\u2013203","author":"Tian","year":"2013","journal-title":"Geoderma"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Wang, G., Wang, W., Fang, Q., Jiang, H., Xin, Q., and Xue, B. (2018). The Application of Discrete Wavelet Transform with Improved Partial Least-Squares Method for the Estimation of Soil Properties with Visible and Near-Infrared Spectral Data. Remote Sens., 10.","DOI":"10.3390\/rs10060867"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.geoderma.2013.09.021","article-title":"Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils","volume":"214\u2013215","author":"Gras","year":"2014","journal-title":"Geoderma"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"568","DOI":"10.2136\/sssaj2012.0093","article-title":"Visible\u2013Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries","volume":"77","author":"Knadel","year":"2013","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/S0034-4257(96)00219-2","article-title":"Relationships of spectral reflectance and color among surface and subsurface horizons of tropical soil profiles","volume":"61","author":"Galvdo","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1097\/00010694-199504000-00005","article-title":"Near infrared analysis (nira) as a method to simultaneously evaluate spectral featureless constituents in soils","volume":"159","author":"Banin","year":"1995","journal-title":"Soil Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1346\/CCMN.1994.0420606","article-title":"Infrared spectroscopic analyses on the nature of water in montmorillonite","volume":"42","author":"Bishop","year":"1994","journal-title":"Clays Clay Miner."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"12653","DOI":"10.1029\/JB095iB08p12653","article-title":"High spectral resolution reflectance spectroscopy of minerals","volume":"95","author":"Clark","year":"1990","journal-title":"J. Geophys. Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1190\/1.1440721","article-title":"Spectral signatures of particulate minerals in the visible and near infrared","volume":"42","author":"Hunt","year":"1977","journal-title":"Geophysics"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.aca.2012.10.007","article-title":"Characterisation of hydrogen bond perturbations in aqueous systems using aquaphotomics and multivariate curve resolution-alternating least squares","volume":"759","author":"Gowen","year":"2013","journal-title":"Anal. Chim. Acta"},{"key":"ref_91","unstructured":"Chen, H.Y. (2012). Hyperspectral Estimation of Major Soil Nutrient Content. [Ph.D. Thesis, Shandong Agricultural University]. (In Chinese with English Abstract)."},{"key":"ref_92","first-page":"274","article-title":"Hyperspectral Estimation of Soil Available Potassium at different Altitudes of the Xihe Watershed","volume":"50","author":"Guo","year":"2019","journal-title":"Chin. J. Soil Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1080\/014311698215090","article-title":"Role of organic matter in obliterating the effects of iron on spectral reflectance and colour of Brazilian tropical soils","volume":"19","author":"Galvao","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1111\/j.1365-2389.2009.01219.x","article-title":"Discriminating between organic matter in soil from grass and forest by near-infrared spectroscopy","volume":"61","author":"Ertlen","year":"2010","journal-title":"Eur. J. Soil Sci."},{"key":"ref_95","first-page":"164","article-title":"Comparison on Inversion Model of Soil Organic Matter Content Based on Hyperspectral Data","volume":"48","author":"Ye","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s11104-012-1436-8","article-title":"Comparison of multivariate methods for estimating soil total nitrogen with visible\/near-infrared spectroscopy","volume":"366","author":"Shi","year":"2013","journal-title":"Plant Soil"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/4000\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:10:01Z","timestamp":1760166601000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/4000"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,6]]},"references-count":96,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13194000"],"URL":"https:\/\/doi.org\/10.3390\/rs13194000","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,6]]}}}