{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:17:13Z","timestamp":1780413433231,"version":"3.54.1"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2015,6,29]],"date-time":"2015-06-29T00:00:00Z","timestamp":1435536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil lead content is an important parameter in environmental and industrial applications. Chemical analysis, the most commonly method for studying soil samples, are costly, however application of soil spectroscopy presents a more viable alternative.  The first step in the method is usually to extract some appropriate spectral features and then regression models are applied to these extracted features. The aim of this paper was to design an accurate and robust regression technique to estimate soil lead contents from laboratory observed spectra. Three appropriate spectral features were selected according to information from other research as well as the spectrum interpretation of field collected soil samples containing lead. These features were then applied to common Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR) and Neural Network (NN) regression models. Results showed that although NN had adequate accuracy, it produced unstable results (i.e., variation of response in different runs). This problem was addressed with application of a Fuzzy Neural Network (FNN) with a least square training strategy.  In addition to the stabilized and unique response, the capability of the proposed FNN was proved in terms of regression accuracy where a Ratio of Performance to Deviation (RPD) of 8.76 was achieved for test samples.<\/jats:p>","DOI":"10.3390\/rs70708416","type":"journal-article","created":{"date-parts":[[2015,6,29]],"date-time":"2015-06-29T10:05:22Z","timestamp":1435572322000},"page":"8416-8435","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Robust Fuzzy Neural Network Model for Soil Lead Estimation from Spectral Features"],"prefix":"10.3390","volume":"7","author":[{"given":"Rohollah","family":"Goodarzi","sequence":"first","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, K.N.Toosi University of Technology,  Tehran 19667-15433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Mokhtarzade","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, K.N.Toosi University of Technology,  Tehran 19667-15433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Zoej","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, K.N.Toosi University of Technology,  Tehran 19667-15433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1080\/10807039.2013.771534","article-title":"Lead (Pb) is now a non-threshold substance: How does this affect soil quality guidelines?","volume":"19","author":"Wilson","year":"2013","journal-title":"Hum. Ecol. Risk Assess. Int. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s10661-010-1649-3","article-title":"Lead (Pb) and arsenic (As) bioaccessibility in various soils from South China","volume":"177","author":"Cui","year":"2011","journal-title":"Environ. Monit. Assess."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, C., Liu, F., and Tang, S. (2012, January 22\u201327). Estimation of heavy metal concentration in the pearl river estuarine waters from remote sensing data. Proceedings of the 2012 IEEE International on Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350953"},{"key":"ref_4","unstructured":"Doebrich, S.J. Uses of Lead. Available online: http:\/\/geology.com\/usgs\/lead\/."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.4314\/mejs.v5i1.85332","article-title":"Heavy metal pollution assessment by partial geochemical extraction technique","volume":"5","author":"Estifanos","year":"2013","journal-title":"Momona Ethiop. J. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3222","DOI":"10.1016\/j.rse.2008.03.017","article-title":"Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the rodalquilar mining area, SE Spain","volume":"112","author":"Choe","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"67","DOI":"10.5194\/isprsannals-I-7-67-2012","article-title":"Soil spectral imaging: Moving from proximal sensing to spatial quantitative domain","volume":"1","author":"Dor","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1016\/j.apgeochem.2005.01.009","article-title":"Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils","volume":"20","author":"Wu","year":"2005","journal-title":"Appl. Geochem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.5713\/ajas.2004.1736","article-title":"Prediction of heavy metal content in compost using near-infrared reflectance spectroscopy","volume":"17","author":"Ko","year":"2004","journal-title":"Asian Australas. J. Anim. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s00254-008-1520-9","article-title":"Qualitative analysis and mapping of heavy metals in an abandoned Au-Ag mine area using nir spectroscopy","volume":"58","author":"Choe","year":"2009","journal-title":"Environ. Geol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1016\/j.cageo.2011.03.009","article-title":"Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model","volume":"37","author":"Liu","year":"2011","journal-title":"Comput. Geosci."},{"key":"ref_15","first-page":"1","article-title":"Relationship between nitrogen and soil properties: Using multiple linear regressions and structural equation modeling","volume":"2","author":"Ibrahim","year":"2013","journal-title":"Int. J. Res. Appl. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1039\/b003805i","article-title":"Introduction to multivariate calibration in analytical chemistryelectronic supplementary information available","volume":"125","author":"Brereton","year":"2000","journal-title":"Analyst"},{"key":"ref_17","unstructured":"Montgomery, D.C., Peck, E.A., and Vining, G.G. (2012). Introduction to Linear Regression Analysis, John Wiley & Sons."},{"key":"ref_18","first-page":"1319","article-title":"A multivariate regression analysis for deriving engineering parameters of expansive soils from spectral reflectance","volume":"37","author":"Yitagesu","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"Pls-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_20","unstructured":"Maitra, S., and Yan, J. (2008). Principle component analysis and partial least squares: Two dimension reduction techniques for regression. Appl. Multivar. Stat. Models, 79\u201390."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0169-7439(93)85002-X","article-title":"Simpls: An alternative approach to partial least squares regression","volume":"18","year":"1993","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_23","first-page":"2011","article-title":"A brief introduction to neural networks","volume":"15","author":"Kriesel","year":"2007","journal-title":"Retriev. August"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive-network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1016\/S1001-0742(09)60335-1","article-title":"Adaptive neuro fuzzy inference system for classification of water quality status","volume":"22","author":"Yan","year":"2010","journal-title":"J. Environ. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","article-title":"Fuzzy identification of systems and its applications to modeling and control","volume":"SMC-15","author":"Takagi","year":"1985","journal-title":"IEEE Trans Syst. Man Cybern."},{"key":"ref_27","unstructured":"Demuth, H., Beale, M., and Hagan, M. (1993). Neural Network Toolbox User\u2019s Guide, The MathWorks Inc."},{"key":"ref_28","unstructured":"Shani, G., and Gunawardana, A. (2011). Recommender Systems Handbook, Springer."},{"key":"ref_29","unstructured":"United States (1975). Soil Taxonomy: Abasic System of Soil Classification for Making and Interpreting Soil Surveys."},{"key":"ref_30","unstructured":"National Geoscience Database of Iran. Available online: http:\/\/www.ngdir.ir\/MiningInfo\/MineDetail.asp?PID=3862."},{"key":"ref_31","first-page":"1","article-title":"Inductively coupled plasma-atomic emission spectrometry","volume":"2","author":"Manning","year":"1997","journal-title":"Chem. Educ."},{"key":"ref_32","unstructured":"Ferraro, J.R., Nakamoto, K., and Brown, C. (2003). Introductory Raman Spectroscopy, Academic Press. [2nd ed.]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1080\/01431160210155965","article-title":"Spectral absorption features as indicators of water status in coast live oak (quercus agrifolia) leaves","volume":"24","author":"Pu","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","first-page":"55","article-title":"Analysis of spectral absorption features in hyperspectral imagery","volume":"5","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","first-page":"1","article-title":"Spectral analysis of absorption features for mapping vegetation cover and microbial communities in yellowstone national park using aviris data","volume":"1717","author":"Kokaly","year":"2007","journal-title":"Prof. Pap."},{"key":"ref_36","first-page":"225","article-title":"The use of remote sensing to locate heavy metal as source of pollution","volume":"7","author":"Paster","year":"2011","journal-title":"Adv. Environ. Res."},{"key":"ref_37","unstructured":"Morrison, D.F. (1990). Multivariate Statistical Methods, McGraw-Hill, Inc.. [3rd ed.]."},{"key":"ref_38","unstructured":"Wonnacott, T.H., and Wonnacott, R.J. (1972). Introductory Statistics, Wiley."},{"key":"ref_39","first-page":"657","article-title":"Partial correlation and conditional correlation as measurment of conditional independed","volume":"46","author":"Baba","year":"2004","journal-title":"J. Stat."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e66972","DOI":"10.1371\/journal.pone.0066972","article-title":"Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the baotou test site","volume":"8","author":"Duan","year":"2013","journal-title":"PLoS ONE"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4111","DOI":"10.1080\/01431160903229200","article-title":"Estimation of heavy metal contamination in soil using reflectance spectroscopy and partial least squares regression","volume":"31","author":"Pandit","year":"2010","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/7\/8416\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:48:30Z","timestamp":1760215710000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/7\/8416"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6,29]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2015,7]]}},"alternative-id":["rs70708416"],"URL":"https:\/\/doi.org\/10.3390\/rs70708416","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,6,29]]}}}