{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:25:00Z","timestamp":1760239500483,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"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>Hyperspectral imaging has recently emerged in the geosciences as a technology that provides rapid, accurate, and high-resolution information from lake sediment cores. Here we introduce a new methodology to infer particle size distribution, an insightful proxy that tracks past changes in aquatic ecosystems and their catchments, from laboratory hyperspectral images of lake sediment cores. The proposed methodology includes data preparation, spectral preprocessing and transformation, variable selection, and model fitting. We evaluated random forest regression and other commonly used statistical methods to find the best model for particle size determination. We tested the performance of combinations of spectral transformation techniques, including absorbance, continuum removal, and first and second derivatives of the reflectance and absorbance, along with different regression models including partial least squares, multiple linear regression, principal component regression, and support vector regression, and evaluated the resulting root mean square error (RMSE), R-squared, and mean relative error (MRE). Our results show that a random forest regression model built on spectra absorbance significantly outperforms all other models. The new workflow demonstrated herein represents a much-improved method for generating inferences from hyperspectral imagery, which opens many new opportunities for advancing the study of sediment archives.<\/jats:p>","DOI":"10.3390\/rs12233850","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T09:06:28Z","timestamp":1606208788000},"page":"3850","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Remote Sensing of Lake Sediment Core Particle Size Using Hyperspectral Image Analysis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9557-495X","authenticated-orcid":false,"given":"Hamid","family":"Ghanbari","sequence":"first","affiliation":[{"name":"Department of Geography, Universit\u00e9 Laval, Qu\u00e9bec, QC G1V 0A6, Canada"},{"name":"Groupe de Recherche Interuniversitaire en Limnologie, Universit\u00e9 de Montr\u00e9al, C.P. 6128, Succursale Centre-Ville, Montr\u00e9al, QC H3C 3J7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5218-8787","authenticated-orcid":false,"given":"Olivier","family":"Jacques","sequence":"additional","affiliation":[{"name":"Department of Geography, Universit\u00e9 Laval, Qu\u00e9bec, QC G1V 0A6, Canada"}]},{"given":"Marc-\u00c9lie","family":"Ada\u00efm\u00e9","sequence":"additional","affiliation":[{"name":"Department of Geography, Universit\u00e9 Laval, Qu\u00e9bec, QC G1V 0A6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0380-5061","authenticated-orcid":false,"given":"Irene","family":"Gregory-Eaves","sequence":"additional","affiliation":[{"name":"Groupe de Recherche Interuniversitaire en Limnologie, Universit\u00e9 de Montr\u00e9al, C.P. 6128, Succursale Centre-Ville, Montr\u00e9al, QC H3C 3J7, Canada"},{"name":"Department of Biology, McGill University, Montr\u00e9al, QC H3A 1B1, Canada"}]},{"given":"Dermot","family":"Antoniades","sequence":"additional","affiliation":[{"name":"Department of Geography, Universit\u00e9 Laval, Qu\u00e9bec, QC G1V 0A6, Canada"},{"name":"Groupe de Recherche Interuniversitaire en Limnologie, Universit\u00e9 de Montr\u00e9al, C.P. 6128, Succursale Centre-Ville, Montr\u00e9al, QC H3C 3J7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.22498\/pages.22.1.10","article-title":"Hyperspectral imaging: A novel, non-destructive method for investigating sub-annual sediment structures and composition","volume":"22","author":"Grosjean","year":"2014","journal-title":"PAGES News"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1080\/03650340.2017.1373185","article-title":"Partial least squares regression (PLSR) associated with spectral response to predict soil attributes in transitional lithologies","volume":"64","author":"Nanni","year":"2017","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_3","unstructured":"Smol, J.P. (2009). Pollution of Lakes and Rivers: A Paleoenvironmental Perspective, John Wiley & Sons."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"96031","DOI":"10.1117\/1.JRS.9.096031","article-title":"Hyperspectral imaging spectroscopy: A promising method for the biogeochemical analysis of lake sediments","volume":"9","author":"Butz","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","first-page":"167","article-title":"Hyperspectral imaging of sedimentary bacterial pigments: A 1700-year history of meromixis from varved Lake Jaczno, northeast Poland","volume":"917","author":"Butz","year":"2017","journal-title":"J. Paleolimnol."},{"key":"ref_6","first-page":"950","article-title":"In-situ reflectance spectroscopy-analysing techniques for high-resolution pigment logging in sediment cores","volume":"91","author":"Rein","year":"2002","journal-title":"Acta Diabetol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s10933-018-0028-x","article-title":"A high-resolution pigment and productivity record from the varved Ponte Tresa basin (Lake Lugano, Switzerland) since 1919: Insight from an approach that combines hyperspectral imaging and high-performance liquid chromatography","volume":"60","author":"Schneider","year":"2018","journal-title":"J. Paleolimnol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s10933-017-0009-5","article-title":"Hyperspectral core logging for fire reconstruction studies","volume":"59","author":"Debret","year":"2018","journal-title":"J. Paleolimnol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.scitotenv.2019.01.320","article-title":"High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core","volume":"663","author":"Jacq","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.5194\/bg-17-2715-2020","article-title":"The influences of historic lake trophy and mixing regime changes on long-term phosphorus fraction retention in sediments of deep eutrophic lakes: A case study from Lake Burg\u00e4schi, Switzerland","volume":"17","author":"Tu","year":"2020","journal-title":"Biogeosciences"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Aymerich, I.F., Oliva, M., Giralt, S., and Mart\u00edn-Herrero, J. (2016). Detection of Tephra Layers in Antarctic Sediment Cores with Hyperspectral Imaging. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0146578"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Last, W.M. (2002). Textural analysis of lake sediments. Tracking Environmental Change Using Lake Sediments, Springer.","DOI":"10.1007\/0-306-47669-X"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"\u017barczy\u0144ski, M., Szma\u0144da, J., and Tylmann, W. (2019). Grain-Size Distribution and Structural Characteristics of Varved Sediments from Lake \u017babi\u0144skie (Northeastern Poland). Quaternary, 2.","DOI":"10.3390\/quat2010008"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.forsciint.2004.02.028","article-title":"Particle size analysis of sediments, soils and related particulate materials for forensic purposes using laser granulometry","volume":"144","author":"Pye","year":"2004","journal-title":"Forensic Sci. Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105536","DOI":"10.1016\/j.sedgeo.2019.105536","article-title":"High-resolution grain size distribution of sediment core with hyperspectral imaging","volume":"393","author":"Jacq","year":"2019","journal-title":"Sediment. Geol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"758","DOI":"10.2136\/sssaj2017.02.0066","article-title":"Complete Soil Texture is Accurately Predicted by Visible Near-Infrared Spectroscopy","volume":"81","author":"Hermansen","year":"2017","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.saa.2017.10.052","article-title":"Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters","volume":"191","author":"Poppi","year":"2018","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ru, C., Li, Z., and Tang, R. (2019). A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI). Sensors, 19.","DOI":"10.3390\/s19092045"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10933-009-9325-8","article-title":"Do spectrally inferred determinations of chlorophyll a reflect trends in lake trophic status?","volume":"43","author":"Michelutti","year":"2009","journal-title":"J. Paleolimnol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"157151","DOI":"10.1109\/ACCESS.2020.3019825","article-title":"Study on Characteristic Spectrum and Multiple Classifier Fusion with Different Particle Size in Marine Sediments","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Angelopoulou, T., Balafoutis, A., Zalidis, G., and Bochtis, D. (2020). From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation\u2014A Review. Sustainability, 12.","DOI":"10.3390\/su12020443"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1590\/0102-695X20142413387","article-title":"Rapid identification of three varieties of Chrysanthemum with near infrared spectroscopy","volume":"24","author":"Chen","year":"2014","journal-title":"Rev. Bras. Farm."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.13031\/trans.58.10715","article-title":"Hydrologic and water quality models: Performance measures and evaluation criteria","volume":"58","author":"Moriasi","year":"2015","journal-title":"Trans. ASABE"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.quascirev.2015.12.022","article-title":"Postglacial environmental succession of Nettilling Lake (Baffin Island, Canadian Arctic) inferred from biogeochemical and microfossil proxies","volume":"147","author":"Narancic","year":"2016","journal-title":"Quat. Sci. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1306\/74D70646-2B21-11D7-8648000102C1865D","article-title":"Brazos River bar [Texas]; a study in the significance of grain size parameters","volume":"27","author":"Folk","year":"1957","journal-title":"J. Sediment. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1002\/esp.261","article-title":"GRADISTAT: A grain size distribution and statistics package for the analysis of unconsolidated sediments","volume":"26","author":"Blott","year":"2001","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lamoureux, S.F., and Bollmann, J. (2005). Image acquisition. Image Analysis, Sediments and Paleoenvironments, Springer.","DOI":"10.1007\/1-4020-2122-4_2"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2006.03.002","article-title":"Reflectance quantities in optical remote sensing\u2014Definitions and case studies","volume":"103","author":"Schaepman","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rasti, B., Scheunders, P., Ghamisi, P., Licciardi, G., and Chanussot, J. (2018). Noise Reduction in Hyperspectral Imagery: Overview and Application. Remote Sens., 10.","DOI":"10.3390\/rs10030482"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Karami, A., Heylen, R., and Scheunders, P. (2014, January 24\u201327). Hyperspectral image noise reduction and its effect on spectral unmixing. Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077632"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2018.2865197","article-title":"Hyperspectral Image Denoising Employing a Spatial\u2013Spectral Deep Residual Convolutional Neural Network","volume":"57","author":"Yuan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1016\/j.snb.2018.11.034","article-title":"Automatic de-noising of close-range hyperspectral images with a wavelength-specific shearlet-based image noise reduction method","volume":"281","author":"Mishra","year":"2019","journal-title":"Sens. Actuators B Chem."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sonka, M., Hlavac, V., and Boyle, R. (1993). Image Processing, Analysis and Machine Vision, Chapman & Hall.","DOI":"10.1007\/978-1-4899-3216-7"},{"key":"ref_34","first-page":"176","article-title":"Normalization techniques for gas sensor array as applied to classification for black tea","volume":"2","author":"Tudu","year":"2009","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.chemolab.2012.05.009","article-title":"Pre-processing of hyperspectral images. Essential steps before image analysis","volume":"117","author":"Vidal","year":"2012","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2018.2890023","article-title":"Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art","volume":"7","author":"Ghamisi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_38","first-page":"e2985","article-title":"Hyperspectral image analysis. When space meets Chemistry","volume":"32","year":"2017","journal-title":"J. Chemom."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/TPAMI.2010.147","article-title":"SIFT Flow: Dense Correspondence across Scenes and Its Applications","volume":"33","author":"Liu","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Koz, A., Caliskan, A., and Alatan, A.A. (2016, January 21\u201324). Registration of MWIR-LWIR band hyperspectral images. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071708"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., and Zhang, Y. (2018). Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071119"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3224","DOI":"10.1109\/JSTARS.2015.2403257","article-title":"A Comprehensive Evaluation of Spectral Distance Functions and Metrics for Hyperspectral Image Processing","volume":"8","author":"Deborah","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","first-page":"243","article-title":"Random Forests: Finding Quasars","volume":"45","author":"Breiman","year":"2006","journal-title":"Stat. Chall. Astron."},{"key":"ref_44","unstructured":"Klusowski, J.M. (2018). Complete analysis of a random forest model. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","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":"Xiaobo","year":"2010","journal-title":"Anal. Chim. Acta"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2019). Feature Engineering and Selection, CRC Press.","DOI":"10.1201\/9781315108230"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1109\/LGRS.2017.2745049","article-title":"A Systematic Approach for Variable Selection with Random Forests: Achieving Stable Variable Importance Values","volume":"14","author":"Behnamian","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2019.05.028","article-title":"A comparison of random forest variable selection methods for classification prediction modeling","volume":"134","author":"Speiser","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.jfoodeng.2015.08.023","article-title":"Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning","volume":"170","author":"Kamruzzaman","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1080\/01431161.2019.1685721","article-title":"Development of deep learning method for lead content prediction of lettuce leaf using hyperspectral images","volume":"41","author":"Zhou","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s10666-016-9523-5","article-title":"Aggregated Versus Individual Land-Use Models: Modeling Spatial Autocorrelation to Increase Predictive Accuracy","volume":"22","author":"Ay","year":"2016","journal-title":"Environ. Model. Assess."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.catena.2016.12.014","article-title":"Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques","volume":"151","author":"Nawar","year":"2017","journal-title":"Catena"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.scitotenv.2018.08.442","article-title":"Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy","volume":"650","author":"Zhang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_56","first-page":"55","article-title":"Analysis of spectral absorption features in hyperspectral imagery","volume":"5","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_57","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":"Rossel","year":"2010","journal-title":"Geoderma"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.3390\/rs6042699","article-title":"Estimating Soil Organic Carbon Using VIS\/NIR Spectroscopy with SVMR and SPA Methods","volume":"6","author":"Peng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"218","DOI":"10.17221\/113\/2015-SWR","article-title":"Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features","volume":"10","author":"Gholizadeh","year":"2016","journal-title":"Soil Water Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3850\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:36:40Z","timestamp":1760179000000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3850"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,24]]},"references-count":59,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233850"],"URL":"https:\/\/doi.org\/10.3390\/rs12233850","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,11,24]]}}}