Evaluation of Vis-NIR preprocessing combined with PLS regression for estimation soil organic carbon, cation exchange capacity and clay from eastern Croatia
Tài liệu tham khảo
Ahmadi, 2021, Soil properties prediction for precision agriculture using visible and near-infrared spectroscopy: a systematic review and meta analysis, Agronomy, 11, 433, 10.3390/agronomy11030433
Andersen, 2010, Variable selection in regression — a tutorial, J. Chemother., 24, 728
Asgari, 2020, Incorporating environmental variables, remote and proximal sensing data for digital soil mapping of USDA soil great groups, Int, J Remote Sen, 41, 7624, 10.1080/01431161.2020.1763506
Asgari, 2020, Carbonates and organic matter in soils characterized by reflected energy from 350–25 000 nm wavelengths, JMt Sci, 17, 1636
Avery, 1980
Bačani, 1999, Quaternary deposits as the hydrogeological system of eastern Slavonia, Geologica Croatica, 52, 141
Barnes, 1989, Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra, Appl. Spectrosc., 43, 772, 10.1366/0003702894202201
Bartholomeus, 2008, Spectral reflectance based indices for soil organic carbon quantification, Geoderma, 145, 28, 10.1016/j.geoderma.2008.01.010
Baumgardner, 1970, Effects of organic matter on the multispectral properties of soils, Proc Indiana Acad Sci, 79, 413
Ben-Dor, 1995, Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties, Soil Sci. Soc. Am. J., 59, 364, 10.2136/sssaj1995.03615995005900020014x
Ben-Dor, 1999, Soil reflectance, 111
Ben-Dor, 2008, A novel method of classifying soil profiles in the field using optical means, Soil Sci. Soc. Am. J., 72, 1113, 10.2136/sssaj2006.0059
Cambule, 2012, Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique, Geoderma, 183–184, 41, 10.1016/j.geoderma.2012.03.011
Chang, 2001, Near-infrared reflectance spectroscopy—principal components regression analyses of soil properties, Soil Sci. Soc. Am. J., 65, 480, 10.2136/sssaj2001.652480x
Chen, S.; Xu, D.; Li, S., 2020. Monitoring soil organic carbon in alpine soils using in situ Vis-NIR spectroscopy and a multilayer perceptron. Land Degrad. Dev. 31(8), 1026–1038. http://dx.doi.org/https://doi.org/10.1002/ldr.3497.
Chong, IG, Jun CH., 2005. Performance of some variable selection methods when multicollinearity is present. Chemom. Intell. Lab. Syst. 78 (1–2), 103–112. doi:https://doi.org/10.1016/j.chemolab.2004.12.011.
Clairotte, 2016, National calibraton of soil organic carbon concentration using diffuse infrared reflectance spectroscopy, Geoderma, 276, 41, 10.1016/j.geoderma.2016.04.021
Clark, 1999, Spectroscopy of rocks and minerals and principles of spectroscopy, 3
Clark, 1984, Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications, J. Geophys. Res. Solid Earth, 89, 6329, 10.1029/JB089iB07p06329
Clingensmith, 2019, Evaluation of calibration subsetting and new chemometric methods on the spectral prediction of a key soil properties in a data-limited environment Eur, J. Soil Sci., 70, 107, 10.1111/ejss.12753
Dalal, R. C., Henry, R. J. (1986). Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Sci. Soc. Am. J. 50 (1), 120–123 http://dx.doi.org/https://doi.org/10.2136/sssaj1986.03615995005000010023x.
Demattê José, 2017, Chemometric soil analysis on the determination of specific bands for the detection of magnesium and potassium by spectroscopy, Geoderma, 288, 8, 10.1016/j.geoderma.2016.11.013
Dotto, 2017, Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy, Soil Tillage Res., 172, 59, 10.1016/j.still.2017.05.008
Dotto, 2018, A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra, Geoderma, 314, 262, 10.1016/j.geoderma.2017.11.006
Duckworth, 2004, Mathematical data preprocessing, 115
Frank, 1993, A statistical view of some chemometrics regression tools, Technometrics, 35, 109, 10.1080/00401706.1993.10485033
Galović, 2016, Sedimentological and mineralogical characteristics of the Pleistocene loess/paleosol sections in the eastern Croatia, Aeolian Res., 20, 7, 10.1016/j.aeolia.2015.10.007
Galović, 2009, Loess chronostratigraphy in Eastern Croatia – a luminescence dating approach, Quat. Int., 198, 85, 10.1016/j.quaint.2008.02.004
Gao, 2014, Estimating soil organic carbon content with visible–near infrared (Vis–NIR) spectroscopy, Appl. Spectrosc., 68, 712, 10.1366/13-07031
Genot, 2011, Near infrared reflectance spectroscopy for estimating soil characteristics valuable in the diagnosis of soil fertility, J. Near Infrared Spectrosc., 19, 117, 10.1255/jnirs.923
Gholizadeh, 2013, Common chemometrics indicators for prediction of soil organic matter content and quality from soil spectra: Review and research perspectives
Gholizadeh, 2013, Visible, nearinfrared, and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: state-of-the-art and key issues, Appl. Spectrosc., 67, 1349, 10.1366/13-07288
Gholizadeh, 2016, A memory based learning approach as compared to other data mining algorithms for the prediction of soil texture using diffuse reflectance spectra, Remote Sens., 8, 341, 10.3390/rs8040341
Gomez, 2012, Regional prediction of eight common soil properties and their spatial structures from hyperspectral Vis-NIR data, Geoderma, 189-190, 176, 10.1016/j.geoderma.2012.05.023
Hećimović, 2009, Quaternary. Chenozoik, 95
Heil, 2021, An evaluation of different NIR spectral pre-treatments to derive the soil parameters C and N of a humus-clay-rich soil, Sensors., 21, 1423, 10.3390/s21041423
Hermansen, 2016, Visible near –infrared spectroscopy can predict the clay/organic carbon and mineral fines/organic carbon ratios, Soil Sci. Soc. Am. J., 80, 1486, 10.2136/sssaj2016.05.0159
Isaksson, 1988, The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy, Appl. Spectrosc., 42, 1273, 10.1366/0003702884429869
ISO, 1998
ISO, 2006
ISO, 2009
IUSS Working Group WRB, 2014
Jaconi, 2019, Near infrared spectroscopy as an easy and precise method to estimate soil texture, Geoderma, 337, 906, 10.1016/j.geoderma.2018.10.038
Jones, 2001, 79
Kawamura, 2017, Vis-NIR spectroscopy and PLS regression with waveband selection for estimating the total C and N of paddy soils in Madagascar, Remote Sens., 9, 1081, 10.3390/rs9101081
Lee, 2009, Wavelength identification and diffuse reflectance estimation for surface and profile soil properties, Trans. ASABE, 52, 683, 10.13031/2013.27385
Lee, 2012, Reproducibility, complementary measure of predict RMSE for robustness improvement of multivariate calibration models via variable selections, Anal. Chim. Acta, 757, 11, 10.1016/j.aca.2012.10.025
Levi N., Karnieli A., Paz–Kagan T., 2020. Using reflectance spectroscopy for detecting land-use effects on soil quality in drylands. Soil Tillage Res. 199, 104571 doi:https://doi.org/10.1016/j.still.2020.104571.
Malley, 2004, Application in analysis of soils, 729
Martens, 1999, A philosophy for sensory science, Food Qual. Prefer., 10, 233, 10.1016/S0950-3293(99)00024-5
Martens, 2000, Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR), Food Qual. Prefer., 11, 5, 10.1016/S0950-3293(99)00039-7
Martens, 1989
Martens, 1983, Multivariate linearity transformations for near infrared reflectance spectroscopy, 205
Mehmood, 2012, A review of variable selection methods in partial least squares regression, Chemom. Intell. Lab. Syst., 118, 62, 10.1016/j.chemolab.2012.07.010
Mehmood, 2020, Comparison of variable selection methods in partial least squares regression, J. Chemom., 34, 10.1002/cem.3226
Mouazen, 2006, Characterization of soil water content using measured visible and near infrared spectra, Soil Sci. Soc. Am. J., 70, 1295, 10.2136/sssaj2005.0297
Mouzaen, 2010, 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, Geoderma, 158, 23, 10.1016/j.geoderma.2010.03.001
Muñoz, 2011, Soil carbon mapping using on-the-go near infrared spectroscopy, topography and aerial photographs, Geoderma, 166, 102, 10.1016/j.geoderma.2011.07.017
Mutić, 1990, Korelacija kvartara istočne Slavonije na osnovi podataka mineraloško-petrografskih analiza, Acta Geologica, 20, 1
Naimi, 2022, Quantification of some intrinsic soil properties using proximal sensing in arid lands: application of vis-NIR, MIR and pXRF Spectrosc. Geoderma Regional, 28
Nawar, 2016, Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy, Soil Tillage Res., 155, 510, 10.1016/j.still.2015.07.021
Ng, 2019, Optimizing wavelength selection by using informative vectors for parsimonius infrared spectra modelling, Comput. Electron. Agric., 158, 201, 10.1016/j.compag.2019.02.003
Peng, 2014, Estimating soil organic carbon using VIS/NIR spectroscopy with SVMR and SPA methods, Remote Sens., 6, 2699, 10.3390/rs6042699
Pinheiro, 2017, Prediction of soil physical and chemical properties by visible and near-infrared diffuse reflectance spectroscopy in the central Amazon, Remote Sens., 9, 293, 10.3390/rs9040293
Pirie, 2005, Ultra-violet, visible, near-infrared and mid-infrared diffuse reflectance spectroscopic techniques to predict several soil properties, Australian J. of Soil Res., 43, 713, 10.1071/SR04182
Rajahalti, 2009, Bomarker discovery in mass spectral profiles by means of selectivity ratio plot, Chemom. Intell. Lab. Syst., 95, 35, 10.1016/j.chemolab.2008.08.004
Rehman, 2019, Comparison of cation exchange capacity estimated from Vis-NIR spectral reflectance data and a pedotransfer function, Vadose Zone J., 18, 1, 10.2136/vzj2018.10.0192
Rinnan, 2009, Review of the most common pre-processing techniques for near-infrared spectra, TrAC Trends Anal Chem, 28, 1201, 10.1016/j.trac.2009.07.007
Rossel, 2006, Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties, Geoderma, 131, 59, 10.1016/j.geoderma.2005.03.007
Rubinić, 2018, Pseudogleyed loess derivates – the most common soil parent materials in the Pannonian region of Croatia, Quat. Int., 494, 248, 10.1016/j.quaint.2017.06.044
Sankey, 2008, Comparing local vs. global visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy (DRS) calibrations for the prediction of soil clay, organic C and inorganic C, Geoderma, 148, 149, 10.1016/j.geoderma.2008.09.019
Santos, 2020, Predicting carbon and nitrogen by visible near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy in soils of Northeast Brazil, Geoderma Reg, 23
Sarathjith, 2016, Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy, Curr. Sci., 110, 1031, 10.18520/cs/v110/i6/1031-1037
Savitzky, 1964, Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., 36, 1627, 10.1021/ac60214a047
Seema, 2020, Application of Vis-NIR spectroscopy for estimation of soil organic carbon using different spectral preprocessing techniques and multivariate methods in the middle Indo- Gangetic plains of India, Geoderma Reg, 23
Shahrayini, 2020, Prediction of soil properties by visible and near – infrared reflectance spectroscopy, Eur Soil Sci, 53, 1760, 10.1134/S1064229320120108
Sherman, 1985, Electronic spectra of Fe3+ oxides and oxyhydroxides in the near IR to near UV, Am. Mineral., 70, 1262
Shi, 2014, Development of a national VNIR soil –spectral library for soil classification and prediction of organic matter concentrations, Sci. China Earth Sci., 57, 1671, 10.1007/s11430-013-4808-x
Silva EB, Giasson E, Dotto AC, ten Caten A, Demattê JAM, Bacic ILZ, Veiga M. A., 2018. Regional legacy soil dataset for prediction of sand and clay content with VIS-NIR-SWIR, in Southern Brazil. Revista Brasileria de Ciencia do Solo 43, e0180174. http://dx.doi.org/https://doi.org/10.1590/18069657rbcs20180174.
Škorić, 1977
Soriano-Disla, 2013, The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties, Appl. Spectrosc. Rev., 49, 139, 10.1080/05704928.2013.811081
Stenberg, 2010, Visible and near infrared spectroscopy in soil science, 107, 163, 10.1016/S0065-2113(10)07005-7
Stevens, 2013, Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy, PLoS One, 8, 10.1371/journal.pone.0066409
Stoner, 1981, Characteristic variation in reflectance of surface soils, Soil Sci. Soc. Am. J., 45, 1161, 10.2136/sssaj1981.03615995004500060031x
Sudduth, 2010, VNIR spectroscopy estimates of within-field variability in soil properties
Tavares, 2021, Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches, Sensors, 21, 148, 10.3390/s21010148
Van Groenigen, 2003, NIR and DRIFT-MIR spectrometry of soils for predicting soil and crop parameters in a flooded field, Plant Soil, 250, 155, 10.1023/A:1022893520315
Vasques, 2008, Comparison of multivariate methods for inferential modelling of soil carbon using visible-near infrared spectra, Geoderma, 146, 14, 10.1016/j.geoderma.2008.04.007
Velić, 2009
Viscarra Rossel, 2010, Using data mining to model and interpret soil diffuse reflectance spectra, Geoderma, 158, 46, 10.1016/j.geoderma.2009.12.025
Viscarra Rossel, 2012, Predicting soil properties from the Australian soilvisible-near infrared spectroscopic database, Eur. J. Soil Sci., 63, 848, 10.1111/j.1365-2389.2012.01495.x
Viscarra Rossel, 2006, Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties, Geoderma, 131, 59, 10.1016/j.geoderma.2005.03.007
Viscarra Rossel, 2008, Using a legacy soil sample to develop a mid-IR spectral library, Australian J. Soil Res., 46, 1, 10.1071/SR07099
Viscarra Rossel, 2016, A global spectral library to characterize the world's soil, Earth Sci. Rev., 155, 198, 10.1016/j.earscirev.2016.01.012
Vohland, 2014, Determination of soil properties with visible to near- and mid-infrared spectroscopy: effects of spectral variable selection, Geoderma, 223-225, 88, 10.1016/j.geoderma.2014.01.013
Vohland, 2016, Using variable selection and wavelets to exploit the full potential of visible–near infrared spectra for predicting soil properties, J Near-Infrared Spec, 24, 255, 10.1255/jnirs.1233
Wetterlind, 2013, Soil analysis using visible and near infrared spectroscopy, Methods Mol. Biol., 953, 95, 10.1007/978-1-62703-152-3_6
Wiklund, 2007, A randomization test for PLS component selection, J. Chemom., 21, 427, 10.1002/cem.1086
Williams, 1986, Attempts at standardization of hardness testing of wheat. II. The near infrared method, Cereal Foods World, 31, 417
Wold, 1975, Soft modelling by latent variables: “The partial least squares approach”
Wold, 2001, PLS-regression: a basic tool of chemometrics, Chemom. Intell. Lab. Syst., 58, 109, 10.1016/S0169-7439(01)00155-1
Xie, 2011, Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in southwestern Ontario, Canada Canadian J Soil Sci, 91, 53, 10.4141/cjss10029
Xu, 2016, Effects of subsetting by parent materials on prediction of soil organic matter content in a hilly area using vis–NIR spectroscopy, PLoS One, 11, 10.1371/journal.pone.0151536
Xu, 2018, Assessment of important soil properties related to Chinese soil taxonomy based on vis NIR reflectance spectroscopy, Comput. Electron. Agric., 144, 1, 10.1016/j.compag.2017.11.029
Xu, 2020, Estimation of organic carbon in anthropogenic soil by VIS-NIR spectroscopy: effect of variable selection, Remote Sens., 12, 3394, 10.3390/rs12203394
Zhao, 2021, Predicting soil physical and chemical properties using vis NIR in Australian cotton areas, Catena, 196, 10.1016/j.catena.2020.104938
