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Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites

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  • 1.

    Jiang, X., Lu, W. X., Zhao, H. Q., Yang, Q. C. & Yang, Z. P. Potential ecological risk assessment and prediction of soil heavy-metals pollution around coal gangue dump. Nat. Hazard. Earth Syst. 2, 1977–2010 (2014).

    Google Scholar 

  • 2.

    Wang, Q. & Li, R. Decline in China’s coal consumption: An evidence of peak coal or a temporary blip?. Energ. Policy 108, 696–701 (2017).

    Article 

    Google Scholar 

  • 3.

    Li, W. et al. Addressing the Co2 emissions of the world’s largest coal producer and consumer: Lessons from the Haishiwan coalfield, China. Energy 80, 400–413 (2015).

    Article 

    Google Scholar 

  • 4.

    Luo, P. et al. Water quality trend assessment in Jakarta: A rapidly growing Asian megacity. Plos One 14, e219009 (2019).

    Google Scholar 

  • 5.

    Luo, P. et al. Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions. Sci. Rep. Uk. 8, 12623 (2018).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 6.

    Guo, B. et al. Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (Copd) mortality using geographically and temporally weighted regression model across Xi’an During 2014–2016. Sci. Total Environ. 756, 143869 (2021).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 7.

    Pei, L., Wang, X., Guo, B., Guo, H. & Yu, Y. Do air pollutants as well as meteorological factors impact corona virus disease 2019 (Covid-19)? Evidence from China based on the geographical perspective. Environ. Sci. Pollut. R. 28, 35584–35596 (2021).

    CAS 
    Article 

    Google Scholar 

  • 8.

    Chen, T., Chang, Q., Liu, J., Clevers, J. G. P. W. & Kooistra, L. Identification of soil heavy metals sources and improvement in spatial mapping based on soil spectral information: A Case Study in Northwest China. Sci. Total Environ. 565, 155–164 (2016).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 9.

    Li, Z., Ma, Z., Kuijp, T. J. V. D., Yuan, Z. & Huang, L. A review of soil heavy metals pollution from mines in China: Pollution and health risk assessment. Sci. Total Environ. 468, 843–853 (2014).

    ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 10.

    Wang, L. et al. A comprehensive mitigation strategy for heavy metals contamination of farmland around mining areas—screening of low accumulated cultivars, soil remediation and risk assessment. Environ. Pollut. 245, 820–828 (2019).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 11.

    Siddiqui, A. U., Jain, M. K. & Masto, R. E. Pollution evaluation, spatial distribution, and source apportionment of trace metals around coal mines soil: The Case Study of Eastern India. Environ. Sci. Pollut. R. 27, 10822–10834 (2020).

    Article 
    CAS 

    Google Scholar 

  • 12.

    Guo, D., Bai, Z., Shangguan, T., Shao, H. & Qiu, W. Impacts of coal mining on the aboveground vegetation and soil quality: A case study of Qinxin Coal Mine in Shanxi Province, China. Clean Soil Air Water. 39, 219–225 (2011).

    CAS 
    Article 

    Google Scholar 

  • 13.

    Woodworth, M. D. Frontier Boomtown Urbanism in Ordos, Inner Mongolia Autonomous Region. Cross Curr. East Asian Hist. Cult. Rev. 1, 74–101 (2012).

    Google Scholar 

  • 14.

    Zeng, X., Liu, Z., He, C., Ma, Q. & Wu, J. Quantifying Surface coal-mining patterns to promote regional sustainability in Ordos, Inner Mongolia. Sustain. Basel. 10, 1135 (2018).

    Article 

    Google Scholar 

  • 15.

    Bu, Q. et al. Concentrations, spatial distributions, and sources of heavy metals in surface soils of the Coal Mining City Wuhai, China. J. Chem. Ny. 2020, 1–10 (2020).

    Article 
    CAS 

    Google Scholar 

  • 16.

    Hou, L., Li, X. & Li, F. Hyperspectral-based inversion of heavy metals content in the soil of coal mining areas. J. Environ. Qual. 48, 57–63 (2019).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 17.

    Liu, X., Bai, Z., Zhou, W., Cao, Y. & Zhang, G. Changes in soil properties in the soil profile after mining and reclamation in an opencast coal mine on the loess plateau, China. Ecol. Eng. 98, 228–239 (2017).

    Article 

    Google Scholar 

  • 18.

    Liu, X., Shi, H., Bai, Z., Zhou, W. & He, Y. Heavy metals concentrations of soils near the large opencast coal mine pits in China. Chemosphere. 244, 125360 (2019).

    ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 19.

    Gabriel, et al. Amending potential of organic and industrial by-products applied to heavy metals-rich mining soils. Ecotox. Environ. Safe. 162, 581–590 (2018).

    Article 
    CAS 

    Google Scholar 

  • 20.

    Zhai, X. et al. Remediation of multiple heavy metals-contaminated soil through the combination of soil washing and in situ immobilization. Sci. Total Environ. 635, 92–99 (2018).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 21.

    Wang, F., Gao, J. & Zha, Y. Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges. ISPRS J. Photogramm. 136, 73–84 (2018).

    Article 

    Google Scholar 

  • 22.

    Shi, T., Chen, Y., Liu, Y. & Wu, G. Visible and near-infrared reflectance spectroscopy—an alternative for monitoring soil contamination by heavy metals. J. Hazard. Mater. 265, 166–176 (2014).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 23.

    Zou, B., Jiang, X., Feng, H., Tu, Y. & Tao, C. Multisource spectral-integrated estimation of cadmium concentrations in soil using a direct standardization and spiking algorithm. Sci. Total Environ. 701, 134890 (2020).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 24.

    Guan, Q. et al. Source apportionment of heavy metals in agricultural soil based on Pmf: A case study in Hexi Corridor, Northwest China. Chemosphere 193, 189–197 (2017).

    ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 

  • 25.

    Horta, A. et al. Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review. Geoderma 241, 180–209 (2015).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 26.

    Saqib, et al. Efficiency and surface characterization of different plant derived biochar for cadmium (Cd) mobility, bioaccessibility and bioavailability to Chinese cabbage in highly contaminated soil. Chemosphere 211, 632–639 (2018).

    Article 
    CAS 

    Google Scholar 

  • 27.

    Wei, L. et al. An improved gradient boosting regression tree estimation model for soil heavy metals (arsenic) pollution monitoring using hyperspectral remote sensing. Appl. Sci. Basel. 9, 1943 (2019).

    CAS 
    Article 

    Google Scholar 

  • 28.

    Ngole-Jeme, V. M. Heavy metals in soils along unpaved roads in south west Cameroon: Contamination levels and health risks. Ambio 3, 374–386 (2016).

    Article 
    CAS 

    Google Scholar 

  • 29.

    Huang, Y. et al. Heavy metals pollution and health risk assessment of agricultural soils in a typical Peri-Urban area in Southeast China. J. Environ. Manage. 207, 159–168 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 30.

    Bruce, P. et al. Low-level lead exposure and mortality in Us adults: A population-based cohort study. Lancet Public Health. 3, 177–184 (2018).

    Article 

    Google Scholar 

  • 31.

    Harari, F. et al. Blood lead levels and decreased kidney function in a population-based cohort. Am. J. Kidney Dis. 72, 381–389 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 32.

    Sun, W., Zhang, X., Sun, X., Sun, Y. & Cen, Y. Predicting Nickel concentration in soil using reflectance spectroscopy associated with organic matter and clay minerals. Geoderma 327, 25–35 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 33.

    Guan, Q. et al. Prediction of heavy metals in soils of an arid area based on multi-spectral data. J. Environ. Manag. 243, 137–143 (2019).

    CAS 
    Article 

    Google Scholar 

  • 34.

    Lin, X. et al. Geographically weighted regression effects on soil zinc content hyperspectral modeling by applying the fractional-order differential. Remote Sens. Basel. 11, 636 (2019).

    ADS 
    Article 

    Google Scholar 

  • 35.

    Leenaers, H., Okx, J. P. & Burrough, P. A. Employing elevation data for efficient mapping of soil pollution on floodplains. Soil Use Manag. 6, 105–114 (2010).

    Article 

    Google Scholar 

  • 36.

    De Jesus, A., Zmozinski, A. V., Damin, I. C. F., Silva, M. M. & Vale, M. G. R. Determination of arsenic and cadmium in crude oil by direct sampling graphite furnace atomic absorption spectrometry. Spectrochim. Acta B 71, 86–91 (2012).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 37.

    Zhang, X., Sun, W., Cen, Y., Zhang, L. & Wang, N. Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy. Sci. Total Environ. 650, 321–334 (2019).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 38.

    Harun, C., Mursit, T. M. & Esen, C. Simultaneous preconcentration and determination of Ni and Pb in water samples by solid-phase extraction and flame atomic absorption spectrometry. J. Aoac Int. 96, 875–879 (2013).

    Article 
    CAS 

    Google Scholar 

  • 39.

    Gholizadeh, A., Saberioon, M., Ben-Dor, E. & Borůvka, L. Monitoring of selected soil contaminants using proximal and remote sensing techniques: background, state-of-the-art and future perspectives. Crit. Rev. Env. Sci. Technol. 48, 243–278 (2018).

    CAS 
    Article 

    Google Scholar 

  • 40.

    Wei, L., Yuan, Z., Yu, M., Huang, C. & Cao, L. Estimation of arsenic content in soil based on laboratory and field reflectance spectroscopy. Sensors-Basel. 19, 3904 (2019).

    ADS 
    CAS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 

  • 41.

    Chen, T., Chang, Q., Clevers, J. G. P. W. & Kooistra, L. Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy. Environ. Pollut. 206, 217–226 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 42.

    Liu, G. et al. Partitioning and geochemical fractions of heavy metals from geogenic and anthropogenic sources in various soil particle size fractions. Geoderma 312, 104–113 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 43.

    Meng, X. et al. Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. Int. J. Appl. Earth Obs. 89, 102111 (2020).

    Article 

    Google Scholar 

  • 44.

    Hong, Y. et al. Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm. Geoderma. 365, 114228 (2020).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 45.

    Hong, Y. et al. Estimating lead and zinc concentrations in Peri-Urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest. Sci. Total Environ. 651, 1969–1982 (2019).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 46.

    Wang, J. et al. Prediction of low heavy metals concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy. Geoderma 216, 1–9 (2014).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 47.

    Jiang, Q., Liu, M., Wang, J. & Liu, F. Feasibility of using visible and near-infrared reflectance spectroscopy to monitor heavy metals contaminants in Urban lake sediment. CATENA 162, 72–79 (2018).

    CAS 
    Article 

    Google Scholar 

  • 48.

    Khosravi, V., Doulati Ardejani, F., Yousefi, S. & Aryafar, A. Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods. Geoderma 318, 29–41 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 49.

    Cheng, H. et al. Estimating heavy metals concentrations in suburban soils with reflectance spectroscopy. Geoderma 336, 59–67 (2019).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 50.

    Zhang, S. et al. Hyperspectral inversion of heavy metals content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods. Spectrochim. Acta A 211, 393–400 (2019).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 51.

    Gholizadeh, A., Saberioon, M., Carmon, N., Boruvka, L. & Ben-Dor, E. Examining the performance of Paracuda-Ii data-mining engine versus selected techniques to model soil carbon from reflectance spectra. Remote Sens.-Basel. 10, 1172 (2018).

    ADS 
    Article 

    Google Scholar 

  • 52.

    Tian, S. et al. Hyperspectral prediction model of metals content in soil based on the genetic ant colony algorithm. Sustainability-Basel. 11, 3197 (2019).

    CAS 
    Article 

    Google Scholar 

  • 53.

    Xu, S., Zhao, Y., Wang, M. & Shi, X. Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–Nir spectroscopy. Geoderma 310, 29–43 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 54.

    Tao, C. et al. A transferable spectroscopic diagnosis model for predicting arsenic contamination in soil. Sci. Total Environ. 669, 964–972 (2019).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 55.

    Lu, Q. et al. Rapid inversion of heavy metals concentration in Karst grain producing areas based on hyperspectral bands associated with soil components. Microchem. J. 148, 404–411 (2019).

    CAS 
    Article 

    Google Scholar 

  • 56.

    Tan, K. et al. Estimation of the spatial distribution of heavy metals in agricultural soils using airborne hyperspectral imaging and random forest. J. Hazard. Mater. 382, 120987 (2020).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 57.

    Chen, S. et al. Fine resolution map of top- and subsoil carbon sequestration potential in France. Sci. Total Environ. 630, 389–400 (2018).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 58.

    Tan, K. et al. Estimating the distribution trend of soil heavy metals in mining area from hymap airborne hyperspectral imagery based on ensemble learning. J. Hazard. Mater. 401, 123288 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 59.

    Mao, X., Meng, J. & Xiang, Y. Cellular automata-based model for developing land use ecological security patterns in semi-arid areas: A case study of Ordos, Inner Mongolia, China. Environ. Earth Sci. 70, 269–279 (2013).

    Article 

    Google Scholar 

  • 60.

    Ramirez-Lopez, L. et al. Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma 226, 140–150 (2014).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 61.

    Liu, W., Zhao, J., Ouyang, Z., Söderlund, L. & Liu, G. Impacts of sewage irrigation on heavy metals distribution and contamination in Beijing, China. Environ. Int. 31, 805–812 (2005).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 62.

    Keshavarzi, A. & Kumar, V. Ecological risk assessment and source apportionment of heavy metals contamination in agricultural soils of Northeastern Iran. Int. J. Environ. Heal. R. 29, 544–560 (2018).

    Article 
    CAS 

    Google Scholar 

  • 63.

    Salminen, R. et al. Geochemical mapping field manual, Espoo, Finland Geological Survey of Finland. Geol. Surv. Den. Greenl. 38, 1–20 (1998).

    Google Scholar 

  • 64.

    Sun, W., Skidmore, A. K., Wang, T. & Zhang, X. Heavy metals pollution at mine sites estimated from reflectance spectroscopy following correction for skewed data. Environ. Pollut. 252, 1117–1124 (2019).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 65.

    Guo, B. et al. Ecological risk evaluation and source apportionment of heavy metals in park playgrounds: A case study in Xi’an, Shaanxi Province, a Northwest City of China. Environ. Sci. Pollut. R. 27, 24400–24412 (2020).

    CAS 
    Article 

    Google Scholar 

  • 66.

    Guo, B. et al. Contamination, Distribution and health riskassessment of risk elements in topsoil foramusement parks in Xi’an, China. Pol. J. Environ. Stud. 30, 601–617 (2021).

    CAS 
    Article 

    Google Scholar 

  • 67.

    Hong, Y. et al. Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest. Soil Tillage Res. 199, 104589 (2020).

    Article 

    Google Scholar 

  • 68.

    Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).

    Article 

    Google Scholar 

  • 69.

    Rudnicki, W. R., Wrzesien, M. & Paja, W. All relevant feature selection methods and applications. Comput. Intell.Us. 584, 11–28 (2015).

    MathSciNet 

    Google Scholar 

  • 70.

    Liu, Z. et al. Estimation of soil heavy metals content using hyperspectral data. Remote Sens. Basel. 11, 1464 (2019).

    ADS 
    Article 

    Google Scholar 

  • 71.

    Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B. & Roger, J. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by Nir spectroscopy. Trac. Trend. Anal. Chem. 29, 1073–1081 (2010).

    CAS 
    Article 

    Google Scholar 

  • 72.

    Wold, S., Martens, H. & Wold, H. The multivariate calibration problem in chemistry solved by the PLS method. Lect. Notes Math. 973, 286–293 (1983).

    MATH 
    Article 

    Google Scholar 

  • 73.

    Shi, T., Wang, J., Chen, Y. & Wu, G. Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants. Int. J. Appl. Earth Obs. 52, 95–103 (2016).

    Article 

    Google Scholar 

  • 74.

    Dotto, A. C., Dalmolin, R. S. D., Caten, A. T. & Grunwald, S. 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, 262–274 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 75.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    MATH 
    Article 

    Google Scholar 

  • 76.

    Douglas, R. K. et al. Evaluation of Vis–Nir reflectance spectroscopy sensitivity to weathering for enhanced assessment of oil contaminated soils. Sci. Total Environ. 626, 1108–1120 (2018).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 77.

    Guo, B. et al. Estimating socio-economic parameters via machine learning methods using Luojia1-01 Nighttime Light remotely sensed images at multiple scales of China in 2018. IEEE Access. 9, 34352–34365 (2021).

    Article 

    Google Scholar 

  • 78.

    Tan, K., Ma, W., Wu, F. & Du, Q. Random forest-based estimation of heavy metals concentration in agricultural soils with hyperspectral sensor data. Environ. Monit. Assess. 191, 446 (2019).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 79.

    Guo, B. et al. Estimating Pm2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017. Sci. Total Environ. 778, 146288 (2021).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 80.

    Ou, D. et al. Semi-supervised Dnn regression on airborne hyperspectral imagery for improved spatial soil properties prediction. Geoderma. 385, 114875 (2021).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 81.

    Gholizadeh, A., Žižala, D., Saberioon, M. & Borůvka, L. Soil organic carbon and texture retrieving and mapping using proximal, airborne and sentinel-2 spectral imaging. Remote Sens. Environ. 218, 89–103 (2018).

    ADS 
    Article 

    Google Scholar 

  • 82.

    Guo, B. et al. Identifying the spatiotemporal dynamic of Pm 2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Sci. Total Environ. 751, 141765 (2021).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 83.

    Guo, B. et al. Detecting spatiotemporal dynamic of regional electric consumption using Npp–Viirs Nighttime stable light data—a Case Study of Xi’an, China. IEEE Access 8, 171694–171702 (2020).

    Article 

    Google Scholar 

  • 84.

    Guo, B. et al. A land use regression application into simulating spatial distribution characteristics of particulate matter (Pm2.5) concentration in city of Xi’an, China. Pol. J. Environ. Stud. 29, 4065–4076 (2020).

    Article 

    Google Scholar 

  • 85.

    Malley, D. F. & Williams, P. C. Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environ. Sci. Technol. 31, 3461–3467 (1997).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 86.

    Pyo, J., Hong, S. M., Kwon, Y. S., Kim, M. S. & Cho, K. H. Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil. Sci. Total Environ. 741, 140162 (2020).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 87.

    Sun, W. & Zhang, X. Estimating soil zinc concentrations using reflectance spectroscopy. Int. J. Appl. Earth Obs. 58, 126–133 (2017).

    Article 

    Google Scholar 

  • 88.

    Chao, T. et al. A transferable spectroscopic diagnosis model for predicting arsenic contamination in soil. Sci. Total Environ. 669, 964–972 (2019).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 89.

    Rossel, R. A. V., Walvoort, D. J. J., McBratney, A. B., Janik, L. J. & Skjemstad, J. O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75 (2005).

    Article 
    CAS 

    Google Scholar 

  • 90.

    Rossel, R. A. V. et al. A global spectral library to characterize the world’s soil. Earth Sci. Rev. 155, 198–230 (2016).

    ADS 
    Article 

    Google Scholar 

  • 91.

    Chakraborty, S. et al. Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils. Sci. Total Environ. 514, 399–408 (2015).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 92.

    Boker, A., Brownell, L. & Donen, N. The Amsterdam preoperative anxiety and information scale provides a simple and reliable measure of preoperative anxiety. Can. J. Anesth. 49, 792–798 (2002).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 93.

    Piñeiro, G., Perelman, S., Guerschman, J. P. & Paruelo, J. M. How to evaluate models: Observed vs. predicted or predicted vs. observed?. Ecol. Model. 216, 316–322 (2008).

    Article 

    Google Scholar 

  • 94.

    Douglas, R. K., Nawar, S., Alamar, M. C., Mouazen, A. M. & Coulon, F. Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using Vis–Nir spectroscopy and regression techniques. Sci. Total Environ. 616, 147–155 (2018).

    ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 95.

    Ji, W., Rossel, R. A. V. & Shi, Z. Accounting for the effects of water and the environment on proximally sensed Vis–Nir soil spectra and their calibrations. Eur. J. Soil Sci. 66, 555–565 (2015).

    Article 

    Google Scholar 

  • 96.

    Altunkaynak, A. & Ozger, M. Comparison of discrete and continuous wavelet—Multilayer perceptron methods for daily precipitation prediction. J. Hydrol. Eng. 21, 04016014 (2016).

    Article 

    Google Scholar 

  • 97.

    Buddenbaum, H., Steffens, M. & Rossel, R. V. The effects of spectral pretreatments on chemometric analyses of soil profiles using laboratory imaging spectroscopy. Appl. Environ. Soil Sci. 2012, 1–12 (2012).

    Article 
    CAS 

    Google Scholar 

  • 98.

    Nawar, S., Buddenbaum, H., Hill, J., Kozak, J. & Mouazen, A. M. Estimating the soil clay content and organic matter by means of different calibration methods of Vis–Nir diffuse reflectance spectroscopy. Soil Till. Res. 155, 510–522 (2016).

    Article 

    Google Scholar 

  • 99.

    Kuang, B. & Mouazen, A. M. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms. Eur. J. Soil Sci. 62, 629–636 (2011).

    CAS 
    Article 

    Google Scholar 

  • 100.

    Sipos, P., Németh, T., Kis, V. K. & Mohai, I. Association of individual soil mineral constituents and heavy metals as studied by sorption experiments and analytical electron microscopy analyses. J. Hazard. Mater. 168, 1512–1520 (2009).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 101.

    Rossel, R. A. V. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54 (2010).

    ADS 
    CAS 
    Article 

    Google Scholar 

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