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Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments

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  • 1.

    Aryal, D. R., De Jong, B. H., Ochoa-Gaona, S., Esparza-Olguin, L. & Mendoza-Vega, J. Carbon stocks and changes in tropical secondary forests of southern Mexico. Agr. Ecosyst. Environ. 195, 220–230 (2014).

    Article 

    Google Scholar 

  • 2.

    Aryal, D. R., De Jong, B. H., Ochoa-Gaona, S., Mendoza-Vega, J. & Esparza-Olguin, L. Successional and seasonal variation in litterfall and associated nutrient transfer in semi-evergreen tropical forests of SE Mexico. Nutr. Cycl. Agroecosys. 103(1), 45–60 (2015).

    CAS 
    Article 

    Google Scholar 

  • 3.

    Aryal, D. R. et al. Soil organic carbon depletion from forests to grasslands conversion in Mexico: A review. Trop. Agric. 8, 181 (2018).

    CAS 

    Google Scholar 

  • 4.

    Gao, W., Yang, J., Ren, S. R. & Liu, H. L. The trend of soil organic carbon, total nitrogen, and wheat and maize productivity under different long-term fertilizations in the upland fluvo-aquic soil of North China. Nutr. Cycl. Agroecosys. 103, 61–73 (2015).

    CAS 
    Article 

    Google Scholar 

  • 5.

    Qi, H., Paz-Kagan, T., Karnieli, A., Jin, X. & Li, S. Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data. Soil Till Res. 175, 267–275 (2018).

    Article 

    Google Scholar 

  • 6.

    Dong, X., Tian, J., Zhang, R., He, D. & Chen, Q. Study on the relationship between soil emissivity spectra and content of soil element. Spectrosc. Spect. Anal. 37(02), 557–564 (2017).

    CAS 

    Google Scholar 

  • 7.

    Kemper, T. & Sommer, S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ. Sci. Technol. 36(12), 2742–2747 (2002).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 8.

    Panigrahi, N. & Das, B. S. Canopy spectral reflectance as a predictor of soil water potential in rice. Water Resour. Res. 54(4), 2544–2560 (2018).

    ADS 
    Article 

    Google Scholar 

  • 9.

    Peddle, D. R., White, H. P., Soffer, R. J., Miller, J. R. & LeDrew, E. F. Reflectance processing of remote sensing spectroradiometer data. Comput. Geoences. 27(2), 203–213 (2001).

    ADS 

    Google Scholar 

  • 10.

    Ben-Dor, E. et al. Using imaging spectroscopy to study soil properties. Remote Sens. Environ. 113, S38–S55 (2009).

    Article 

    Google Scholar 

  • 11.

    Rossel, R. A., Walvoort, D. 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(1), 59–75 (2006).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 12.

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

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 13.

    Ding, J., Yang, A., Wang, J., Sagan, V. & Yu, D. Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy. PeerJ 6(3), e5714 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 14.

    Gobrecht, A., Bendoula, R., Roger, J.-M. & Bellon-Maurel, V. A new optical method coupling light polarization and vis–NIR spectroscopy to improve the measurement of soil carbon content. Soil Till Res. 155, 461–470 (2016).

    Article 

    Google Scholar 

  • 15.

    Gu, X., Wang, Y., Song, X. & Xu, X. The Inversion Model of Soil Organic Matter of Cultivated Land Based on Hyperspectral Technology. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII (International Society for Optics and Photonics, 2015).

    Google Scholar 

  • 16.

    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 

  • 17.

    Yu, X., Liu, Q., Wang, Y., Liu, X. & Liu, X. Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. CATENA 137, 340–349 (2016).

    CAS 
    Article 

    Google Scholar 

  • 18.

    Ji, W. J., Li, X., Li, C. X., Zhou, Y. & Shi, Z. Using different data mining algorithes to predict soil organic matter based on visible-near infrared spectroscopy. Spectrosc. Spect. Anal. 32(09), 2393–2397 (2012).

    CAS 

    Google Scholar 

  • 19.

    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 

  • 20.

    Mouazen, A. M. & Al-Asadi, R. A. Influence of soil moisture content on assessment of bulk density with combined frequency domain reflectometry and visible and near infrared spectroscopy under semi field conditions. Soil Till Res. 176, 95–103 (2018).

    Article 

    Google Scholar 

  • 21.

    Rossel, R. A. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1), 46–54 (2010).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 22.

    Nawar, S. & Mouazen, A. M. Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors. 17(10), 2428 (2017).

    ADS 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 23.

    Wang, J., Chen, Y., Chen, F., Shi, T. & Wu, G. Wavelet-based coupling of leaf and canopy reflectance spectra to improve the estimation accuracy of foliar nitrogen concentration. Agr. Forest Meteorol. 248, 306–315 (2018).

    ADS 
    Article 

    Google Scholar 

  • 24.

    Hong, Y. et al. Combining fractional order derivative and spectral variable selection for organic matter estimation of homogeneous soil samples by vis–NIR spectroscopy. Remote Sens. 10(3), 479 (2018).

    ADS 
    Article 

    Google Scholar 

  • 25.

    Sorenson, P. T. et al. Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy. Can J Soil Sci. 97(2), 241–248 (2017).

    CAS 
    Article 

    Google Scholar 

  • 26.

    Gomez, C., Rossel, R. A. V. & Mcbratney, A. B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma 146(3–4), 403–411 (2008).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 27.

    Shi, T. Z. et al. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil. 366(1–2), 363–375 (2013).

    CAS 
    Article 

    Google Scholar 

  • 28.

    Stenberg, B., Rossel, R. A. V., Mouazen, A. M. & Wetterlind, J. Chapter five-visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215 (2010).

    CAS 
    Article 

    Google Scholar 

  • 29.

    Uddin, M. P., Mamun, M. A. & Hossain, M. A. PCA-based feature reduction for hyperspectral remote sensing image classification. IETE Tech. Rev. 5, 1–21 (2020).

    Google Scholar 

  • 30.

    Cambule, A. H., Rossiter, D. G., Stoorvogel, J. J. & Smaling, E. M. A. Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park Mozambique. Geoderma 183, 41–48 (2012).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 31.

    Kawamura, K. et al. Vis-NIR spectroscopy and PLS regression with waveband selection for estimating the total C and N of paddy soils in Madagascar. Remote Sens. 9(10), 1081 (2017).

    ADS 
    Article 

    Google Scholar 

  • 32.

    Leone, A. P., Viscarra-Rossel, R. A., Amenta, P. & Buondonno, A. Prediction of soil properties with PLSR and vis-NIR spectroscopy: Application to mediterranean soils from southern Italy. Curr. Anal. Chem. 8(2), 283–299 (2012).

    CAS 
    Article 

    Google Scholar 

  • 33.

    Wang, S., Chen, Y., Wang, M., Zhao, Y. & Li, J. 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(8), 967 (2019).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 34.

    Barnes, E. M. et al. Remote- and ground-based sensor techniques to map soil properties. Photogramm. Eng Rem S. 69(6), 619–630 (2003).

    Article 

    Google Scholar 

  • 35.

    Priori, S. et al. Field-scale mapping of soil carbon stock with limited sampling by coupling gamma-ray and vis-NIR spectroscopy. Soil Sci Soc Am J. 80(4), 954–964 (2016).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 36.

    Amin, I., Fikrat, F., Mammadov, E. & Babayev, M. Soil organic carbon prediction by vis-NIR spectroscopy: Case study the Kur-Aras plain Azerbaijan. Commun. Soil Sci. Plan. 51(6), 726–734 (2020).

    CAS 
    Article 

    Google Scholar 

  • 37.

    Yu, L. et al. Hyperspectral estimation of soil organic matter content based on partial least squares regression. Trans. CSAE. 31(14), 103–109 (2015).

    Google Scholar 

  • 38.

    Liu, Y. F., Lu, Y. N., Guo, L., Xiao, F. T. & Chen, Y. Y. Construction of calibration set based on the land use types in visible and near-infrared (VIS-NIR)model for soil organic matter estimation. Acta Pedol. Sin. 53, 332–341 (2016).

    Google Scholar 

  • 39.

    Zhou, X. M. & Zhang, T. Analysis of the April 2019 atmospheric circulation and weather. Meteor. Mon. 45(7), 1028–1036 (2019).

    Google Scholar 

  • 40.

    Guan, L. & Zhang, T. Analysis of the May 2019 atmospheric circulation and weather. Meteor. Mon. 45(8), 1181–1188 (2019).

    Google Scholar 

  • 41.

    Li, X., He, Y. & Wu, C. Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy. J. Stored Prod. Res. 44(3), 264–268 (2008).

    Article 

    Google Scholar 

  • 42.

    Boško, M. & Bensa, A. Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia. Eurasian J. Soil Sci. 6(4), 365–373 (2017).

    Google Scholar 

  • 43.

    McCarty, G. W., Reeves, J. B. III., Reeves, V. B., Follett, R. F. & Kimble, J. M. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci. Soc. Am. J. 66(2), 640–646 (2002).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 44.

    Gholizadeh, A. et al. Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Res. 10(4), 218–227 (2015).

    CAS 
    Article 

    Google Scholar 

  • 45.

    Wang, X., Xue, L., He, X. W. & Liu, M. H. Vitamin C content estimation of chilies using Vis/NIR spectroscopy. Int. Conf. Electr. Inf. Control Eng. 2011, 1894–1897 (2011).

    Google Scholar 

  • 46.

    Lee, K. S. et al. Wavelength identification and diffuse reflectance estimation for surface and profile soil properties. Am. Soc. Agric. Biol. Eng. 52(3), 683–695 (2009).

    CAS 

    Google Scholar 


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