References
- Araújo SR, Wetterlind J, Dematte JAM, Stenberg B. 2014. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques. Eur J Soil Sci. 65(5):718–729. doi:https://doi.org/10.1111/ejss.12165.
- Chang CW, Laird DA, Mausbach MJ, Hurburgh CR. 2001. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Sci Soc Am J. 65:480–490. doi:https://doi.org/10.2136/sssaj2001.652480x.
- Cheng H, Shen RL, Chen YY, Wan QJ, Shi TZ, Wang JJ, Wan Y, Hong YS, Li XC. 2019. Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy. Geoderma 336:59–67. doi:https://doi.org/10.1016/j.geoderma.2018.08.010.
- Dalal RC, Henry RJ. 1986. Simultaneous determination of moisture, organic carbon, and total nitrogen by near-infrared reflectance spectrophotometry. Soil Sci Soc Am J. 50:120–123. doi:https://doi.org/10.2136/sssaj1986.03615995005000010023x.
- Fijani E, Barzegar R, Deo R, Tziritis E, Skorda K. 2019. Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters. Sci Total Environ. 648:839–853. doi:https://doi.org/10.1016/j.scitotenv.2018.08.221.
- Fystro G. 2002. The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis-NIR spectroscopy and comparative methods. Plant Soil 246:139–149. doi:https://doi.org/10.1023/A:1020612319014.
- Guo L, Fu P, Shi TZ, Chen YY, Zhang HT, Meng R, Wang S. 2020. Mapping field-scale soil organic carbon with unmanned aircraft system-acquired time series multispectral images. Soil Tillage Res. 196. doi:https://doi.org/10.1016/j.still.2019.104477.
- Guo L, Zhao C, Zhang HT, Chen YY, Linderman M, Zhang Q, Liu YL. 2017. Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology. Geoderma 285:280–292. doi:https://doi.org/10.1016/j.geoderma.2016.10.010.
- Hong YS, Chen SC, Zhang Y, Chen YY, Yu L, Liu YF, Liu YL, Cheng H, Liu Y. 2018a. Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: effects of two-dimensional correlation coefficient and extreme learning machine. Sci Total Environ. 644:1232–1243. doi:https://doi.org/10.1016/j.scitotenv.2018.06.319.
- Hong YS, Chen YY, Yu L, Liu YF, Liu YL, Zhang Y, Liu Y, Cheng H. 2018b. Combining fractional order derivative and spectral variable selection for organic matter estimation of homogeneous soil samples by VIS-NIR spectroscopy. Remote Sens. 10. doi:https://doi.org/10.3390/rs10030479.
- Hong YS, Shen RL, Cheng H, Chen SC, Chen YY, Guo L, He JH, Liu YL, Yu L, Liu Y. 2019. Cadmium concentration estimation in peri-urban agricultural soils: using reflectance spectroscopy, soil auxiliary information, or a combination of both? Geoderma 354. doi:https://doi.org/10.1016/j.geoderma.2019.07.033.
- Huang GB, Zhu QY, Siew CK. 2006. Extreme learning machine: theory and applications. Neurocomputing 70:489–501. doi:https://doi.org/10.1016/j.neucom.2005.12.126.
- Kennard RW, Stone LA. 1969. Computer aided design of experiments. Technometrics. 11:137–148. doi:https://doi.org/10.1080/00401706.1969.10490666.
- Khosravi V, Ardejani FD, Yousefi S, Aryafar A. 2018. Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods. Geoderma 318:29–41. doi:https://doi.org/10.1016/j.geoderma.2017.12.025.
- Leardi R, Gonzalez AL. 1998. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom Intell Lab Syst. 41:195–207. doi:https://doi.org/10.1016/S0169-7439(98)00051-3.
- Li HD, Liang YZ, Xu QS, Cao DS. 2009. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal Chim Acta. 648:77–84. doi:https://doi.org/10.1016/j.aca.2009.06.046.
- Moron A, Cozzolino D. 2004. Determination of potentially mineralizable nitrogen and nitrogen in particulate organic matter fractions in soil by visible and near-infrared reflectance spectroscopy. J Agr Sci-Cambridge. 142:335–343. doi:https://doi.org/10.1017/S0021859604004290.
- Morra MJ, Hall MH, Freeborn LL. 1991. Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy. Soil Sci Soc Am J. 55:288–291. doi:https://doi.org/10.2136/sssaj1991.03615995005500010051x.
- National Soil Survey Office. 1993. Soils of China. Beijing:China Agriculture Press.
- Nelson DW, Sommers LE. 1980. Total nitrogen analysis of soil and plant tissues. J Assoc Offic Anal Chem. 63:770–777.
- Ning JM, Sheng MG, Yi XY, Wang YJ, Hou ZW, Zhang ZZ, Gu XG. 2018. Rapid evaluation of soil fertility in tea plantation based on near-infrared spectroscopy. Spectrosc Lett. 51:463–471. doi:https://doi.org/10.1080/00387010.2018.1475398.
- Sarathjith MC, Das BS, Wani SP, Sahrawat KL. 2016. Variable indicators for optimum wavelength selection in diffuse reflectance spectroscopy of soils. Geoderma 267:1–9. doi:https://doi.org/10.1016/j.geoderma.2015.12.031.
- Savitzky A, Golay MJ. 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 36:1627–1639. doi:https://doi.org/10.1021/ac60214a047.
- Shi TZ, Chen YY, Liu HZ, Wang JJ, Wu GF. 2014a. Soil organic carbon content estimation with laboratory-based visible-near-infrared reflectance spectroscopy: feature selection. Appl Spectrosc. 68:831–837. doi:https://doi.org/10.1366/13-07294.
- Shi TZ, Chen YY, Liu YL, Wu GF. 2014b. Visible and near-infrared reflectance spectroscopy-An alternative for monitoring soil contamination by heavy metals. J Hazard Mater. 265:166–176.
- Shi TZ, Cui LJ, Wang JJ, Fei T, Chen YY, Wu GF. 2013. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil 366:363–375. doi:https://doi.org/10.1007/s11104-012-1436-8.
- van Groenigen JW, Mutters CS, Horwath WR, van Kessel C. 2003. NIR and DRIFT-MIR spectrometry of soils for predicting soil and crop parameters in a flooded field. Plant Soil 250:155–165. doi:https://doi.org/10.1023/A:1022893520315.
- Viscarra Rossel RA, Behrens T. 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158:46–54. doi:https://doi.org/10.1016/j.geoderma.2009.12.025.
- Wang YQ, Zhang XC, Huang CQ. 2009. Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China. Geoderma 150:141–149. doi:https://doi.org/10.1016/j.geoderma.2009.01.021.
- Xu SX, Zhao YC, Wang MY, Shi XZ. 2017. Determination of rice root density from Vis-NIR spectroscopy by support vector machine regression and spectral variable selection techniques. Catena 157:12–23.
- Xu SX, Zhao YC, Wang MY, Shi XZ. 2018. Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis-NIR spectroscopy. Geoderma 310:29–43. doi:https://doi.org/10.1016/j.geoderma.2017.09.013.
- Zou XB, Zhao JW, Povey MJW, Holmes M, Mao HP. 2010. Variables selection methods in near-infrared spectroscopy. Anal Chim Acta. 667:14–32. doi:https://doi.org/10.1016/j.aca.2010.03.048.