Development of a predictive tool for rapid assessment of soil total nitrogen in wheat-corn double cropping system with hyperspectral data

References

• Stephan G, Daniel V, Darlene Z, et al. Hyperspectral analysis of soil nitrogen, carbon, carbonate, and organic matter using regression trees[J]. Sensors. 2012;12(8):10639–10658.
   Web of Science ®Google Scholar
• Zheng W, Zhang M, Liu Z, et al. Combining controlled-release urea and normal urea to improve the nitrogen use efficiency and yield under wheat-maize double cropping system[J]. Field Crops Res. 2016;197(13):52–62.
   Google Scholar
• Zheng W, Liu Z, Zhang M, et al. Improving crop yields, nitrogen use efficiencies, and profits by using mixtures of coated controlled-released and uncoated urea in a wheat-maize system[J]. Field Crops Res. 2017;205(6):106–115.
   Google Scholar
• Bao S. Analysis of soil agrochemistry [M]. Beijing: China agricultural press; 2000. p. 42–54.
   Google Scholar
• Bai Y, Jin J, Yang L, et al. Technology of low altitude remote sensing and its applications in precision agriculture [J]. Soil Fert. 2004;(01):3–5.
   Google Scholar
• Yang L. Present situation and development trend of remote sensing technology in precision agriculture [J]. Agric Technol. 2019;39(16):21–22.
   Google Scholar
• Wang H, Liu F, Yunger John A, et al. A fitting model of hyperspectral characteristics of soil total nitrogen content with different particle size treatments[J]. Trans Chin Soc Agric Mach. 2019;50(02):195–204.
   Google Scholar
• Vigneau N, Rabatel G, Rabatel G, et al. Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat[J]. Field Crops Res. 2011;122(102):25–31.
   Google Scholar
• Yang H, Kuang B, A M Q M, et al. Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction[J]. Eur J Soil Sci. 2012;63(3):27–34.
   Web of Science ®Google Scholar
• Mingzhu W, Xiaomei L, Sha J, et al. Spectral inversion models for prediction of red soil total nitrogen content in subtropical region (Fu zhou) [J]. Specftroscopy Spectral Anal. 2013;33(11):3111–3115.
   Web of Science ®Google Scholar
• Qiao X, Wang C, Feng M, et al. Hyperspectral estimation of soil organic matter based on different spectral preprocessing techniques[J]. Spectrosc Lett. 2017;50(3):156–163.
   Web of Science ®Google Scholar
• Serbin G, Daughtry CST, Raymond Hunt Jr E, et al. Effects of soil composition and mineralogy on remote sensing of crop residue cover[J]. Remote Sens Environ. 2009;113(1):224–238.
   Web of Science ®Google Scholar
• A H A-A, P H S, Baumgardner MF. Relating organic matter and clay content to the multispectral radiance of soils[J]. Soil Sci. 1972;114(6):477–485.
   Web of Science ®Google Scholar
• Iman T, Xu Z, Boyd S, et al. Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions[J]. Geoderma. 2018;330:254–263.
   Web of Science ®Google Scholar
• Bi YM, Yuan KL, Xiao WQ, et al. A local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation[J]. Analytica Chimica Acta. 2016;909:30–40.
   Google Scholar
• Huete AR. A soil-adjusted vegetation index (SAVI)[J]. Remote Sens Environ. 1988;25(3):295–309.
   Web of Science ®Google Scholar
• C M M, R P Z, Robertson JA. An analysis of soil organic matter dynamics in relation to management, erosion and yield of wheat in long-term crop rotation plots[J]. Can J Soil Sci. 1997;77(4):553–563.
   Web of Science ®Google Scholar
• Jia S, Hongyang L, Wang Y, et al. Hyperspectral imaging analysis for the classification of soil types and the determination of soil total nitrogen[J]. Sensors. 2017;17(10):2252.
   Web of Science ®Google Scholar
• Broadhurst D, Goodacre R, Jones A, et al. Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry[J]. Anal Chim Acta. 1997;348(1–3):71–86.
   Web of Science ®Google Scholar
• Luan F, Zhang X, Xiong H, et al. Comparative analysis of soil organic matter content based on different hyperspectral inversion models[J]. Spectrosc Spectral Anal. 2013;33(1):196.
   Web of Science ®Google Scholar
• Liu C, Fang Z, Chen Z, et al. Nitrogen nutrition diagnosis of winter wheat based on ASD Field Spec3[J]. Trans Chin Soc Agric Eng. 2018;34(19):162–169.
   Google Scholar
• Udelhoven T, Emmerling C, Jarmer T, et al. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study[J]. Plant Soil. 2003;251(2):319–329.
   Web of Science ®Google Scholar
• Zheng D, Jeong YS, Myong K, et al. Multidimensional local spatial autocorrelation measure for[J]. Appl Intell. 2012;36(3):542–552.
   Web of Science ®Google Scholar
• Yang XS, Wang LL, Zhou XR, et al. Determination of protein, fat, starch, and amino acids in foxtail millet [Setaria italica (L.) Beauv.] by Fourier transform near-infrared reflectance spectroscopy [J]. Food Science and Biotechnology. 2013;22(6):1495–1500.
   Google Scholar
• Jin YH, Xiong HG, Fang Z. Comparative study of canopy spectral reflectance characteristics of spring wheat in irrigated land and dry land[J]. Remote Sensing for Land & Resources. 2014;26(3):24–30.
   Google Scholar
• Yanli L, Bai Y, Wang L, et al. Determination for total nitrogen content in black soil using hyperspectral data. Trans CSAE. 2010;26(1):256－261.
   Google Scholar
• Yan L, Wang R, Guan Y, et al. Prediction analysis of soil total nitrogen content based on hyperspectral[J]. Remote Sens Technol Appl. 2017;32(01):173–179.
   Google Scholar
• Shao X, Ning Y, Liu F, et al. Application of near infrared spectroscopy in the analysis of inorganic trace components [J]. J Chem. 2012;70(20):2109–2114.
   Google Scholar
• Yang H, Irudayaraj J, Paradkar MM, et al. Discriminant analysis of edible oils and fats by FTIR, FT-NIR and FT-Raman spectroscopy[J]. Food Chem. 2005;93(1):25–32.
   Web of Science ®Google Scholar
• Jiabo L. Research carbon, nutrient elements and FTIR characteristics of lateritic red soil of guangxi natural forest ecosystem[D]; 2017:48–50. Guangxi: Guangxi University.
   Google Scholar
• M V C, J D R, A R F, et al. The decomposition of straw in soil: use of multiple internal reflectance infrared spectroscopy[J]. Eur J Soil Sci. 1993;44(4):693–702.
   Google Scholar
• J D R, A R F, A H G, et al. Rumen digestion of untreated and alkali-treated cereal straws: a study by multiple internal reflectance infrared spectroscopy[J]. J Sci Food Agric. 2010;45(2):95–107.
   Google Scholar
• Gomez C, Lagacherie P, Coulouma G, et al. Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements[J]. Geoderma. 2008;148(2):141–148.
   Web of Science ®Google Scholar
• Tamayo C, Jesús H, Rubiano SY, et al. Near-infrared (NIR) diffuse reflectance spectroscopy for the prediction of carbon and nitrogen in an Oxisol[J]. Agron Colomb. 2014;32(1):86–94.
   Google Scholar
• Sun B, Chen W. Classification of sEMG signals using integrated neural network with small sized training data [J]. Biomed Eng Appl Basis Commun. 2012;24(04):365–376.
   Google Scholar
• Wu J, Guo XS, Wang YQ, et al. Decomposition characteristics of rapeseed and wheat straws under different rice cultivations and straw mulching models[J]. Scientia Agricultura Sinica. 2011;44(16):3351–3360.
   Google Scholar
• Xiaoli L, Sun C, Zhou B, et al. Determination of hemicellulose, cellulose and lignin in moso bamboo by near infrared spectroscopy[J]. Sci Rep. 2015;5:17210.
   Web of Science ®Google Scholar
• Serrano L, S L U,Ustin SL, et al. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals[J]. Remote Sens Environ. 2002;81(2–3):355–364.
   Web of Science ®Google Scholar