[1] Kweon G, Lund E, Maxton C. Soil organic matter and cation-exchange capacity sensing with on-the-go electrical conductivity and optical sensors[J]. Geoderma, 199, 80-89(2013). http://www.sciencedirect.com/science/article/pii/S0016706112003886

[2] Shi Z, Ji W. Viscarra Rossel R A, et al. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese VIS-NIR spectral library[J]. European Journal of Soil Science, 66, 679-687(2015).

[3] Wang X P, Zhang F, Kung H T et al. New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China[J]. Remote Sensing of Environment, 218, 104-118(2018).

[4] Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[J]. Soil Science Society of America Journal, 59, 364-372(1995).

[5] Zhang Z P, Ding J L, Wang J Z et al. Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices[J]. Catena, 185, 104257(2020).

[6] Li G W, Gao X H, Xiao N W et al. Estimation of soil organic matter content based on characteristic variable selection and regression methods[J]. Acta Optica Sinica, 39, 0930002(2019).

[7] He D J, Chen X. Real-time measurement of soil organic matter content in field[J]. Transactions of the Chinese Society for Agricultural Machinery, 46, 127-132(2015).

[8] Wang H F, Zhang Z T, Karnieli A et al. Hyperspectral estimation of desert soil organic matter content based on gray correlation-ridge regression model[J]. Transactions of the Chinese Society of Agricultural Engineering, 34, 124-131(2018).

[9] Shi Z, Wang Q L, Peng J et al. Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations[J]. Science China Earth Sciences, 57, 1671-1680(2014).

[10] Hong Y S, Chen S C, Liu Y L et al. Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy[J]. Catena, 174, 104-116(2019).

[11] Zhang H L, Luo W, Liu X M et al. Measurement of soil organic matter with near infrared spectroscopy combined with genetic algorithm and successive projection algorithm[J]. Spectroscopy and Spectral Analysis, 37, 584-587(2017).

[12] Luan F M, Zhang X L, Xiong H G et al. Comparative analysis of soil organic matter content based on different hyperspectral inversion models[J]. Spectroscopy and Spectral Analysis, 33, 196-200(2013).

[13] Ye Q, Jiang X Q, Li X C et al. Comparison on inversion model of soil organic matter content based on hyperspectral data[J]. Transactions of the Chinese Society for Agricultural Machinery, 48, 164-172(2017).

[14] Huang F M, Zhang J, Zhou C B et al. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction[J]. Landslides, 17, 217-229(2020).

[15] Ayinde B O, Inanc T, Zurada J M. Regularizing deep neural networks by enhancing diversity in feature extraction[J]. IEEE Transactions on Neural Networks and Learning Systems, 30, 2650-2661(2019).

[16] Feng J, Zhou Z H. Autoencoder by forest. [C]//Thirty-Second AAAI Conference on Artificial Intelligence. New York: AAAI(2018).

[17] Bello G A. Dawes T J W, Duan J M, et al. Deep-learning cardiac motion analysis for human survival prediction[J]. Nature Machine Intelligence, 1, 95-104(2019).

[18] Zhang R, Isola P, Efros A A. Split-brain autoencoders: unsupervised learning by cross-channel prediction. [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 645-654(2017).

[19] Chen B, Zou X Y, Zhu W J. Eliminating outlier samples in near-infrared model by method of PCA-mahalanobis distance[J]. Journal of Jiangsu University (Natural Science Edition), 29, 277-279, 292(2008).

[20] Ge X Y, Ding J L, Wang J Z et al. Estimation of soil moisture content based on competitive adaptive reweighted sampling algorithm coupled with machine learning[J]. Acta Optica Sinica, 38, 1030001(2018).

[21] Hong Y S, Liu Y L, Chen Y Y et al. Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy[J]. Geoderma, 337, 758-769(2019).

[22] Xu S X, Zhao Y C, Wang M Y et al. Determination of rice root density from Vis-NIR spectroscopy by support vector machine regression and spectral variable selection techniques[J]. Catena, 157, 12-23(2017).

[23] Yu L, Hong Y S, Zhou Y et al. Wavelength variable selection methods for estimation of soil organic matter content using hyperspectral technique[J]. Transactions of the Chinese Society of Agricultural Engineering, 32, 95-102(2016).

[24] Ouyang Q, Chen Q S, Zhao J W. Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 154, 42-46(2016).

[25] Morellos A, Pantazi X E, Moshou D et al. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy[J]. Biosystems Engineering, 152, 104-116(2016).

[26] Yuan F N, Zhang L, Shi J T et al. Theories andapplications of auto-encoder neural networks: a literature survey[J]. Chinese Journal of Computers, 42, 203-230(2019).