[1] Zhou T, Geng Y J, Ji C et al. Prediction of soil organic carbon and the C∶N ratio on a national scale using machine learning and satellite data: a comparison between Sentinel-2, Sentinel-3 and Landsat-8 images[J]. Science of the Total Environment, 755, 142661(2021).

[2] Su Y Z, Zhao H L. Advances in researches on soil organic carbon storages, affecting factors and its environmental effects[J]. Journal of Desert Research, 22, 220-228(2002).

[3] Zhu Y L, Wang D Y, Zhang H et al. Soil organic carbon content retrieved by UAV-borne high resolution spectrometer[J]. Transactions of the Chinese Society of Agricultural Engineering, 37, 66-72(2021).

[4] Gopal Ramdas M, Manjunath B L, Narendra Pratap S et al. Effect of organic and inorganic sources of nutrients on soil microbial activity and soil organic carbon build-up under rice in west coast of India[J]. Archives of Agronomy and Soil Science, 63, 414-426(2017).

[5] 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).

[6] Huang X Y, Wang X M, Baishan K et al. Hyperspectral estimation of soil organic carbon content based on continuous wavelet transform and successive projection algorithm in arid area of Xinjiang, China[J]. Sustainability, 15, 2587(2023).

[7] Zhou Y, Zhang R J, Yuan W D et al. Research progress on influencing factors and correction methods of near infrared spectroscopy and imaging[J]. Laser & Optoelectronics Progress, 61, 0400003(2024).

[8] Liu W Q, Chen Z Y, Liu J G et al. Advances with respect to the environmental spectroscopy monitoring technology[J]. Acta Optica Sinica, 40, 0500001(2020).

[9] Shen Q, Xia K, Zhang S W et al. Hyperspectral indirect inversion of heavy-metal copper in reclaimed soil of iron ore area[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 222, 117191(2019).

[10] Nocita M, Stevens A, Noon C et al. Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy[J]. Geoderma, 199, 37-42(2013).

[11] Li W Y, Maimaiti S, Maihemuti B. Fractional differential-based hyperspectral inversion of soil organic matter content[J]. Laser & Optoelectronics Progress, 60, 0730005(2023).

[12] Xie P, Wang Z H, Xiao B et al. Hyperspectral inversion of soil selenium content based on seagull algorithm optimized random forest[J]. Laser & Optoelectronics Progress, 60, 1730001(2023).

[13] Zhu C M, Wang H W, Xie X et al. Inversion of soil organic matter content based on spectral index and machine learning[J]. Jiangsu Agricultural Sciences, 48, 233-241(2020).

[14] Nijiati K, Rukeya S, Shi Q D et al. Estimation of soil organic matter content based on optimized spectral index[J]. Transactions of the Chinese Society for Agricultural Machinery, 49, 155-163(2018).

[15] Zhou Y, Cheng Y S, Wang D P et al. Hyperspectral inversion of soil arsenic content in polymetallic mining areas based on optimized spectral index combined with PLSR[J]. Journal of Chinese Society of Nonferrous Metals, 34, 653-667(2024).

[16] Ye M, Zhu L, Liu X D et al. Hyperspectral inversion of soil organic matter content based on continuous wavelet transform, SHAP, and XGBoost[J]. Environmental Science, 45, 2280-2291(2024).

[17] Jiang Y D, Li X G, Yang H. Hyperspectral estimation of organic carbon content in surface soils based on continuous wavelet transform[J]. Jiangsu Journal of Agricultural Sciences, 39, 118-125(2023).

[18] Wang Y C, Zhang L, Wang H et al. Quantitative inversion of soil organic matter content based on continuous wavelet transform[J]. Spectroscopy and Spectral Analysis, 38, 3521-3527(2018).

[19] Ward K J, Chabrillat S, Brell M et al. Mapping soil organic carbon for airborne and simulated EnMAP imagery using the LUCAS soil database and a local PLSR[J]. Remote Sensing, 12, 3451(2020).

[20] Castaldi F, Chabrillat S, van Wesemael B. Sampling strategies for soil property mapping using multispectral sentinel-2 and hyperspectral EnMAP satellite data[J]. Remote Sensing, 11, 309(2019).

[21] Wang X P, Zhang F, Ding J L et al. Estimation of soil salt content (SSC) in the Ebinur Lake wetland national nature reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices[J]. Science of the Total Environment, 615, 918-930(2018).

[22] 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).

[23] Zhang J J, Tian Y C, Yao X et al. Estimating soil total nitrogen content based on hyperspectral analysis technology[J]. Journal of Natural Resources, 26, 881-890(2011).

[24] Jordan C F. Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 50, 663-666(1969).

[25] Gitelson A A, Viña A, Arkebauer T J et al. Remote estimation of leaf area index and green leaf biomass in maize canopies[J]. Geophysical Research Letters, 30, 1248(2003).

[26] Cheng T, Rivard B, Sánchez-Azofeifa G A et al. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation[J]. Remote Sensing of Environment, 114, 899-910(2010).

[27] Shahraiyni H, Ghafouri M, Shouraki S et al. Comparison between active learning method and support vector machine for runoff modeling[J]. Journal of Hydrology and Hydromechanics, 60, 16-32(2012).

[28] Breiman L. Random forests[J]. Machine Learning, 45, 5-32(2001).

[29] Ramadan Z, Hopke P K, Johnson M J et al. Application of PLS and back-propagation neural networks for the estimation of soil properties[J]. Chemometrics and Intelligent Laboratory Systems, 75, 23-30(2005).

[30] Benesty J, Chen J D, Huang Y T et al. Pearson correlation coefficient[M]. Noise reduction in speech processing, 1-4(2009).

[31] Chicco D, Warrens M J, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation[J]. PeerJ Computer Science, 7, e623(2021).

[32] Zhou W, Li H R, Wen S Y et al. Simulation of soil organic carbon content based on laboratory spectrum in the three-rivers source region of China[J]. Remote Sensing, 14, 1521(2022).

[33] Rossel R A V, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma, 158, 46-54(2010).

[34] Zhang Z P, Ding J L, Wang J Z. Spectral characteristics of oasis soil in arid area based on harmonic analysis algorithm[J]. Acta Optica Sinica, 39, 0228003(2019).

[35] Zhang X L, Zhang F, Zhang H W et al. Optimization of soil salt inversion model based on spectral transformation from hyperspectral index[J]. Transactions of the Chinese Society of Agricultural Engineering, 34, 110-117(2018).

[36] Yu L, Hong Y S, Zhou Y et al. Inversion of soil organic matter content using hyperspectral data based on continuous wavelet transformation[J]. Spectroscopy and Spectral Analysis, 36, 1428-1433(2016).

[37] Yang H B, Li F, Wang W et al. Estimating above-ground biomass of potato using random forest and optimized hyperspectral indices[J]. Remote Sensing, 13, 2339(2021).

[38] Zhang Y, Wang T, Li Z et al. Based on machine learning algorithms for estimating leaf phosphorus concentration of rice using optimized spectral indices and continuous wavelet transform[J]. Frontiers in Plant Science, 14, 1185915(2023).