[1] Wang Q, Yi Q X, Bao A M et al. Estimating chlorophyll density of cotton canopy by hyperspectral reflectance[J]. Transactions of the Chinese Society of Agricultural Engineering, 28, 125-132(2012).
[2] Wang D W, Huang C Y, Zhang W et al. Relationships analysis between cotton chlorophyll content, chlorophyll density and hyperspectral data[J]. Cotton Science, 20, 368-371(2008).
[3] Xu D Q, Liu X L, Wang W et al. Hyper-spectral characteristics and estimation model of leaf chlorophyll content in cotton under waterlogging stress[J]. Chinese Journal of Applied Ecology, 28, 3289-3296(2017).
[4] Hong S, Zhang Z, Zhang L F et al. Hyperspectral estimation model of chlorophyll content in cotton canopy leaves under drip irrigation at different growth stages[J]. Cotton Science, 31, 138-146(2019).
[5] Wu Q X, Li J B, Zhu J Q et al. Hyperspectral models for estimating SPAD value of cotton leaves under waterlogging stress[J]. Cotton Science, 29, 579-588(2017).
[6] Liu J D, Cao W B, Ma R. Study on remote sensing estimation models about LAI of cotton[J]. Scientia Agricultura Sinica, 41, 4301-4306(2008).
[7] Ma Y C, Liu H, Chen Z F et al. Canopy equivalent water thickness estimation of cotton based on hyperspectral index[J]. Scientia Agricultura Sinica, 52, 4470-4483(2019).
[8] Krockover G H, Odden T D. Remote sensing simulation activities for earthlings[J]. Science Teacher, 44, 42-43(1977).
[14] Chen Y, Huang C Y, Wang D W et al. Estimation of cotton chlorophyll density in north Xinjiang by using high spectral data[J]. Xinjiang Agricultural Sciences, 43, 451-454(2006).
[15] Ershat A, Mamat S, Baidengsha M et al. Estimation of leaf chlorophyll content in cotton based on the random forest approach[J]. Acta Agronomica Sinica, 45, 81-90(2019).
[16] Chu W L. Study on simulation model of cotton grow information based on hyperspectral data[D](2015).
[17] Tian M, Zhou J, Zhang Z et al. Estimation of cotton chlorophyll content based on hyperspectral vegetation index[J]. Jiangsu Agricultural Sciences, 45, 216-219(2017).
[18] Zhang X L, Li X Y, Chen Y J et al. Preliminary study on the difference of nutritional characteristics between long-staple cotton and upland cotton[J]. China Cotton, 29, 29-30(2002).
[19] Lü J. Hyperspectral inversion models of crop chlorophyll content based on support vector machine[J]. Science of Surveying and Mapping, 40, 88-91(2015).
[20] Liu C X, Gong Z N, Zhao W J et al. Remote sensing retrieval of chlorophyll-a concentration in Beijing Guishuihe river using support vector machine model[J]. Remote Sensing Technology and Application, 29, 419-427(2014).
[21] Chen F Y, Zhou X, Chen Y Y et al. Estimating biochemical component contents of diverse plant leaves with different kernel based support vector regression models and VNIR spectroscopy[J]. Spectroscopy and Spectral Analysis, 39, 428-434(2019).
[22] Qiu Z J, Song H Y, He Y et al. Variation rules of the nitrogen content of the oilseed rape at growth stage using SPAD and visible-NIR[J]. Transactions of the Chinese Society of Agricultural Engineering, 23, 150-154(2007).
[26] Zhang X L, Zhang F, Zhang H M 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).
[27] Lin S Z, Peng Z G, Zhang B Z et al. The estimation model of winter wheat canopy SPAD value based on spectral transformation[J]. China Rural Water and Hydropower, 33-38(2020).
[32] Zhang X Z, Zheng G Q, Dai T B et al. Estimation models of summer maize leaf pigment content based on canopy reflectance spectra[J]. Journal of Maize Sciences, 18, 55-60(2010).
[33] Guo J B, Ma X M, Shi L et al. Variety variation and hyperspectral estimate model of leaf area index of winter wheat[J]. Journal of Triticeae Crops, 38, 340-347(2018).
[34] Yang X H, Huang J F, Wang X Z et al. The estimation model of rice leaf area index using hyperspectral data based on support vector machine[J]. Spectroscopy and Spectral Analysis, 28, 1837-1841(2008).
[35] Liang D, Guan Q S, Huang W J et al. Remote sensing inversion of leaf area index based on support vector machine regression in winter wheat[J]. Transactions of the Chinese Society of Agricultural Engineering, 29, 117-123(2013).
[36] Li Y Y, Chang Q R, Liu X Y et al. Estimation of maize leaf SPAD value based on hyperspectrum and BP neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 32, 135-142(2016).
[37] Yao X, Wu H B, Zhu Y et al. Relationship between pigment concentrations and hyperspectral parameters in functional leaves of cotton[J]. Cotton Science, 19, 267-272(2007).
[38] Lü J. Chlorophyll content of rice irrigated with sewage estimated by the hyperspectral estimation model[J]. Guizhou Agricultural Sciences, 42, 237-239, 248(2014).
[39] Liu Z X. Research on remote sensing retrieval model of chlorophyll a concentration in inland water[D](2014).
[40] Zhang Z R, Chang Q R, Zhang T L et al. Hyperspectral estimation of chlorophyll content of cotton canopy leaves based on support vector machine[J]. Journal of Northwest A & F University (Natural Science Edition), 46, 39-45(2018).
[41] Dong Y C, Cai B X, Wang F M et al. Estimation of fresh biomass of rice based on optimum vegetation index[J]. Bulletin of Science and Technology, 35, 58-65(2019).
[42] Wang K N, Wang X X. Research on winter wheat yield estimation with the multiply remote sensing vegetation index combination[J]. Journal of Arid Land Resources and Environment, 31, 44-49(2017).
[43] Yang X H, Wu Y P, Huang J F et al. Remote sensing estimation of rice biophysical parameters based on support vector machine[J]. Science in China (Series C: Life Sciences), 39, 1080-1091(2009).
[44] Cheng L Z, Zhu X C, Gao L L et al. Estimation of chlorophyll content in apple leaves based on RGB model using digital camera[J]. Acta Horticulturae Sinica, 44, 381-390(2017).
[45] Wang N Y, Yu F H, Xu T Y et al. Hyperspectral retrieval modelling for chlorophyll contents of japonica-rice leaves based on machine learning[J]. Acta Agriculturae Zhejiangensis, 32, 359-366(2020).
[46] Xu Y, Dong X Y, Wang J J. Use of remote multispectral imaging to monitor chlorophyll-a in Taihu lake: A comparison of four machine learning models[J]. Journal of Hydroecology, 40, 48-57(2019).