[1] Rodríguez-Pérez J R, Ordóñez C, González-Fernández A B et al. . Leaf water content estimation by functional linear regression of field spectroscopy data[J]. Biosystems Engineering, 165, 36-46(2018).

[2] Fang M H, Ju W M, Zhan W F et al. A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves[J]. Remote Sensing of Environment, 196, 13-27(2017).

[3] Lin Y, Li Q, Wang H B et al. Research summary of vegetation water content inversion with hyperspectral technology[J]. Chinese Agricultural Science Bulletin, 31, 167-172(2015).

[4] Liang L, Zhang L P, Lin H et al. Estimating canopy leaf water content in wheat based on derivative spectra[J]. Scientia Agricultura Sinica, 46, 18-29(2013).

[5] Deng B, Yang W N, Mu N et al. The research of vegetation water content based on spectrum analysis and angle slope index[J]. Spectroscopy and Spectral Analysis, 36, 2546-2552(2016).

[6] Dobrowski S, Pushnik J, Zarco-Tejada P et al. Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale[J]. Remote Sensing of Environment, 97, 403-414(2005). http://www.sciencedirect.com/science/article/pii/S0034425705001665

[7] Tian Y C, Zhu Y, Cao W X et al. Relationship between canopy reflectance and plant water status of wheat[J]. Chinese Journal of Applied Ecology, 15, 2072-2076(2004).

[8] Wang Y Y, Li G C, Zhang L J et al. Retrieval of leaf water content of winter wheat from canopy hyperspectral data using partial least square regression[J]. Spectroscopy and Spectral Analysis, 30, 1070-1074(2010).

[9] Chen D Y, Huang J F, Jackson T J. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands[J]. Remote Sensing of Environment, 98, 225-236(2005). http://europepmc.org/abstract/agr/ind500662009

[10] Zheng X M, Ding Y L, Zhao K et al. Estimation of vegetation water content from Landsat 8 OLI data[J]. Spectroscopy and Spectral Analysis, 34, 3385-3390(2014).

[11] Ghulam A, Li Z L, Qin Q M et al. A method for remote sensing monitoring of total vegetation canopy water: short wave infrared vertical water loss index[J]. Science in China, 37, 957-965(2007).

[12] Xu J G, Feng X L, Guan L et al. Fractional differential application in reprocessing infrared spectral data[J]. Control and Instruments in Chemical Industry, 39, 347-351(2012).

[13] Zhang W W, Yang K M, Xia T et al. Correlation analysis on spectral fractional-order differential and the content of heavy metal copper in corn leaves[J]. Science Technology and Engineering, 17, 33-38(2017).

[14] Lin S M, Xu C J. Theoretical and numerical investigation of fractional differential equations[J]. Mathematica Numerica Sinica, 38, 1-24(2016).

[15] Zhang D, Tiyip T, Zhang F et al. Effect of fractional differential algorithm on hyperspectral data of saline soil[J]. Acta Optica Sinica, 36, 0330002(2016).

[16] Liu K, Chen X J, Li L M et al. A consensus successive projections algorithm-multiple linear regression method for analyzing near infrared spectra[J]. Analytica Chimica Acta, 858, 16-23(2015).

[17] Liu M B, Tang Y L, Li X L et al. Feasibility of using successive projections algorithm in spectral monitoring of rice leaves nitrogen contents[J]. Infrared and Laser Engineering, 43, 1265-1271(2014).

[18] Liu Q M, Cheng Q M, Wang X et al. Soil salinity inversion in Hetao Irrigation district using microwave radar[J]. Transactions of the Chinese Society of Agricultural Engineering, 32, 109-114(2016).

[19] Umut H, Mamat S, Ilyas N et al. Inversion model of soil salt content based on WorldView-2 image[J]. Transactions of the Chinese Society of Agricultural Engineering, 33, 200-206(2017).