Acta Scientiarum Naturalium Universitatis Sunyatseni ›› 2020, Vol. 59 ›› Issue (4): 56-63.doi: 10.13471/j.cnki.acta.snus.2019.09.20.2019D037

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Hyperspectral estimation of soil salt content in lake oasis on the west bank of Bosten lake 

ZHAO Hui, LI Xinguo, JIN Wangui, Mamattursun·Eziz, NIU Fangpeng   

  1. College of Geographic Sciences and TourismXinjiang Normal University / Xinjiang Laboratory of Lake Environment and Resources in Arid ZoneUrumqi 300548China
  • Received:2019-09-20 Online:2020-07-25 Published:2020-07-25

Abstract: Taking the soil salinity and corresponding hyperspectral data of the lakeside oasis on the west bank of the Bosten Lake as the research objectthe root mean square Rlogarithmic lgRrecipro⁃cal 1/Rreciprocal 1/lgR were used to transform the original spectral reflectance. The fractional differentiation was introduced to perform differential preprocessing within 0-2 on the transformed spectral reflectance. The characteristic band through significance test was used to model and verify partial least squares regression. The results show that: 1)The characteristic bands that passed the significance test were mostly 1/R of 691 bandswhich was 226 more bands than the original band R. With increasing orders,the number of bands that passed the significance test in each order showed a trend of increasing first and then decreasing. The characteristic bands that passed more were 0.4 and 0.6 orders of 1/Rwhich were 145 and 150respectively. 2)The absolute value of the correlation maximum value of the original spectrum R was 2nd order 0.53. The absolute value of the correlation maximum value of the other four mathematical transformations was 0.11-0.16higher than R.The characteristic bands were mainly concentrated at 600-1 000 nm and 2 020-2 330 nm. 3)The fractional differential of the original spectral Rthe root mean square R,the logarithm lgRthe reciprocal 1/Rand the logarithm reciprocal 1/lgR was modeled by partial least squares regression. The model established at 0.2th order of 1/lgR was the best onewith R2C=0.78RMSEC=1.56R2V=0.63RMSEV=1.44

Key words: fractional differential,  , soil salinity,  , spectral transformation,  , partial least squares regression methods,  , hyperspectral estimation

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