| Orginal Article |
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Estimation model of soil salinization based on Landsat8 OLI image spectrum |
Yali ZHANG1,2( ), Tashpolat·Teyibai2(), Ardak·Kelimu1,2, Dong ZHANG1,2, Ilyas·Nuermaimaiti1,2, Fei ZHANG1,2 |
1. College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China
2. Key Laboratory of Oasis Ecology under Ministry of Education, Xinjiang University, Urumqi 830046, China |
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Abstract The purpose of this paper is to improve the precision of salinity monitoring model with Landsat8 OLI multi-spectral images in the oasis of arid area. In this paper, the authors chose the Ebinur Lake region as the study area, and reflectivity of saline soil based on OLI image and spectral reflectivity from resampled ASD data were measured respectively. According to the findings of the correlation analysis of twelve transforms of soil spectral reflectance with soil salt content, multiple stepwise regression analysis algorithm was used. Based on the analysis, the authors chose the most sensitive band ranges to establish a soil salinization monitoring model using the ASD actual measurement data and corrected OLI image inversion of soil salinity. The results show that the soil salt content inversion model based on the measured field spectral is satisfying, the first-order of the logarithm of the reciprocal with the best accuracy and the R2 is 0.779. Spectral reflectivity after resampling data performed better than those monitoring models with OLI spectral data, the coefficient of determination (R2) is raised from 0.28 to 0.777 6, and the RMSE is 0.281. The authors realized the scale transformation of the soil salt content spectral inversion model from field measurements of spectral scales to spectral scale of multi-spectral remote sensing, and the results could provide a theoretical reference for further improvement of the accuracy of quantitative remote sensing monitoring of soil salt content at the regional scale.
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| Keywords
hyperspectral
Landsat8 OLI image
soil salinization
correlation coefficient
multiple stepwise regression
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Issue Date: 08 February 2018
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2018,
Vol. 30
