| 光谱学与光谱分析 |
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| Study on Estimation of Deserts Soil Total Phosphorus Content from Thermal-Infrared Emissivity |
| HOU Yan-jun, Tashpolat·Tiyip*, ZHANG Fei, Mamat·Sawut, Ilyas·Nurmemet |
| College of Resource and Environment Sciences, Xinjiang University, Key Laboratory of Oasis Ecology (Xinjiang University) Ministry of Education, Urumqi 830046, China |
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Abstract Soil phosphorus provides nutrient elements for plants, is one of important parameters for evaluating soil quality. The traditional method for soil total phosphorus content (STPC) measurement is not effective and time-consuming. However, remote sensing (RS) enables us to determine STPC in a fast and efficient way. Studies on the estimation of STPC in near-infrared spectroscopy have been developed by scholars, but model accuracy is still poor due to the low absorption coefficient and unclear absorption peak of soil phosphorus in near-infrared. In order to solve the deficiency which thermal-infrared emissivity estimate desert soil total phosphorus content, and could improve precision of estimation deserts soil total phosphorus. In this paper, characteristics of soil thermal-infrared emissivity are analyzed on the basis of laboratory processing and spectral measurement of deserts soil samples from the eastern Junggar Basin. Furthermore, thermal-infrared emissivity based RS models for STPC estimation are established and accuracy assessed. Results show that: when STPC is higher than 0.200 g·kg-1, the thermal-infrared emissivity increases with the increase of STPC on the wavelength between 8.00 μm and 13 μm, and the emissivity is more sensitive to STPC on the wavelength between 9.00 and 9.6 μm; the estimate mode based on multiple stepwise regression was could not to estimate deserts soil total phosphorus content from thermal-infrared emissivity because the estimation effects of them were poor. The estimation accuracy of model based on partial least squares regression is higher than the model based on multiple stepwise regression. However, the accuracy of second-order differential estimation model based on partial least square regression is higher than based on multiple stepwise regression; The first differential of continuous remove estimation model based on partial least squares regression is the best model with R2 of correction and verification are up to 0.97 and 0.82 respectively, and RMSE of correction and verification are only 0.010 6 and 0.015 7 respectively, RPD is 2.62. Research results provide optimized models for remotely sensed analysis on deserts soil total phosphorus content and could realize timeliness and effective monitoring on the space-time dynamic of deserts soil total phosphorus content for future regional ecological restoration.
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Received: 2014-09-25
Accepted: 2014-12-20
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Corresponding Authors:
Tashpolat·Tiyip
E-mail: tash@xju.edu.cn
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