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| Hyperspectral inversion model of Zn in high standard farmland soil in Xiping County |
| Received:August 26, 2022 Revised:September 21, 2022 |
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| KeyWord:high standard farmland;hyperspectral inversion;partial least squares;continuous projection algorithm;Zn |
| Author Name | Affiliation | E-mail | | CAI Taiyi | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China | | | WANG Zhigang | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China | | | YANG Liushuai | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
The Fourth Topographic Survey Team of the Ministry of Natural Resources, Harbin 150000, China | | | WANG Qun | College of Agronomy, Henan Agricultural University/Henan Province Agro-ecosystem Field Observation and Research Station, Zhengzhou 450046, China | | | HUANG Huijuan | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China | | | YU Haiyang | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China | | | ZHANG Chuanzhong | Henan Province Soil Conditioning and Repair Engineering Technology Research Center, Shangqiu 476000, China | | | ZHANG Can | View Sino Orise Technology Co., Ltd., Wuxi 214400, China | | | LIU Peng | Mineral Resources Exploration Center of Henan Geological Bureau, Zhengzhou 450053, China | | | FENG Yuqing | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China | | | HE Chenglong | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China | | | ZHANG Hebing | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China | jzitzhb@hpu.edu.cn |
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| Abstract: |
| To rapidly determine the heavy metal Zn content in high-standard farmland soil, we collected and analyzed Zn in soil collected in Xiping County. Through indoor experiments in which 168 soil samples were collected, soil hyperspectral data（400-2 400 nm）were obtained and smoothed using the Savitzky–Golay method. Five types of spectral transformations and continuous projection algorithms were used to identify the best characteristic bands, and the partial least square regression method was used to construct an optimal inversion model of Zn. The correlation of the second-order differential（-0.502）was highest at the 1 409 nm band; the correlation of the first-order differential（0.491）was largest in the 2 323 nm band; and the correlation of the de-envelope（0.476）was the highest in the 2 439 nm band. The fitting degree of a reciprocal logarithm, first-order differential, second-order differential, smooth curve, and de-envelope was 0.65- 0.70, and the residual predictive deviation（RPD）was 1.71-2.29. The de-envelope showed the highest fitting degree（R2=0.70, RPD= 2.29） . The five types of spectral transformation can highlight variations in spectral reflectance and can be used to construct an inversion mode. The best model for soil heavy metal Zn is the de-enveloping spectral transformation, which is a partial least square model. |
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