| Autor: |
SONG Xue, ZHANG Min, ZHOU Hong-yin, YU Xiao-jing, LIU Zhi-guang, XU Zi-yun, WANG You-liang |
| Jazyk: |
čínština |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Journal of Agricultural Resources and Environment, Vol 37, Iss 1, Pp 43-50 (2020) |
| ISSN: |
2095-6819 |
| Popis: |
In order to achieve a rapid and accurate estimation of soil total nitrogen(TN) in the Taihu Lake region, the hyperspectral characteristics of TN and quantitative analysis models should be constructed to reduce potential environmental risks and provide a technical reference for precision agriculture. In this study, we acquired spectral data and TN of 93 samples with similar soil textures from a large geographical lakeshore zone in Wuxi, Jiangsu Province. Three model methods were investigated. We filtered the sensitive wavelengths using a correlation coefficient in peak value. Based on regulating soil spectral index(MSASI) arithmetic established by sensitive wavelengths, we used multivariate linear regression, artificial neural networks and partial least squares methods to construct a quantitative spectral analysis model of soil TN content. Our results indicated that total soil nitrogen content and spectral reflectance were positively correlated. We also found that sensitive wave bands, including 420~444 nm and 480~537 nm based on MSASI and multiple linear regression analysis on the sensitive wavelengths yield the best results(R2=0.98, RMSE=0.04) with high accuracy and reliability and were the best models for predicting soil total nitrogen content. The partial least squares model(R2=0.70, RMSE=0.13) and artificial neural network model(R2=0.69, RMSE=0.15) were the least accurate. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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