| Cite this article: | 孙宇乐,屈忠义,刘全明.基于地面光谱联合SAR多源数据的农田表土氮磷监测[J].灌溉排水学报,0,():-. |
| Sun Yu-le,Qu Zhong-yi,Liu Quan-ming.基于地面光谱联合SAR多源数据的农田表土氮磷监测[J].灌溉排水学报,0,():-. |
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| Monitoring of Nitrogen and Phosphorus in Farmland Topsoil based on Multi-source Data of Ground Spectrum Combined with SAR |
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Sun Yu-le, Qu Zhong-yi, Liu Quan-ming
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College of Water Conservancy and Civil Engineering,Inner Mongolia Agricultural University
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| Abstract: |
| 【Objective】To establish a more accurate hyperspectral prediction model for more accurate and rapid determination of nitrogen and phosphorus content in soil topsoil, and further promote the application and development of multi-source remote sensing technology in modern and high-standard agricultural production and management.【Method】Based on the experimental area of Jiefang gate irrigation in Hetao irrigated area in Inner Mongolia, the measured spectral reflectance on the ground was used in combination with four-polarization backscattering data in C-band microwave Radar SAR (Synthetic Aperture Radar). Based on the selection of characteristic bands of soil nutrients, soil nitrogen and phosphorus quantities were modeled and evaluated.First using the spectral reflectance, and the logarithmic, first and second order derivative 4 kinds of spectral data, correlation analysis and filter selected to obtain the coefficient associated with the nitrogen and phosphorus were greater than 0.4 of near infrared 1, 2, 480, 050, 2 314 nm band at the same time using 1 ~ 8 layer get rid of the noise mapping technology, wavelet analysis and reconstruction to exclude specific value interference.After wavelet denoising, the characteristic bands with strong correlation are found, and a neural network model is formed by combining SAR backscattering coefficient and nutrients as regression and neural network input.【Result】Through the wavelet decomposition and reconstruction of hyperspectral data, the correlation between reflectance and its three transformation forms and soil nutrients can be effectively improved, especially the effect of 1~3 layers of low-frequency component and 4~6 layers of high-frequency component is better.The neural network model with the first derivative of reflectivity was the best prediction model, and its R2 and RMSE of soil nitrogen and phosphorus nutrient contents were 0.749 6 and 0.759 2 respectively, and the mean square error was 0.110 2, respectively. The stability and prediction accuracy of the model were better than that of the multiple linear regression model.【Conclusion】Combined spectral SAR can be used to predict soil total nitrogen and total phosphorus more quickly. |
| Key words: Soil nutrients; remote sensing; hyperspectral; model; wavelet transform; neural networks |
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