Study On Soil Total Nitrogen Inversion Model Based On Remote Sensing

Soil is the basis of agricultural production.As an important index of soil quality and fertility,soil total nitrogen content can provide important reference information for growers.In recent years,with the development of hyperspectral remote sensing technology and machine learning technology,the combination of them to solve agricultural problems has become one of the research hotspots of intelligent agriculture.In order to further study the mapping relationship between soil total nitrogen content and remote sensing data as well as the influence of some factors,this study focused on the feasibility study of remote sensing monitoring and UAV image remote sensing monitoring of soil total nitrogen content under different background and particle size factors,aiming at the problems of insufficient information technology means in soil monitoring.By obtaining remote sensing data of different influencing factors,the correlation between soil total nitrogen content,index and spectrum was analyzed,and inversion models under different influencing factors were constructed.The response bands of soil total nitrogen were extracted based on multiple feature selection methods,and the estimation model of soil total nitrogen content was constructed by machine learning algorithm.At the same time,field plots with significant difference in total nitrogen content were selected,and hyperspectral camera was installed to acquire soil total nitrogen image by using eight-rotor UAV as remote sensing platform,and the feasibility of UAV hyperspectral data in estimating soil total nitrogen content was evaluated.The main results are as follows:(1)In view of soil total nitrogen monitoring under different background factors,133 spectral samples of tidal soil were studied and it was found that indoor spectral reflectance was overall larger than outdoor spectral reflectance,and indoor spectral curve was smoother than outdoor spectral curve.Outdoor spectra of the same sample showed obvious bumps at 900 nm and 1200 nm due to background noise.The features of indoor and outdoor spectra(index,competitive adaptive reweighting CARS,continuous wavelet CWT)were screened respectively,and three machine methods were combined for modeling.The results show that,among all models,the third layer sensitivity coefficient after continuous wavelet decomposition has the highest modeling accuracy.For outdoor spectrum,the R~2 of random forest(RF)validation model established after continuous wavelet processing is 0.85.The best indoor spectral model is the partial least squares(PLSR)verified model R~2 of 0.91 after continuous wavelet processing.After comparing the outdoor spectral model with the indoor spectral model,it is found that the prediction accuracy of indoor spectral model is better than that of outdoor spectral model.The reason is that the external environment introduces more spectral background noise,and the light source is not as stable and bright as indoor.At the same time,by comparing various feature screening methods,it is found that continuous wavelet processing is better than the other two preprocessing methods in improving the modeling accuracy,because continuous wavelet not only takes into account the amplification of spectral features,but also balances the introduction of noise and invalid information.(2)In order to study the influence of different particle sizes on the monitoring accuracy of soil total nitrogen,118 tidal soils were selected as research samples,and screened with 2mm(10 mesh),0.9mm(20 mesh),and 0.15mm(100 mesh),respectively.Soil spectra and corresponding total nitrogen content were tested synchronously.The results show that the spectral reflectance curve of soil is greatly affected by soil particle size.In the range of 400nm-2450 nm,the trend of spectral curve of soil with different particle sizes was roughly the same.At 1400 nm,1900nm,and 2200 nm,there were obvious water absorption peaks,and the reflectance changed greatly.The spectral reflectance of soil with different particle sizes in the same sample varies in the same wavelength,and the spectral reflectance value increases with the decrease of soil particle size.Both soil particle size and soil total nitrogen content were negatively correlated with soil reflectance.After mathematical transformation of the original spectrum,the correlation was significantly improved,among which the first derivative spectrum had the best correlation with the two.The regression models of the three particle sizes were studied respectively.The first derivative spectrum of 0.15 mm particle size was selected by CARS algorithm and support vector machine(SVM)to construct the soil total nitrogen content estimation model with the best performance,and the modeling R~2 was 0.91 and the verification R~2 was0.87.(3)In order to study the feasibility of inversion of soil total nitrogen by UAV hyperspectral image,the spectra of 75 surface soil samples in the study area were obtained by UHD185 imaging spectral camera.The results show that in the range of 450nm-950 nm,the spectrum extracted from UAV image has roughly the same trend as outdoor spectrum,and the sensitive band range of original spectrum is 450nm-480 nm and 630nm-860 nm.After mathematical transformation of the original spectrum,the index model and the machine regression model were constructed respectively.Among them,the ratio spectral index model based on RVI(CR622,CR910)showed the best performance,and the modeling R~2 was 0.64.The best machine regression model was RF model established after continuum removal(CR)and CARS processing,and the modeling R~2 was 0.69.The low accuracy of UAV hyperspectral data modeling may be caused by uneven ground and different particle size.Both models can provide reference for estimating soil total nitrogen on a large scale and have certain application potential.