A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Summer maize LAI retrieval based on multi-source remote sensing data

Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize in different growth stages quickly and accurately,further guiding field fertilization and irrigation.The Unmanned aerial vehicles(UAV)multispectral data,growing degree days,and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion.The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands.The correlation analysis was conducted to verify the accuracy of the multispectral data.To include many bands as possible,four vegetation indices which included R,G,B,and NIR bands were selected in this study to test the spectral accuracy.There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band.Through correlation analysis of LAI and the vegetation index,vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model.In addition,the Canopy Height Model(CHM)and Growing degree days(GDD)of summer maize were also calculated to build the LAI inversion model.The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model(GLR),Multivariate nonlinear regression model(MNR),and the partial least squares regression(PLSR)models.R²and RMSE were used to assess the accuracy of the model.The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64,which was significant.The Wide Dynamic Range Vegetation Index(WDRVI),Normalized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI),Plant Biochemical Index(PBI),Optimized Soil-Adjusted Vegetation Index(OSAVI),CHM and GDD have a higher correlation with LAI.By comparing the models constructed by the three methods,it was found that the PLSR has the best inversion effect.It was based on OSAVI,GDD,RVI,PBI,CHM,NDVI,and WDRVI,with the training model’s R²being 0.8663,the testing model’s R²being 0.7102,RMSE was 1.1755.This study showed that the LAI inversion model based on UAV multispectral vegetation index,GDD,and CHM improves the accuracy of LAI inversion effectively.That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly,and this method can provide decision support for maize growth monitoring and field fertilization.