Feasibility of Field Evaluation of Rice Nitrogen Status From Reflectance Spectra of Canopy *1

Techniques for measurement of the N status of rice can be an aid to making management decisions with economic and environmental implications. A field experiment was conducted to identify spectral variables most sensitive to N deficiency detection in rice canopy with the possibility for their use as a management tool. Spectral and agronomic measurements were collected in the evaluation experiment of N status from rice canopy under five N treatments in a silt loam soil. Nitrogen fertilization effects were seen across the entire wavelength measured. Red reflectance decreased and near infrared reflectance increased with increasing N fertilizer application. Spectral differences between treatments were seen throughout the test period. The near infrared reflectance/red reflectance ratio (RVI) differed more between treatments than between single bands. Variations in canopy reflectances due to other environmental factors were reduced by the use of RVI. In the spectral variables examined, the RVI separated the treatments most effectively, and three or four treatments can be separated. Differences in spectral responses between the treatments were attributable to leaf area index, leaf chlorophyll concentration and phytomass, which all changed with N fertilization.

Quantitative Relationship between Leaf Area Index and Canopy Reflectance Spectra of Rice under Different Nitrogen Levels

Monitoring rice growth by spectral remote sensing technology can provide scientific basis for the high yield and efficient production of rice. Field experiments with different nitrogen application amounts using Tianyouhuazhan rice as test sam- ples were set up to study the relationship between rice leaf area index （LAI） and canopy reflectance spectral. The results showed that： the LAI increased with the amount of applied nitrogen; the canopy reflectance spectral showed significant re- sponse characteristics to groups with different nitrogen application levels; the corre- lation coefficient of LAI and canopy spectral reflectance reached the maximum at 720 nm red edge region. The mathematical model was constructed to predict the LAI according to the canopy reflectance spectra of rice.

Winter wheat biomass estimation based on canopy spectra

The winter wheat aboveground biomass is an important agronomic parameter to estimate the growth status,and evaluate the yield and quality.Spectrum technique provides a nondestructive and fast method for estimating the winter wheat biomass.In order to find the optimum model by analyzing the wheat canopy spectral characteristic during the whole growth period,field trails were conducted at the National Demonstration Base of Precision Agriculture in Beijing Xiaotangshan town.A portable spectrometer(200-1100 nm)was used to collect the wheat canopy spectra of different varieties at the different growth stages(green stage,jointing stage,booting stage,heading stage and filling stage),clipping the winter wheat at ground level at the same time.Regression and correlation analysis were used to establish the winter wheat biomass estimation models in this study.The results showed that the biggest different bands of the winter wheat canopy spectral reflection curves mainly lied along the blue and near-infrared bands.The spectral reflectance at 678 nm in the visible light range had the best correlation with the biomass(correlation=0.724).The monadic regression analysis,the multiple regression analysis and the partial least squares regression analysis were applied to establish the biomass estimation models,among which the partial least squares regression(PLS)model had higher modeling precision.The R2 of the calibration and validation were 0.916 and 0.911,respectively.The root-mean-square error(RMSE)of the calibration and validation were 0.090 kg and 0.094 kg(Sample area 50 cm×60 cm).The results indicated that the PLS model(400-1000 nm)could fully estimate the aboveground biomass in the whole growth period of wheat with a better measurement accuracy.

Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat

Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation.However,the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors.This study carried out research on the correlations among spectral parameters of the canopy of winter wheat,crop growth process,and soil water content,and finally constructed the soil water content prediction model with the growth days parameter.The results showed that the plant water content of winter wheat tended to decrease during the whole growth period.The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer.At different growth stages,even if the soil water content was the same,the plant water content and characteristic spectral reflectance were also different.Therefore,the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy.It is found that the determination coefficient(R^(2))of the models built during the whole growth period was greatly increased,ranging from 0.54 to 0.60.Then,the model built by OSAVI(Optimized Soil Adjusted Vegetation Index)and Rg/Rr,two of the highest precision characteristic spectral parameters,were selected for model validation.The correlation between OSAVI and soil water content,Rg/Rr,and soil water content were still significant(p<0.05).The R^(2),MAE,and RMSE validation models were 0.53 and 0.58,3.19 and 2.97,4.76 and 4.41,respectively,which was accurate enough to be applied in a large-area field.Furthermore,the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward.The research results could guide the agricultural production of winter wheat in northern China.