Remote Sensing For Maize Nitrogen Nutrition Diagnosis Based On Field Experiments And The DSSAT Model

Nitrogen is an essential nutrient in crop growth and development,closely related to crop vigor,yield,and quality.The rapid,macroscopic,accurate,and timely diagnosis of crop nitrogen nutrition status using remote sensing techniques has always been a research focus.This study is based on a field nitrogen fertilizer gradient experiment and combines the DSSAT model to establish a critical nitrogen dilution curve for maize.Remote sensing spectral data is used to estimate plant nitrogen concentration and aboveground biomass,and the two are combined to construct a nitrogen nutrition remote sensing diagnosis model based on the nitrogen nutrition index（NNI）as the diagnostic indicator.On this basis,the quantitative estimation of nitrogen demand of maize was realized.The nitrogen nutrition status of maize plants in the Changchun planting area is diagnosed using the nitrogen nutrition diagnosis model,and its spatiotemporal variation characteristics are analyzed.The main work and conclusions of the paper are as follows:（1）Establishment of critical nitrogen dilution curveTo generate a more accurate and effective critical nitrogen dilution curve for experimental maize,the DSSAT model is combined with the method of establishing critical nitrogen concentration.Based on the localized DSSAT model and simulated PNC（Plant Nitrogen Concentration）and Biomass data,the critical nitrogen dilution curve formula for maize plants from the jointing stage to the milk maturity stage is generated as Nct=5.238*Bio^-0.573,with a construction accuracy（R²）of 0.95.The nitrogen utilization efficiency parameter value is estimated to be approximately 62%based on simulated soil nitrogen change data.（2）Construction of nitrogen nutrition remote sensing diagnostic modelThe remote sensing diagnostic model of nitrogen nutrition in maize plants is the result of the combination of the remote sensing inversion model of model parameters（PNC and Biomass）and the established critical nitrogen dilution curve formula.In the remote sensing inversion of PNC and Biomass,the correlation between vegetation indices and PNC/Biomass is analyzed.The vegetation index with the highest correlation coefficient is combined with a regression model to establish remote sensing inversion models for PNC and Biomass at different growth stages.The R²of each model is above0.67.The results are then incorporated into the nitrogen nutrition diagnosis model to achieve remote sensing diagnosis of maize plant nitrogen nutrition status.The diagnostic accuracy（R²）of each growth stage model is above 0.79,enabling effective estimation of maize nitrogen nutrition status at different growth stages.（3）Construction of nitrogen demand estimation modelThe regression model of plant nitrogen demand under different growth stages of maize was constructed by using the results of nitrogen nutrition remote sensing diagnosis,and the model accuracy R² was greater than 0.77,which could realize the quantitative estimation of large-scale nitrogen demand in maize.Finally,based on the nitrogen nutrition remote sensing diagnosis model,the nitrogen nutrition status of maize plants in Changchun area is diagnosed.The results showed that maize plants were well developed in most areas of Changchun area,with sufficient corn nitrogen fertilizer,and nitrogen deficient areas were mainly distributed in parts of southwest Nongan,and the amount of fertilizer that needed to be supplemented was 20kg/ha-40kg/ha.