Study On Variable Nitrogen Topdressing Decision Based On Spectral Diagnosis Of Winter Wheat Seeding Growth

The crop growth is a comprehensive manifestation of factors such as basic ground force,water and fertilizer profit and loss,and meteorological conditions.Nitrogen fertilizer is not only a nutrient source for winter wheat growth,but also a source of pollution for agro-ecosystems.Therefore,we need to accurately manage nutrients and improve the efficiency of nitrogen fertilizer utilization by modern agriculture.With the rapid development of spectral velocimeter,unmanned aerial vehicle and satellite remote sensing technology in recent years,the spectrum diagnosis technology based on different platforms has also made great progress,and initially realized quantitative,rapid and objective spectral diagnosis of population parameters,which also provides reliable technical support for decision-making of nitrogen fertilizer in key growth stages based on real-time dynamics of seedlings.As the growth of winter wheat is fast,accurate field variable fertilization decision is inseparable from the real-time and accurate acquisition of seedling information,and the simple and simplified variable nitrogen fertilizer decision-making mode.Therefore,it is necessary to develop a set of point-and-face combined winter wheat seedling diagnosis and variable nitrogen application technology system based on the characteristics of multi-platform spectral information acquisition and the regional differences in winter wheat production.We selected the winter wheat production county,Gaocheng District,Luquan District and Jinzhou City as the research area.With the support of years of nitrogen fertilizer control experiments and variable nitrogen tests,a complete set of experimental data,including canopy spectra,agronomic parameters,production,meteorology,soil and remote sensing images,was obtained.The spectral response of key agronomic parameters of winter wheat under different nitrogen levels was analyzed,and the dynamic monitoring model of winter wheat growth time series with effective accumulated temperature was constructed to solve the regional and inter-annual differences in winter wheat seedling diagnosis.The dynamic changes of leaf area index(LAI)and normalized red edge index(NDRE)of winter wheat under different target yields were analyzed,and the suitable growth curves of winter wheat LAI and NDRE under high-yield target were fitted.Combined with yield level,suitable and measured growth index,we proposed winter wheat variable nitrogen topdressing decision-making models based on suitable LAI and NDRE to realize the winter wheat topdressing decision based on seedling diagnosis at any time point;and compared with the maximum LAI method.Finally,combined with the satellite remote sensing image of the key growth period,the winter wheat variable nitrogen application decision-making technology system combining land size and regional scale is realized.The main conclusions are as follows:(1)In this chapter,the relationship between the field speed-measure NDRE and the effective accumulated temperature and actual yield under different nitrogen application levels was analyzed.The growth time series dynamic model of winter wheat growth based on the combination of relative NDRE and effective accumulated temperature was constructed.The model R2 of the three nitrogen application levels in Gaocheng experimental area was 0.96.0.94,0.95,respectively,and the lowest RMSE were 0.05,0.06,0.06,respectively.The model was verified by Jinzhou and Luquan test data,and high precision was obtained,which indicates that the model has high universality and accuracy.The RNDRE dynamic model coupled with the effective accumulated temperature as an adjustment parameter can accurately reflect the dynamic status of winter wheat seedlings in real time,and makes up for the shortcomings of independent modeling in different growth stages in the past,and is applicable to different regions,sowing dates,varieties,and top dressing time.(2)In this chapter,we constructed the suitable dynamic curve of winter wheat growth index under high-yield target,and proposed winter wheat variable nitrogen topdressing decision-making models based on suitable LAI and NDRE.Compared with the maximum LAI method,we analyzed the practical application effects of three variable nitrogen topdressing decision-making models and achieved the variable nitrogen topdressing prescription chart of combined land size and regional scale.The measurement results of the CGMD302 crop growth monitor used in this study are in good agreement with the ASD hyperspectral apparatus,and the coefficient of determination is better than 0.70;the nitrogen application level has a great influence on the growth of winter wheat seedlings.NDRE,LAI,biomass,plant nitrogen content and other parameters showed a change of 0.5 times N<1 times N<2 times N.The suitable dynamic curve of the growth index(LAI,NDRE)of winter wheat with the effective accumulated temperature can effectively avoid the interference of sowing date and meteorological conditions on the spectral diagnostic accuracy of the seedling.Due to the early spring drought in Hebei,the maximum LAI is not suitable for the nitrogen topdressing in the weak state of the seedlings,while the suitable LAI and suitable NDRE variable nitrogen topdressing model can effectively realize the quantitative topdressing of the winter wheat in the research area.On the one hand,it saves the investment of nitrogen fertilizer;on the other hand,it Improves fertilizer efficiency and effectively raises the winter wheat yield.The field speedometer CGMD302 achieved the decision-making guidance of the winter wheat variable nitrogen-seeking decision on the plot scale.At the same time,with the support of satellite remote sensing images,a remote sensing inversion model of winter wheat leaf area index was carried out.Combined with the appropriate LAI nitrogen-topdressing model,the county-level winter wheat nitrogen-topdressing prescription map was produced,and the nitrogen-topdressing prescription maps of point-and-face combination can provide reliable management of winter wheat field management for grassroots agricultural managers.