Diagnosis Of Nitrogen Nutrition Statu And Growth Monitoring Manage System Of Rice Using Satellite Remote Sensing Image

Remote sensing as cutting-edge technology of modern information system that can quickly and accurately obtain a large area real-time crop nutrition and growth status information to implement the precision agriculture provide an important technical support for help crop high production, high efficiency, high quality and other. This study mainly to explore the possibility of using GF-1 satellite image to estimate growth parameters and nitrogen status of rice at jointing stage, and develop estimating and diagnosis model. Meanwhile the expert system of rice growth monitoring and nitrogen diagnosis was developed with ArcGIS and ENVI.The canopy hyperspectral information of rice at elongation stage in different nitrogen treatments experiments plot. The relationships and error information between aboveground biomass, plants nitrogen content, plants nitrogen accumulation and per unit yield with vegetation indices, which were simulated by the canopy hyperspectral information and the response spectral parameters of GF-1 WFV satellite sensor, were used to verify the viability of using GF-1 WFV images to monitor rice growth status and diagnose nitrogen nutrition. According to the result of the simulation experiment, the relationships between growth parameters of the rice field samples at joint stage with vegetation indices, which were calculate by spectral parameters of real GF-1 WFV images, were used to verify the first conclusion. And then three different modeling methods were comparison with each other, and the optimal model was used to perform inversion of aboveground biomass, plants nitrogen content, plants nitrogen accumulation and nitrogen nutrition index. Finally, using the in-season per unit area yields to verify the diagnosis and give fertilizer recommendations.The results showed that the correlation of using the GF-1 WFV indices, which were simulated by canopy hyperspectral data of rice at jointing stage, and growth parameter of rice with different nitrogen treatment was very well and the coefficient of determination R2 of aboveground biomass, plant nitrogen content, plant nitrogen uptake and yield with vegetation indices were 0.74,0.61,0.66 and 0.62, respectively. This conclusions could demonstrated that the data of GF-1 satellite WFV sensor can be used as important data sources of monitoring growth parameters and nitrogen status of rice at jointing stage. Three different modeling methods called as stepwise multiple linear regression, BP neural network regression and random forest regression was compared with each other, the coefficient of determination R2 of aboveground biomass, plant nitrogen content and plant nitrogen uptake with vegetation indices were 0.82,0.57, and 0.79, respectively. The results showed that random forest algorithm regression model can effectively improve the model predictive power, and so that random forest can be use as primary method to calculate growth parameters. Finally, the nitrogen nutrition status of rice was diagnosed by nitrogen nutrition index. The coefficient of the practical yield of each rice fields at farm and nitrogen nutritional index was showed significant quadratic correlation. The result indicating that nitrogen nutrition index can predict the yield of rice at jointing stage and can diagnose rice nitrogen lack, suitable and excess states. In short, GF-1 satellite WFV data is capable to inversion growth parameters and can accurately diagnosis Nitrogen Nutrition Status of rice. In addition, it can provide the theoretical support for rice production management.The remote sensing information system of crop growth monitoring and nitrogen diagnostics with practical and business-oriented was developed by ArcGIS Engine, Server and IDL development platform and using the aerospace-ground remote sensing data as the information source. This integrated system has a file management, image processing, feature identification and classification, vegetation index, growth index calculation, the physiological parameter estimation, yield and quality indicators forecast, ground remote sensing monitoring, diagnosis and dynamic growth regulation, and system management tools, such as help functions. Finally, the system was validated and give an analysis case of Shengnong farm. The results showed that the design and structure of the system framework in accordance with operational requirements of crop growth monitoring and diagnosis system, system operation is simple, intuitive display results, test results and actual field has a high degree of compliance to achieve a crop condition monitoring and diagnostic technology exact and digitization.