A Model on Monitoring Nitrogen Content and Growth Status of Wheat Based on Hyperspectral Remote Sensing in Temporal and Spatial Changes
|Keywords||Wheat Hyperspectral Temporal and spatial variation Nitrogen Nutrition Growing parameters Model|
Objective: Nitrogen Nutrition on crop yield and quality have an important role, not suitable for the application of nitrogen will result in a waste of resources and ecological environment. Quality and efficient production of wheat is an urgent need of China's agricultural development, in which nitrogen is an important factor affecting the yield and quality of wheat. Timely and accurate rapid monitoring of the temporal and spatial variation characteristics of wheat nitrogen nutrition wheat precise nitrogen management. Hyperspectral remote sensing techniques in irrigation and crop water, fertilizer, nutrition and growing monitoring showed a strong advantage, is the main way to achieve the required timing precision agriculture quantitative monitoring crop conditions. At present, the use of hyperspectral monitoring of crop nitrogen nutrition and growing conditions also conducted a lot of research to determine NITROGEN NUTRITION monitoring sensitive band and build a nitrogen monitoring spectral parameters and spectral model. Canopy spectra, however, is a mixed spectrum of the various organs of the wheat groups, different growth stages of leaf nitrogen content, also showed the distribution of the spatial structure. Based on canopy nitrogen distribution in time and space characteristics, research wheat in different temporal and spatial changes in canopy spectral groups nitrogen content and growing parameters, appropriate screening of feature parameters and vegetation index, the establishment of wheat nitrogen Nutrition diagnosis and growing monitoring model. Nitrogen and growing level of remote sensing estimation, provide the basis for the use of satellite imaging spectrometer data for crop monitoring. Methods: determination canopy spectra of different wheat varieties and nitrogen levels, use of biometric analysis, spectral analysis method for quantitative analysis of the wheat canopy spectral reflectance characteristics between the derivative spectra characteristic differences and agronomic indicators canopy spectral establish the appropriate regression model; synchronization model validation and accuracy test. Results: The results showed that the relationship between nitrogen fertilizer and dry matter, building nitrogen nutrition index (NAI) under different nitrogen levels, wheat pre-plant canopy reflectance and NAI good correlation between best spectral index red edge inflection point (REP) performance. Nitrogen abundance (NR) is a reflection of the composite indicator of the wheat groups nitrogen and growing conditions, late in the wheat canopy reflectance and nitrogen abundance have reached a significant level (a = 0.01), in which the spectral index RVI and The VD672 best performance. Therefore, NR and NAI reaction Wheat and growing conditions, its plant canopy spectral good correlation can be established based plant the canopy spectra wheat pre, mid and late Nitrogen Nutrition Diagnosis and growing monitoring model. For bloom parameter monitoring, chlorophyll, leaf dry weight and leaf area of ??wheat based on a comprehensive reflection of the growing field conditions. Selected spectral parameters good correlation are concentrated near the red edge in the feature band seedling to tillering stage of wheat canopy spectral reflectance and chlorophyll content, leaf area index and leaf dry weight poor correlation, jointing stage to the filling of a good correlation, red edge near the spectral parameters VOG2 and RVI can on winter wheat jointing to monitor during grain growing. Based on the spatial variation of wheat leaf nitrogen content monitoring, determination of whole wheat growth and development of the mid-canopy reflected spectrum, spectral parameters RVI higher viewing angle (0 °) and leaf nitrogen content. Therefore, the use of the observation angle (0 °) Determination of canopy spectra can be well predicted leaf nitrogen content.