Monitoring Wheat Growth and Powdery Mildew Using Multi-source Remote Sensing Images
|School||Anhui Agricultural University|
|Course||Crop Cultivation and Farming System|
|Keywords||winter wheat growing powdery mildew remote Sensing|
China is the world’s first large agricultural country. Population growth is anurgentence needing for the increase in the number of agricultural products.Wheat is one ofChina’s major food crops, high and stable yield is the key to ensure that the foodproblem.With China’s entry into the digital age of agriculture, timely and accurate,low-cost monitoring of wheat are growing and pest outbreak situation not only meet theinformation needs of all levels of agricultural management departments, but also for themajority of farmers and agriculture-related enterprises to provide important referenceinformation, timely implementation of scientific management measures to ensure thewheat harvest yield.In this paper our study is about using multi-source remote sensingtechnology in crop production,with wheat as the main object,consolidating the daejeonbasic agricultural information, geographic data, and multi-source, multi-temporal remotesensing data, and disease around the growing of wheat were studied remotesensing,focusing on the remote sensing research of the wheat growing and disease,Themain contents are as follows:1.The use of multi-temporal remote sensing data to monitor changes in winter wheatleaf area index.Taixing City,Jiangsu Province,for example,We use multi-temporal Landsat/TM remote sensing data, based on the spot sampling and survey by GPS and establishedinterpretation signs,used remote sensing image correction, interpretation, unsupervisedclassification and other operations, combined with the measured experimental samplesGPS data tested and corrected wheat acreage accuracy.We monitor different periods ofmultiple growth stages of wheat growing by using vegetation Index Leaf Area IndexInversion, and product the winter wheat leaf area index grading chart.2. The use of winter wheat biomass model (APBW) through environmental Star(HJ-1) forecast of winter wheat biomass.On the base of wheat biomass formationphysiological processes, the use of quantitative modeling techniques,wo establish a crop ofwheat biomass estimation model.We amendments the biomass model parameters timely byusing the LAI inversion of remote sensing images of winter wheat heading in Taixing,thenwe forecast winter wheat biomass of XinHua by the revised parameters and model thenproduct winter wheat biomass prediction of thematic maps, obtain more good estimationresults.We constructed wheat biomass estimation model on the base of the organicaccumulation process of wheat though photosynthesis, coupling remote sensinginformation to revise and enhance the mechanistic, interpretative and applicability of remote sensing estimation of the wheat biomass.3.The relationship between physiological and biochemical indexes of winter wheatand canopy spectral characteristics.Wheat growing similarities and differences mainlyfrom changes in the physiological and biochemical characteristics.We studied therelationship between the spectral parameters of the different vegetative wheat leaf canopyand the biochemical composition of wheat (chlorophyll, nitrogen) and physiologicalindicators (water content) through the relevant methods of analysis then establishedphysiological and biochemical indexes of wheat remote sensing estimation model.Wemonitoring rapidly of a variety of physiological and biochemical of different varieties ofwheat vegetative leaves by using the estimation models, Also distinguish different varietiesof wheat growing conditions based on the characteristics of the blade hyperspectral.4.HJ satellite remote sensing-based winter wheat powdery mildew monitoring.Wesurveyed flowering wheat powdery mildew condition of the different county region,analysed of the relationship among the number of powdery mildew,growing and climatefactors, defined several main parameters that have an important impact factor of thedisease index (leaf area index, leaf chlorophyll content and leaf water content and airtemperature).the parameter factors and disease index (DI) were multipleregression,Establishment of powdery mildew disease index estimation model,on this basis,production of wheat powdery mildew disease index of remote sensing thematic maps.