Study On The Time Series Of Quantitative Remote Sensing Of Winter Wheat Growth

China is a large agricultural country,and wheat is a major food crop.With the development of remote sensing technology and the development of precision agriculture,timely,accurate and dynamic monitoring of agricultural crop growth,quantitative assessment of growth,and production forecast have become a national or regional development.Appropriate agricultural policies and the majority of farmers provide important scientific decision-making basis.The growth monitoring is mainly based on the combination of terrestrial data and remote sensing satellite data to establish a model and invert crop growth indicators.Leaf area index LAI(Leaf Area Index)is a variable closely related to the characteristics of the growing individual and the characteristics of the population.Its size is directly related to the final yield.Chlorophyll is a substrate where crops carry out photosynthesis and convert light energy into dry matter.The chlorophyll content of crops affects the rate of photosynthesis of crops,which in turn affects crop yield.The monitoring of chlorophyll content can provide a basis for forecasting crop growth and yield.Nitrogen promotes the increase of leaf area index,chlorophyll content and growth rate.At the same time,water is a reaction substance in the metabolic process.Water participates in photosynthesis,respiration,and organic matter synthesis and decomposition.The parameters play a key role in the growth of crops,so crop growth monitoring can be achieved through the inversion of vegetation parameters.This paper takes the winter wheat at Longyue Farm in Handan City,Anhui Province as an example.Based on domestic and foreign satellite data with medium-resolution and field experiments,the vegetation parameters(LAI,chlorophyll,nitrogen,and moisture)related to the growth of winter wheat are counteracted.The performance of time series monitoring and yield forecasting of winter wheat in the study area was achieved.The main work and conclusions of this paper are:?: The radiative transmission mechanism of the vegetation spectra was studied.The LAI,chlorophyll,nitrogen,and the sensitive spectral bands and spectral indices of the leaf area index were analyzed.The VIopt,RVI II,SR705,MTCI,RVI,GNDVI,OSAVI,TCARI,NDVI,and ND705 were selected.Ten vegetation indices that are sensitive to leaf area index and eight chlorophyll and nitrogen-sensitive vegetation indices,SR I,RVI I,VIopt,RVI II,SR705,RM,MSR705,and MTCI,were selected based on previous studies.The NDWI vegetation index inverts the moisture content.?: Based on the selected vegetation spectral index and the band characteristics of the selected satellites,the vegetation index of each satellite data is calculated,a remote sensing inversion model for vegetation parameters of different satellite sensors is established,and the optimal coefficient of each satellite is selected using the determination coefficient R2 of the model.The vegetation parameter model was used to verify the in-phase satellite inversion using surface measured data,and the accuracy of the model was analyzed.The chlorophyll density inversion model based on RVI II,the nitrogen density inversion model based on VIopt,and the NDVI-based model were found.The inversion model of leaf area index and the equivalent water thickness model based on NDWI are distributed uniformly on both sides of the y=x line.Therefore,the model constructed in this paper is reasonable.?: Through the previous part of the research,based on the obtained inversion results of vegetation parameters of long-time sequences of winter wheat in the study area,the growth status of different stages of winter wheat was analyzed,and combined with the yield data,the differences in the growth of winter wheat between different yields were analyzed.High-yielding fields can survive the wintering season better than the low-yielding fields,and they are able to withstand the cold,providing a good basis for the selection of late crops.Through the research literature and the above research,it was found that NDVI is the leaf area index sensitive vegetation index.In this paper,the NDVI products from September 2016 to July 2017 in the study area were used to study the association characteristics of the NDVI of the leaf areasensitive vegetation index(NDVI)at different growth stages with the harvest yield of the corresponding winter wheat,clarifying the performance of NDVI in estimating the yield during the single growth period,and establishing the estimated yield.The model analyzes and compares the accuracy and universality of each model.The results showed that the optimal growth period based on NDVI was different from the heading date,and the coefficient of determination(R2)of the model was 0.7211.At the same time,from the yield forecast map based on the model,it was concluded that the growth of wheat in the middle and southeast of the study area was general.Due to early and late cropping of wheat on farms,late-winter wheat is found in central and southeastern parts of the country,and fertility progress is slow.The wheat in the northwest has a strong overall growth and high yield.