Research To Growth Monitoring Indicators Of Winter Wheat Based On Remote Sensing

Our country is the largest country of population. With the speed increasing of population and sustained improving of people's living standard, demanding of agricultural products keeps increasing. Wheat is an important food crop in our country. With the swift development of"3S"(GIS RS GPS) technology, demand of domestic precision agriculture's rising and developing, timely, precise, macroscopic and real-time monitoring the growth of crops and technology of growth quantitative evaluation have become basis to make scientific decision for government departments and peasant, but also is key to promoting the scientific management of agriculture and guaranteeing the grain production.The method of monitoring growing situation of crop is to build the model via combining remote sensing data and ground data, calculate the growing situation of crop. LAI(Leaf Area Index)is a variable, it is relevant with individual characteristic and colony characteristic of growing situation, the vegetation index calculating LAI is one of the main trends of monitoring the growing situation of remote sensing at present.The purpose of this study is to establish indictor collection of remote sensing growth monitoring for Henan winter wheat. The study is taking Henan winter wheat for example based on the polar orbit satellite (FY-3A and MODIS) datum, combining with field controlled experimentation, combining simulation model for winter wheat growth and development with remote sensing and GIS technology, accompany with agrometeorological observation and investigation. The main content and conclusions of this study are as following:1. Review the research and application of crop monitoring technology based on remote sensed data in winter wheat home and abroad.2. We conducted surface investigation and research of winter wheat growth and formulated monitoring plans. Besides, we established surface observation databases.3. Delimit first order cotyledon, second order cotyledon and third order cotyledon's data range by average and variance of three grade of winter wheat's crop height, tiller number, LAI and biomass.4. Calculate the cotyledon monitoring indicators of vegetation index, such as NDVI, DVI, RVI and EVI. Then delimit the cotyledon of winter wheat into three grades after calculate the average and variance of NDVI and EVI.5. Choose 15 surface observatory sites and remote sensing pixels and extract their LAI and biomass. Using the least-squares algorithm to build statistics relationship between surface observation data and RS inversion which can be used to correct the RS inversion result. Besides, using the least-squares algorithm to build statistics relationship between biomass can be measured directly.6. Using weighting method based on the cotyledon monitoring indicators of LAI. Values assigned the first order cotyledon can range from 2.5~4.5, second order cotyledon 4.5~6.5 and third order cotyledon >6.5. Base on this, display the comprehensive thematic map of winter wheat cotyledon situation.The main results are as follows: NDVI is more sensitive than other vegetation index in monitoring the total growth period; EVI is more sensitive than other vegetation index in specific growth on different temporal and spatial conditions. Besides, LAI inversed by NDVI has adequate accuracy to display the thematic map of winter wheat cotyledon situation.