Study On The Method Of Monitoring The Growth Of Winter Wheat With MODIS Data

With reform and opening-up of our country deepenning constantly and economy High-speed developing, population increasing constantly, cultivated land resources being reduced constantly, the safe problem of grain comes out prominently. All need growing situation information of the crop from national supreme policymaker to basic peasant household. Grasping growing situation of crop as soon as possible in different crop growing-up period is important than accurating to estimate crop cultivated area and output itself in certain cases, because monitoring growing situation information of the crop can make prewarning early to extensive grain shortage or surplus, instructing agriculture to produce further.The winter wheat are one of the most important cereal crops of our country, so, for large agricultural country with 1,300 million people, it is the major issue which concerns the national economy and the people's livelihood to monitor the growing situation to the winter wheat.With the development of" 3S " technology, especially the development of the agricultural remote sensing technology, the application of remote sensing image fast, prompt and dynamic monitoring growing situation of the assessment crop replace some old agronomy methods and become a kind of new technological means and main development trend.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 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 .This study is monitoring the growing situation of crop in shijiazhuang, heshui and xingtai region which in main producing region of Huai-Hai in use of MODSI day data in 2004, drawing 11 vegetation index (NDVI, RVI, DVI, EVI, IAVI, PVI , SAVI , TSAVI , RDVI , MSAVI and GEMI) as remote sensing parameter, analysing with ground data, setting up LAI - vegetation Index and LAI - growth Index model.Studies shown, in LAI - Vegetation index model, multianalysis linear model is better than unitary linear model, some unitary non-linear models is better than the multianalysis linear model, In unitary non-linear models we select 3 better vegetation index and 3 better unitary non-linear model to validate precision using ground, establish final LAI - Vegetation index model. S model of MSAVI, predicting to LAI that precision and accuracy on are all superior to the index of other vegetation Index and other models, R =0.918, so choose this model as final models icalculating LAI.LAI - growth Index model, stems in one mu instead of the density, calculating the growing situation of crop, choosing stems in one mu to build the mode, R =0.699, combine the value of LAI oneself, with the agronomy standard, can confirm the categorised grade of the winter wheat . So far, the vegetation index - LAI- ground index combining model is final setting up.Using MSAVI to calculate agronomy standard of winter wheat, we monitor the growing situationsof the winter wheat in the first ten days of April of 2004, combining the meteorological data, the model's accuracy is up to 70.58%.Regard LAI as media, we join the vegetation index and the agronomy standard, combine remote sensing data and agronomy data, offer certain method for monitoring the growing situation of crops, but error of the model exists inevitably, may affect the model in other range, we want to perfect the model of growing situation of crop in the large-scale by collecting more ground data in the future work.