Study On Monitoring Of Paddy Rice Growth Based On Eos/Modis Data In Jiangxi Province

Objective, timely, accurate getting crop growth information has important significance in guiding the crop development scientifically. VI (vegetation index) calculating LAI (Leaf Area Index) is one of the main trends of monitoring the growth by remote sensing at present. LAI can reflect crop growth and development to a certain degree. VI is the most immediate reaction of LAI in remote sensing image. Considering MODIS data has the advantage of big coverage、high time resolution、higher spectral resolution、free access, etc, we select MODIS data to monitor crop growth. The method of monitoring rice growth is to build the model via combing remote sensing data and ground data for determining the best index to realize rice growth monitor.Main content of each part and conclusion is introduced as follows:(1) The dissertation calculated NDVI、EVI and LSWI and collected pixel which meet the condition that EVI is equal to LSWI plus0.05or less in use of transplant period image data. This was the first conditional function. We used the condition that EVI should exceed half of maximum value40days after transplant period as the second function. The condition that the reflectivity of blue band should be equal or greater than0.2was the recognition of cloud. So we could get rid of cloud from the image. Finally we obtained rice planting area.(2) The dissertation was about the monitoring index of the growth of paddy rice by remote sensing in use of MODIS data of Jiang’xi province in2010, drawing5vegetation index(RVI、 NDVI、VCI、EVI、SAVI)as remote sensing parameters. The vegetation indexes were selected to inverse LAI and vegetation index of LAI model was established.The study showed, among the vegetation index of LAI models, EVI and NDVI had significant correlations with LAI, and Cubic model of EVI was superior to the other vegetation and models, so that EVI was chosen as the final monitoring index for monitoring the growth.(3) When establishing agronomy index, through statistical analysis of LAI data of main growth period of paddy rice, exceptional value was rejected. Average value of growth index of different seedling situation was calculated. Within the plus or minus20%of LAI average value was as a standard, agronomy index of paddy rice growth was established preliminarily. Finally remote sensing monitoring index was established based on agronomy index and function equation of EVI and LAI.(4) According to the classification index of seedling situation, image of heading stage of paddy rice was selected to get the image of paddy rice seedling situation in Jiangxi Province. Seedling situation of test points ware marked in the image, and comparing it with practical seedling situation data of heading stage, we found out it basically met actual results. So seedling situation classification gotten by remote sensing image can reflect actual situation basically.(5)We joined the vegetation index and agronomy index LAI, combined remote sensing data and agronomy data, offered certain method for monitoring the crop growth, but relatively less data in test points may affect the model, we want to perfect the model of crop growth in the large-scale by collecting more ground data in the future work.