Study On The Method Of Crop Pehnology Detection And Crop Types Discrimination Based On Modis Data

Because of the limit of natural and economic condition, the agricultural production strongly depends on the weather and climate. In this case, the food security faces great threat and it has attracted wide attention. It is very important to real-time monitor the crop growth and to make prediction of crop production for policy making and sustainable development. So the crop monitoring and prediction of production draws great attention all the time in not only developing country but also developed country. It is one of the most promising methods to make prediction of crop production with remote sensing.Terra/MODIS is a new remote sensing sensor. It can view the entire surface of the Earth in 36 spectral bands sampling the electromagnetic spectrum from 0.4 to 14μm with a spatial resolution ranging from 250 to 1,000 meters and high time resolution. So it has potential advantage in crop monitor on large-scale.How to separate crop information from the others and how to resolve the match and transformation between remote sensing information and agricultural information in monitoring crop growth are the next-step research when the information of the crop growth is extracted from remote sensing. In this paper, based on MODIS data of 2003 and 2004, phenological information of primary crop is extracted and crop type is identified in North China. With MODIS NDVI and EVI as parameters, together with non-linear formulation, the key crop phenological phases can be extracted and the remote sensing phenological monitoring index is contrasted with the land observation index to determine the matching relationship. Selecting the proper classification features, the primary crop types in North China could be identified through the analysis of the vegetation index time series in growth season. Main research results and initiatives in the thesis are as follows:1. Modeling the vegetation index curve respectively using a series of piecewise Logistic and Gaussian functions of time and extracting the key crop phenological phase through the methods of maximum curvature and dynamic threshold. The estimated phenological stages and statistical data are compared. The result shows that EVI time series is more efficient than the NDVI time series in estimating heading date. The method of maximum curvature gets better result than dynamic threshold. The two asymmetric functions fitting methods used to extract crop phenology are all successful.2. The phenology detected using MODIS time series is linked to statistical data. The relationship between crop development stages and temporal variation in satellite derived VI data is discussed. It was realized that the estimated phenology using MODIS time series is translated to statistic data. The comparison between the key phenological phases monitoring result and agricultural statistic data of winter wheat and summer maize development stages shows that the remote sensing monitoring result matches the agricultural statistic well. The remote sensing monitoring start phase of growth is...