Advances in rice planting area extraction technology based on MODIS data

The changes of remote sensing data source,character index and temporal selection of remote sensing data and the development of remote sensing classification methods were summarized.The advances and development direction of applying MODIS image to extraction of the rice planting area were analyzed.The results indicate that MODIS data have many advantages such as high spectral,and temporal resolution,multi-temporal,cost savings compared with other remote sensing data,and it can improve the identification and monitoring accuracy and efficiency in rice planting area estimation in a large scale area.Application of MODIS data in rice planting area estimation has achieved good effects.The rice is identified and the planting area is extracted using the spectral bands sensitive to water bodies and vegetation or vegetation indices such as NDVI,LSWI and EVI and so on.Thus,the optimum time extracting rice planting area should be the transplanting period and booting stage of paddy.The traditional classification methods such as supervised classification and unsupervised classification are simple and are widely used.The new method developed recently could improve the accuracy of classification,such as decision tree,expert system,neural network and support vector machine methods that can extract the target objects more accurately.In the practical application,these methods are usually used in combination with traditional classification methods.The multi-temporal analysis method combines with high time,high resolution and high multi-spectral image in order to get crop planting area in a higher precision.Compared with the traditional method,the effect of classification improves greatly.For single or large rice planting area,the accuracy of rice planting area extraction is higher based on MODIS data,while it is lower for fragile plots.The accuracy could be improved if the additional data such as elevation and slope etc.are added and the multi-temporal classification methods are combined.