| Agricultural drought can lead to crop failures with large area,and can cause secondary disasters such as crop diseases and insect pests,has direct effect on agriculture and national economy and environment.Corn as the main crops of our country,needing to effective monitoring and evaluation of maize drought urgently.Remote sensing technology is the most promising technology,having advantages of fast,affordable and wide scope of effection.Vegetation index as one of the most important parameter of vegetation growth,having an effective monitor and estimate of crop drought situation and level.High resolution vegetation index time series data has obvious advantage in remote sensing monitoring drought application.It is not only can monitor the feature of changes of affected time,but also it can extract the effected area accurately and exactly.Owing to some factors just like: cloud pollution,satellite revisit cycle,the limit of sensor design,a single remote sensing sensor is difficult to obtain the images with high spatialand temporal resolution.It is urgently need fusion method to obtain high spatialand temporal resolution data.In this paper,we take northwest Liaoning corn planting area as an example,using spatial-temporal fusion model simulated high spatialand temporal resolution data from 2013 to 2014.The data has both MOD09A1 time resolution of MOD09A1 and spatial resolution of landsat.We have constructed the vegetation index time series curve based on fusion data.According to variations of cron vegetation index time series curves in no affected year and affected year,we extracted cron drought area of northwest Liaoning in 2014.The conclusions are as follows:(1)Each band of high time and spatial remote sensing data that we have built has good applicability.The R2 of temporal spatial fusion model simulated bands and on real landsat bandsare approximately above 0.7,RMSE below 0.02.In the corn area,the R2 of NDVI and EVI all above 0.7,RMSE below 0.02,the number of P below 0.01.Simulation and real data has a good consistency.(2)High spatial and temporal data can extract the planting area of corn effectively.Using vegetation index time-series data based on fusion data,we can make refined classification in the study area,the overall precision is 90.03%,the coefficient of Kappa is 0.8768,the user precision of corn is 92.22%,the mapping accuracy is 87.56%.(3)The EVI time series curve of drought corn showed bimodal or abnormal unimodal characteristics.The first state of the affected corn sequence curves showed a bimodal characteristic.EVI will significantly decreased after drought,then there will be a slow rise;The second state of affected corn sequence curves show a abnormal single peak;Compared with no affected corn the EVI decreases significantly after experiencing a drought and EVI has no obvious rise. |
