Spatial-temporal Distribution and Developmental Potentials Analysis on Multiple Cropping Index in Shaanxi Province from 2000 to 2012
Multiple cropping index (MCI),which is an index for monitoring cropping systems,can be extracted by NVDI time series.Due to cloud contamination or other disturbance,NDVI time series are preprocessed to remove or reduce noise.We interpolate the NDVI value of NDVI time series if it is considered as cloud or ice/snow which can be detected by using pixel reliability summary QA layer.Then we use Savitzky-Golay filter to smooth the NVDI time series and find that there are some interference peaks.After observing the NDVI time series of some sample pixels,we make two rules to get the statistic number of peaks.Then combining Shaanxi Province's land-use map and meteorological data,we get the Shaanxi's MCI and multiple cropping potential from 2000 to 2012,using the methods including trend analysis,slope index and standard deviation.The results show that ① The spatial differ ences of the MCI are significant.The latitude zonal laws were obvious for the multiple cropping index spatial distribution.Generally speaking,the MCI of Guanzhong is the greatest and the MCI of Southern Shaanxi is greater than Northern Shaanxi.② The spatial differences of the inter-annual change of MCI are also significant.The MCI Standard deviation of the most areas of Loess plateau in Northern Shaanxi is less than 0.15.The standard deviation of MCI in most area of Guanzhong Plain,Hanzhong Basin and Ankang Basin are ranged between 0.15 and 0.2.The standard deviation of MCI in Feng County and Taibai County are less than 0.15.The standard deviations of MCI are greater than 0.2 in other part of Shaanxi Province.③ The average of MCI in Shaanxi is 160.32％ and the average of multiple cropping potential in Shaanxi is 184.29％.The multiple cropping potential of Southern Shaanxi is the greatest,and the multiple cropping potential of Northern Shaanxi is greater than Guanzhong Plain.
MODISmultiple cropping indexmultiple cropping potentialspatial-temporal distributionShaanxi Province
国家科技期刊平台
NSTL
万方数据
维普
