Cropland identification in inner Mongolia, China with SPOT-4 VEGETATION imagery

Detecting the situation of cropland in the semi-arid area, north of China is very important for agricultural management, land degradation and ecosystem assessment. In this study we tried to explore the potential and the methodology for the cropland identification using high temporal resolution SPOT-4 VEGETATION (VGT) imagery and some ancillary data. The imagery used in this study were 10-day composite Normalized Difference Vegetation Index (NDVI) over the year 2000 obtained by the Maximum Value Composite (MVC) technique, and the ancillary data included phonological calendar and agricultural knowledge. The discrete Fourier transform was applied to the NDVI data set on a per pixel basis for the whole study areas and then the additive and the first four harmonics (amplitude and phase) were classified using ISOLATE unsupervised classification algorithm. Analysis of the characteristics of NDVI temporal profiles reconstructed from the first four harmonics and the phonological calendar over the plant growing season allowed for the identification of distinct growth patterns between the different vegetation types. The accuracy of the result was evaluated with the agricultural census data and existing land-cover dataset derived from TM images. The result of this study shows that the methodology used in this study is, in general, feasible for cropland identification in semi-arid area of north China.