Contents of soil nutrients and soil organic matter of arable land will decrease gradually after high cropping intensity for several years. When a minimal amount of crop residue remains in farmland

significant decreased trends can be observed. Monitoring the dynamic changes in arable land utilization

specifically the dynamic identification of cropped and uncropped arable land

is important. The objective of this study is to monitor the dynamic change in arable land utilization by using time series of remote sensing data. Three major crop-producing provinces in Argentina（ Buenos Aries

Cordoba

and Santa Fe） are selected as our study area. Time series of MODerate-resolution Imaging Spectroradiometer（ MODIS） Sixteen-day composite Normalized Difference Vegetation Index（ NDVI） products at 250 m resolution is used. On the basis of an analysis of profiles of time series NDVI

SavitzkyGolay filters are used to smooth the noise in NDVI curves

and Lagrange polynomials are employed to extract the extreme points for the smoothed NDVI curves. A threshold method associated with NDVI curve analysis is used to identify dynamic changes in the distribution of cropped and uncropped arable land. Independent field samples are used to evaluate the accuracy of the classification using producer’s accuracy

user’s accuracy

overall accuracy

and Kappa coefficient derived from confusion matrix. Accuracy assessment results indicate that the proposed method can effectively identify whether arable land is cropped. The overall accuracy is above 97%. In regions with only a short period of time between harvesting one crop and sowing the following crops

the accuracy is approximately 95%. According to the analysis in this study

the error mainly comes from the Sixteen-day maximum value composite algorithm of MODIS NDVI products

which lose a low NDVI value during harvesting and sowing periods. In future

such applications will require higher spatial and temporal resolution NDVI data to obtain higher recognition accuracy of cropped and uncropped arable land. One solution could be to construct high spatial and temporal resolution NDVI datasets combined with high temporal and high spatial resolution images. A new method for monitoring arable land utilization is developed on the basis of time series NDVI data

and new products—dynamic changes in arable land utilization—are produced. The proposed method for identifying cropped and uncropped arable land by using time series NDVI data is applicable for regions with large farms. Extensive validation needs to be conducted in different regions to apply the proposed method to other regions.