Monitoring crop condition can provide near real-time information on crop growth and reflect diverse information on crop yield before harvesting. Time series clustering method was employed to improve crop condition monitoring in the Crop Watch global crop monitoring system. The scale suitability of methods in the Crop Watch system was evaluated. Yield data covering the 12 main producing states in India were selected to validate the accuracy improvement obtained by clustering method.Process,real-time,and time series clustering methods were employed in this research. All these methods were based on the comparison of Normalized Difference Vegetation Index（ NDVI）. Process method used the regional average of NDVI to develop crop growth profiles across the growing period and to compare the current profile with that of previous years or selected period. Real-time method used the difference of two NDVI images that represent different periods to identify the distribution of crop conditions,i. e.,poor,better,or maintain balance. Clustering method used a time series of NDVI,compared the time profiles of all the pixels,and categorized these profiles into different homogeneous types.Among the selected main producing areas in India,six states have consistent results achieved by real-time monitoring method and clustering method. The actual variations in yield can be explained clearly in the crop condition monitoring by the two methods.Clustering method obtains more accurate crop condition results than real-time monitoring in four states. The clustering results can better describe yield variation. On the contrary,real-time monitoring obtains more accurate crop condition in one state. Only one of the 12 selected states has an inaccurate crop condition description provided by both real-time monitoring method and clustering method. Clustering method is more accurate than real-time monitoring method in continuous monitoring and quantitative expression for the spatial distribution of crop condition.Process monitoring describes the regional crop growth across the growing period as a whole,whereas real-time method and time series clustering method can be used to show the spatial distribution of different crops. All these methods are consistent with one another in essence,but their scope suitability and aims are different. For provinces or smaller areas,process method performs well.For countries or continents,noises exist in the profiles because of the disturbance from crops in different areas. Real-time method can be applied to any scope to describe the regional crop growth difference in selected discrete time. As for the time series clustering method,it can be used to quantitatively describe crop growth and the distribution of corresponding types and performs better than real-time method.

crop condition;remote sense monitoring;NDVI;time series clustering;India