Comparison of cloud-reconstruction methods for time series of composite NDVI data

Land cover change can be assessed from ground measurements or remotely sensed data. As regards remotely sensed data, such as NDVI (Normalized Difference Vegetation Index) parameter, the presence of atmospherically contaminated data in the time series introduces some noise that may blur the change analysis. Several methods have already been developed to reconstruct NDVI time series, although most methods have been dedicated to reconstruction of acquired time series, while publicly available databases are usually composited over time. This paper presents the IDR (iterative Interpolation for Data Reconstruction) method, a new method designed to approximate the upper envelope of the NDVI time series while conserving as much as possible of the original data. This method is compared quantitatively to two previously applied methods to NDVI time series over different land cover classes. The IDR method provides the best profile reconstruction in most cases. Nevertheless, the IDR method tends to overestimate low NDVI values when high rates of change are present, although this effect can be lowered with shorter compositing periods. This method could also be applied to data before compositing, as well as to reconstruct time series for other biophysical parameters such as land surface temperature, as long as atmospheric contamination affects these parameters negatively.