CloudSim: A fair benchmark for comparison of methods for times series reconstruction from cloud and atmospheric contamination

Abstract

Cloud contamination of optical data is a constant and annoying feature of time series analyses, whether while using vegetation indices or surface temperatures, since it tends to decrease artificially the values taken by these parameters. Therefore, any time series analysis of optical data needs a previous step for gap-filling reconstruction of the time series. Numerous techniques have been presented in the literature to carry out this preliminary and mandatory step. However, the evaluation and comparison of these techniques is difficult, since no “truth” time series is available. We present here a probabilistic model (CloudSim) to provide global typical annual time series for NDVI (Normalized Difference Vegetation Index) and surface temperature (ST) time series (both over land and sea) with and without cloud contamination. This model takes advantage of the newly released Long Term Data Record Version 4 (LTDR-V4) dataset, by estimating at the pixel level daily probabilities of cloud presence, as well as mean and standard deviations for both cloud and clear acquisitions of daily NDVI and ST. Therefore, we can simulate real time series of cloud contamination influence on NDVI or ST parameters. These simulated time series allow for the assessment of the validity and usefulness of any time series reconstruction method, as well as an objective comparison of the efficiency of different methods.