Latif, BA, Lecerf, R, Mercier, G, Hubert-Moy, L (2008). Preprocessing of low-resolution time series contaminated by clouds and shadows. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 46(7), 2083-2096.
Monitoring changes in the vegetation cover during the intercrop season is of special interest in intensive agricultural region, such as the Brittany region in France, to locate bare soils and control their influence to the environment. The presence of bare soils leads to detrimental environmental effects such as soil erosion or water quality degradation. Therefore, identification and monitoring of bare soils at a regional scale in the winter season are required for any agricultural management program. Data from the Moderate Resolution Imaging Spectroradiometer have been selected for this paper due to their low spatial resolution, which decreases the cost of processing and storage, and the high revisit frequency, which increases the probability to acquire scenes free of clouds and shadows during the winter season. Unfortunately, few images per season only are free of cloud contamination and associated shadows. Therefore, the specific objective of this paper is to develop and implement a preprocessing method to recover spectral values of contaminated data by weather conditions for subsequent bare-soil mapping. In the context of this paper, Kohonen's self-organizing map (SOM) is used for recovering data contaminated by weather conditions that have been considered as erroneous data. This nonparametric regression procedure has proved its success to deal with missing-values problem. Hence, the erroneous-values problem, reflectance values contaminated by clouds or shadows, has been converted to the missing-values problem by using a cloud and shadow (outlier) detector. The SOM algorithm was tested also on the erroneous data directly, but better results were found with the missing values formulation. The idea is to, first, train SOM onto clear temporal profiles free of clouds and shadows during the winter season. Second, erroneous values are converted to missing values by an outlier detector which operates on each temporal profile (set of colocated pixels acquired at different dates). Finally, the SOM algorithm for missing values is used to estimate contaminated reflectance values.