A land cover-driven approach for fitting satellite image time series in a change detection context

Thanks to the freely availability of several Satellite Image Time Series (SITS) covering the Earth, it is now possible to monitor and analyse Land Covers (LC) and Land Cover Changes (LCC) on a yearly or even longer time span. Such applications are relevant in the context of Climate Change (CC), where consequences of the changes can only be seen on long term. Nevertheless, SITS suffer from atmospheric condition related problems (when talking about passive sensors) that reduce the temporal resolution of images in SITS. Several methods have been proposed in literature to mitigate these problems, and are placed under gap filling or SITS fitting methods. Such methods generally work with a single feature, being it a radiometric index or a spectral band. The use of multiple features is limited to specific single LC class or satellite sensor, limiting its usage in LCC and CC. Thus, in this paper, we propose an approach that is automatic, and both LC and feature independent. Here we propose the use of Normalized Difference Indices (NDI), with combination of all available spectral bands. The proposed approach uses a dropout upper-envelope strategy to reconstruct SITS trends, based on a set of rules, and guarantees a smoother closer trend to that of the original data. The proposed approach has been applied over two regions (Amazonia and Saudi Arabia) in the period 2013-2017, and has been compared to other fitting methods: Cubic Splines and Univariate Splines. It has been further evaluated by detecting LCC with long SITS methods such as Breaks For Additive Seasonal and Trend (BFAST). The preliminary results are promising demonstrating the robustness of the approach across different LCs and across different features.