Abstract
Time series reconstruction methods are widely used to generate smooth and gap-free time series using imagery acquired at coarse spatial resolution and high frequency return intervals. However, as interest has grown in leveraging the nearly 40-a record of Landsat to study long-term changes in terrestrial ecosystems at 30-m spatial resolution, new methods are required to reconstruct time series of Landsat imagery, which have lower temporal density than coarse resolution sensors such as AVHRR or MODIS. To address this need, we introduce a dynamic temporal smoothing (DTS) method that reconstructs sparse and noisy signals into dense time series at regular intervals. The DTS is a weighted smoother with parameters that adjust dynamically to variation in time series and can be applied to both dense and sparse time series measurements. Because the DTS smoother we describe is specifically designed to reconstruct high-quality time series of optical imagery, it has utility for applications focused on land cover and vegetation remote sensing over long time periods at moderate spatial resolution. We present the DTS algorithm that we have implemented and illustrate the ability of the DTS to reconstruct time series of Landsat imagery across multiple sensors (TM, ETM+, and OLI). To demonstrate the effectiveness of the DTS algorithm we apply it and evaluate results across a diverse range of land cover and vegetation types in the South American Southern Cone region.
Highlights
Smoothing and curve-fitting techniques for reconstructing time series of remotely sensed observations have been used for decades [1]–[4]
Because these instruments provide daily repeat frequency, relatively complete time series can be created for many parts of the world using 8- or 16day composites to minimize the impact of clouds
By dynamically adjusting to both the temporal frequency and local properties of the data, the technique we describe provides a general, robust, and accurate basis for creating high-quality time series of Landsat imagery at weekly time steps that are well-suited for analyses focused on long-term trends and changes in land cover and land use, as well as shorter-term ecological processes such as phenology
Summary
Smoothing and curve-fitting techniques for reconstructing time series of remotely sensed observations have been used for decades [1]–[4] The majority of these techniques have been developed for time series derived from instruments such as Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) (e.g., [5]). Because these instruments provide daily (or neardaily) repeat frequency, relatively complete time series can be created for many parts of the world using 8- or 16day composites to minimize the impact of clouds.
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More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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