Abstract
Dense time series of optical remote sensing data have long been the domain of broad-scale sensors with daily near-global coverage, such as the Advanced Very High Resolution Radiometer (AVHRR), the Medium Resolution Imaging Spectrometer (MERIS), the Moderate Resolution Imaging Spectrometer (MODIS) or the Satellite Pour l’Observation de la Terre (SPOT) VEGETATION. More recently, satellite data suitable for fine-scale analyses are becoming attractive for time series approaches. The major reasons for this development are the opening of the United States Geological Survey (USGS) Landsat archive along with a standardized geometric pre-processing including terrain correction. Based on such standardized products, tools for automated atmospheric correction and cloud/cloud shadow masking advanced the capabilities to handle cloud-contamination effectively. Finally, advances in information technology for mass data processing today allow analysing thousands of satellite images with comparatively little effort. Based on these major advancements, time series analyses have become feasible for solving questions across different research domains, while the focus here is on land systems. While early studies focused on better characterising forested ecosystems, now more complex ecosystem regimes, such as shrubland or agricultural system dynamics, come into focus. Despite the evolution of a wealth of novel time series-based applications, coherent analysis schemes and good practice guidelines are scarce. This chapter accordingly strives to structure the different approaches with a focus on potential applications or user needs. We end with an outlook on forthcoming sensor constellations that will greatly advance our opportunities concerning time series analyses.
Patrick Hostert
Professor
