rss Elsevier. Remote Sensing of Environment
To understand carbon dynamics, we need to know how vegetation characteristics affect photosynthesis dynamics and ecosystem functions. Remote sensing has long been used to study terrestrial carbon and water cycles at regional and global scale. Remote sensing data have been shown to be useful for mapping vegetation structural parameters, such as leaf area index, clumping index and fractional vegetation cover. These parameters have been used as key inputs to terrestrial biosphere, ecological, hydrological and meteorological models.
Remote sensing time series research and applications have a rich history for large area monitoring of land and water dynamics. Time series studies utilizing data from global daily polar orbiters such as AVHRR and Spot VEGETATION set the stage for operational monitoring using data from MODIS, MERIS, and other missions. Today, a new generation of time series studies using sub 100-m imagery are capitalizing on the availability of data from high spatial resolution global monitoring missions. For example, the unprecedented 45-year long global Landsat archive is increasingly used to analyze past and present global land and water changes, and higher temporal frequency global observations from Sentinel-2 are enabling the use of dense high resolution time series for near real time monitoring. In addition to Sentinel-2 and Landsat, data from other global Landsat-class missions are increasingly being integrated into virtual Earth observation constellations that further advances global land and water monitoring.