Land surface parameters derived from remotely sensed data at national to global scales

Abstract

Since the mid-1970s remote sensing has evolved as a key tool for the documentation of the state of the environment. On local to national scales, high to medium resolution satellite data with pixel sizes of 5-30m provide opportunities for the detailed mapping of even complex mosaic landscapes. The low temporal resolution of these sensors, large data volumes, and costly data acquisition, however, make them less ideal for regular monitoring purposes. On continental to global scales, the derivation of land indicators is generally provided by remote sensing data originating from low to moderate-resolution sensors with spatial resolutions between 250m-1000m. Due to the short revisit periods of these sensors, a spatially continuous, cloud free, and consistent coverage can be ensured within relatively short time spans, features that are particularly demanding for cloud prone areas. Furthermore, the daily or near-daily global coverage of these sensors provide the basis for continuous time series that allow the analysis of regional to global scale intra-annual land surface dynamics.