An effective approach for gap-filling continental scale remotely sensed time-series.

ABSTRACT: The archives of imagery and modeled data products derived from remote sensing programs with high temporal resolution provide powerful resources for characterizing inter- and intra-annual environmental dynamics. The impressive depth of available time-series from such missions (e.g., MODIS and AVHRR) affords new opportunities for improving data usability by leveraging spatial and temporal information inherent to longitudinal geospatial datasets. In this research we develop an approach for filling gaps in imagery time-series that result primarily from cloud cover, which is particularly problematic in forested equatorial regions. Our approach consists of two, complementary gap-filling algorithms and a variety of run-time options that allow users to balance competing demands of model accuracy a

SUBMITTER: Weiss DJ 

PROVIDER: S-EPMC4308023 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature