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 and processing time. We applied the gap-filling methodology to MODIS Enhanced Vegetation Index (EVI) and daytime and nighttime Land Surface Temperature (LST) datasets for the African continent for 2000–2012, with a 1km spatial resolution, and an 8-day temporal resolution. We validated the method by introducing and filling artificial gaps, and then comparing the original data with model predictions. Our approach achieved R2 values above 0.87 even for pixels within 500km wide introduced gaps. Furthermore, the structure of our approach allows estimation of the error associated with each gap-filled pixel based on the distance to the non-gap pixels used to model its fill value, thus providing a mechanism for including uncertainty associated with the gap-filling process in downstream applications of the resulting datasets.
Highlights
Past and current data collection efforts have produced numerous remotely sensed imagery time-series, often exceeding a decade in length, with tremendous utility for a wide range of research applications (Hay et al, 2006; Scharlemann et al, 2008)
These approaches can be roughly divided into the following categories: (1) methods that rely on spatial information, (2) methods based on temporal information available within an image time-series, and (3) methods that include both spatial and temporal information in the gap-filling process
The core datasets resulting from this research are 8-day daytime Land Surface Temperature (LST), nighttime LST, and Enhanced Vegetation Index (EVI) products that were gap-filled to create spatially and temporally complete datasets for all of Africa from 2000 to 2012
Summary
Past and current data collection efforts have produced numerous remotely sensed imagery time-series, often exceeding a decade in length, with tremendous utility (both realized and potential) for a wide range of research applications (Hay et al, 2006; Scharlemann et al, 2008). Our goals in this research were to develop a data-driven gapfilling methodology that (1) balances the need for accuracy with the computational efficiency necessary for feasible application to continental-scale time-series, (2) uses both spatial and temporal information within the data time-series to fill the gap pixels, (3) requires no ancillary datasets such as land cover products or digital elevation models to model missing pixel values, and (4) provides a standardized yet flexible approach that is applicable to a wide range of datasets Among these goals, the first was most relevant to the wider remote sensing community as the large data volume associated with continental-scale time-series limits the utility of mathematically complex (e.g., geostatistical) algorithms for rapid gap-filling. Using data from an alternative date is the technique underlying the novel Neighborhood Similar Pixel Interpolator method for filling gaps in Landsat ETM+ imagery developed by Chen et al (2011), which was later augmented to include geostatistical theory by Zhu et al (2012)
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