  Remotely sensed data are sparse, which means that data have missing values,
for instance due to cloud cover. This is problematic for applications and
signal processing algorithms that require complete data sets. To address the
sparse data issue, we present a new gap-fill algorithm. The proposed method
predicts each missing value separately based on data points in a
spatio-temporal neighborhood around the missing data point. The computational
workload can be distributed among several computers, making the method suitable
for large datasets. The prediction of the missing values and the estimation of
the corresponding prediction uncertainties are based on sorting procedures and
quantile regression. The algorithm was applied to MODIS NDVI data from Alaska
and tested with realistic cloud cover scenarios featuring up to 50% missing
data. Validation against established software showed that the proposed method
has a good performance in terms of the root mean squared prediction error. The
procedure is implemented and available in the open-source R package gapfill. We
demonstrate the software performance with a real data example and show how it
can be tailored to specific data. Due to the flexible software design, users
can control and redesign major parts of the procedure with little effort. This
makes it an interesting tool for gap-filling satellite data and for the future
development of gap-fill procedures.
