Code Record


[DOI: 10.21982/nkd7-cd05 _target: ] gapfill
Gerber, Florian
Tools to predict missing values in satellite data and to develop new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. The package is illustrated with MODIS NDVI data. see:

Code Site: codelibrary/49547140355a573a317757c04e3a3c82.gz

Code Access Instructions:

Appears in: Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-
Strub, G., and Furrer, R. Predicting missing values in
spatio-temporal remote sensing data.
IEEE Transactions on Geoscience and Remote Sensing,
56(5):2841–2853, 2018. doi: 10.1109/TGRS.2017.2785240

Code Languages: C++, Other - R

To compile code: The statistical software R is needed (runs on most Mac, Windows, and Linux operating systems).

Sensor Categories: Optical Radiometer, Optical Imager, Optical Spectrometer

Instrument Processing and Calibration Categories: Optical Radiometer, Optical Imager, Optical Spectrometer, Other - Sensor

Keywords: gapfill, gap-fill, prediction, missing values, low quality values, R, clouds, parallel processing, parallel, R package,