The MODIS Assimilation and Processing Engine combines a state-of-the art Whittaker smoother, implemented as fast C-extension through Cython and including a V-curve optimization of the smoothing parameter, with a HDF5 based processing chain optimized for MODIS data.
The sub-module modape.whittaker
includes the following variations of the Whittaker smoother with 2nd order differences:
- ws2d: Whittaker with fixed smoothing parameter (
s
) - ws2dp: Whittaker with fixed smoothing parameter (
s
) and expectile smoothing using asymmetric weights - ws2doptv: Whittaker with V-curve optimization of the smoothing parameter
- ws2doptvp: Whittaker with V-curve optimization of the smoothing parameter and expectile smoothing using asymmetric weights
The MODIS processing chain consists of the following executables, which can be called through commandline:
modis_download
: Query and download raw MODIS products (requires Earthdata credentials)modis_collect
: Collect raw MODIS data into daily datacubes stored in an HDF5 filemodis_smooth
: Smooth, gapfill and interpolate raw MODIS data using the implemented Whittaker smoothermodis_window
: Extract mosaic(s) of multiple MODIS tiles, or subset(s) of a global/tiled MODIS product and export it as GeoTIFF raster in WGS1984 coordinate system
Additional executables:
csv_smooth
: Smooth timeseries stored within a CSV filemodis_info
: Retrieve metadata from created HDF5 files
For a more information please check out the documentation!
Dependencies:
modape depends on these packages:
- click
- gdal
- h5py
- numpy
- pandas
- python-cmr
- requests
Some of these packages (eg. GDAL) can be difficult to build, especially on windows machines. In the latter case it's advisable to download an unofficial binary wheel from Christoph Gohlke's Unofficial Windows Binaries for Python Extension Packages and install it locally with pip install
before installing modape.
Installation from github:
$ git clone https://github.com/WFP-VAM/modape
$ cd modape
$ pip3 install .
Installation from PyPi:
$ pip3 install modape
If you find a bug, see a typo, have some kind of troubles running the module or just simply want to have a feature added, please submit an issue!
References:
P. H. C. Eilers, V. Pesendorfer and R. Bonifacio, "Automatic smoothing of remote sensing data," 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Brugge, 2017, pp. 1-3. doi: 10.1109/Multi-Temp.2017.8076705 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8076705&isnumber=8035194
Core Whittaker function adapted from whit2
function from R package ptw:
Bloemberg, T. G. et al. (2010) "Improved Parametric Time Warping for Proteomics", Chemometrics and Intelligent Laboratory Systems, 104 (1), 65-74
Wehrens, R. et al. (2015) "Fast parametric warping of peak lists", Bioinformatics, in press.