produce kalman filtered raster time series

SYNOPSIS

Usage: pkkalman -mod modelinput.tif -obs obsinput.tif [-direction [forward|backward|smooth]]* -ofw output_fc.tif -obw output_bw.tif -ofb output_fb.tif

Options[-tmod time]* [-tobs time]* [-modnodata value]* [-obsnodata value]* [-modmask mask.tif] [-obsmask mask.tif] [-msknodata value]* [-mskband] [-u_ofw uncert_fw.tif] [-u_obw uncert_bw.tif] [-u_ofb uncert_fb.tif]

Advanced options (see table)

Description

The utilty pkkalman will complement a time series of observations (option -obs) at fine spatial resolution. A data assimilation technique based on a Kalman filter is hereby used. The data at fine spatial resolution are assimilated with coarse spatial resolution time series at a finer temporal resolution, referred to as a model (option -mod). The time series for both observation and model can either be provided as multi-band raster datasets or as multiple single band datasets. Missing data in the observations are predicted by the algorithm. The model must cover at least the spatial coverage of the observation. The missing data must be provided either as nodata values in the input (using option -obsnodata) or as an external mask (using option -obsmask). The time sequence for the model and observation should be provided via the options -tmod and -tobs. Tuning parameters for the algorithm are process noise (option -q) and the weights for uncertainty of valid observations (-uo) and the model (-um).

Options

• use either -short or --long options (both --long=value and --long value are supported)
• short option -h shows basic options only, long option --help shows all options

  short	long	type	default	description
  dir	direction	std::string	forward	direction to run model (forward|backward|smooth)
  mod	model	std::string		coarse spatial resolution input datasets(s) used as model. Use either multi-band input (-model multiband_model.tif) or multiple single-band inputs (-mod model1 -mod model2 etc.)
  modmask	modmask	std::string		model mask datasets(s). Must have same dimension as model input. Use either multi-band input or multiple single-band inputs
  obs	observation	std::string		fine spatial resolution input dataset(s) used as observation. Use either multi-band input (-obs multiband_obs.tif) or multiple single-band inputs (-obs obs1 -obs obs2 etc.)
  obsmask	obsmask	std::string		observation mask dataset(s). Must have same dimension as observation input (use multi-band input or multiple single-band inputs
  tmod	tmodel	int		time sequence of model input. Sequence must have exact same length as model input. Leave empty to have default sequence 0,1,2,etc.
  tobs	tobservation	int		time sequence of observation input. Sequence must have exact same length as observation input
  a_srs	a_srs	std::string		Override the projection for the output file (leave blank to copy from input file, use epsg:3035 to use European projection and force to European grid
  ofw	outputfw	std::string		Output raster dataset for forward model
  u_ofw	u_outputfw	std::string		Uncertainty output raster dataset for forward model
  obw	outputbw	std::string		Output raster dataset for backward model
  u_obw	u_outputbw	std::string		Uncertainty output raster dataset for backward model
  ofb	outputfb	std::string		Output raster dataset for smooth model
  u_ofb	u_outputfb	std::string		Uncertainty output raster dataset for smooth model
  modnodata	modnodata	double	0	invalid value for model input
  obsnodata	obsnodata	double	0	invalid value for observation input
  msknodata	msknodata	float	0	Mask value not to consider
  mskband	mskband	short	0	Mask band to read (0 indexed)
  obsmin	obsmin	double		Minimum value for observation data
  obsmax	obsmax	double		Maximum value for observation data
  eps	eps	double	1e-05	epsilon for non zero division
  um	uncertmodel	double	1	Uncertainty of the model
  uo	uncertobs	double	1	Uncertainty of valid observations
  unodata	uncertnodata	double	100	Uncertainty in case of no-data values in observation
  q	q	double	1	Process noise: expresses instability (variance) of proportions of fine res pixels within a moderate resolution pixel
  down	down	int		Downsampling factor for reading model data to calculate regression (default is ratio between coarse (model) and fine (obs) resolution raster datasets)
  ot	otype	std::string		Data type for output image ({Byte/Int16/UInt16/UInt32/Int32/Float32/Float64/CInt16/CInt32/CFloat32/CFloat64}). Empty string: inherit type from input image
  of	oformat	std::string	GTiff	Output image format (see also gdal_translate).
  co	co	std::string		Creation option for output file. Multiple options can be specified.
  v	verbose	short	0	verbose mode when positive

  Examples

Some examples how to use pkcrop can be found here