pktools 2.6.7
Processing Kernel for geospatial data
pkkalman

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