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        "author": "T. Quaife and E. Pinnington",
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        "description": "signaturesimulator\n==================\n\nThis Python package simulates satellite data from passive optical and active microwave sensors based on land surface\nbiogeophysical state variables (leaf area index, canopy height and soil moisture) and viewing geometries and times.\nHere we provide documentation on this package. Below we illustrate a few basic examples of installing the package and\nits use.\n\nInstalling the package\n----------------------\n\nTo install the signaturesimulator Python package using pip simply run the following from the command line::\n\n    pip install signaturesimulator\n\nExample of calculating reflectance for a single acquisition\n-----------------------------------------------------------\n\n>From an interactive console we first import the signaturesimulator package and setup an instance of the Simulator\nclass::\n\n    import signaturesimulator as ss\n    sim = ss.Simulator()\n\nWe can now set the viewing geometries and time of the satellite observation, to do this we also need to specify a\nPython datetime object so we will also import the datetime module::\n\n    import datetime as dt\n    sim.get_geom = sim.geom_default(date_utc=dt.datetime(2016,6,17,10,25), vza=5.5, vaa=286.3, sza=26.8, saa=157.0)\n\nWhere vza and vaa are the view zenith and azimuth angles and sza and saa are the solar zenith and azimuth angles. We\nmust now specify the land surface state variables for which to calculate reflectance::\n\n    sim.get_land_state = sim.state_default(date_utc=dt.datetime(2016,6,17,9,0), lai=3.0, canopy_ht=1.0, soil_m=0.3)\n\nWhere lai is leaf are index (m2 m-2), canpopy_ht is vegetation canopy height (m) and soil_m is the top layer soil\nmoisture (m3 m-3). We now specify the radiative transfer model we want to use and then run the simulator (currently\nthere is the choice between active microwave and passive optical with these models being setup for the Sentinel 1 and 2\nmissions respectively). Here we are interested in reflectance, so set the following::\n\n    sim.run_rt = sim.passive_optical\n    sim.run()\n\n.. note::  Currently the passive optical model will only work if the signaturesimulator is installed on a linux machine,\n    due to the packaged Semi-Discrete binaries. We are working to bring out a version compatiable with Windows and Mac\n    OSx. Please see the next example if you are not on a linux machine. The active microwave model is usable on any\n    architecture.\n\n\nOnce the simulator has finished its run it will write the output to a new spectra class. We can then look at the\nreflectance values for different bands (ordered by wavelength) using the following command::\n\n    sim.spectra.refl\n\nWhich for this example outputs::\n\n    [array([ 0.018664,  0.020378,  0.026745,  0.02097 ,  0.035889,  0.213055,\n             0.332095,  0.338523,  0.343047,  0.337906,  0.230565,  0.192891,\n             0.086688])]\n\nNext we show how to run the simulator for an active microwave sensor over a time series of specified geometries and\nland surface state variables.\n\nExample of calculating backscatter for multiple aquisitions\n-----------------------------------------------------------\n\nUsing the Simulator instance specified in the previous example we show how we can run the simulator for multiple\naquisitions. First we specify the dates and view geometries of our observations using a csv file::\n\n    sim.get_geom = sim.geom_csv(fname=sim.example_s1_geometries)\n\nHere :code:`sim.example_s1_geometries` points to an example csv file included with the signaturesimulator package found\nhere::\n\n$PYTHONPATH/signaturesimulator/data/geometries/s1_example_const.csv\n\nAny geometry csv file must be of the following format::\n\n    # date, vza, vaa, sza, saa\n    2016/01/03 05:23,34.3773946043,100.545700717,105.298744327,107.406448412\n    2016/01/08 05:31,23.4284120953,102.103838392,103.928256857,108.076934788\n    ...\n\nWe next specify the dates and values for our land surface state variables using a csv file::\n\n    sim.get_land_state = sim.state_csv(fname=sim.example_state_variables)\n\nAgain we are using an example csv file packaged with the simulator, for the land state csv files must follow the\nformat::\n\n    # date, lai (m2 m-2), canopy_height (m), soil_moisture (m3 m-3)\n    2016/01/01 12:00,0.001,0.001,0.339560508728\n    2016/01/02 12:00,0.001,0.001,0.341959953308\n    ...\n\nWe can now set the radiative transfer model we wish to use and run the simulator::\n\n    sim.run_rt = sim.active_microwave\n    sim.run()\n\nOnce the simulator has finished its run it will write the output to a new backscat class instance. We can see the output\nvariables available to us using::\n\n    sim.output_variables\n\nIn this case this returns::\n\n    ['lai', 'date_sat_ob', 'soil_moisture', 'hv', 'can_height', 'date_land_ob', 'hh', 'vv']\n\nTo inspect the output in the hv polarisation we use::\n\n    sim.backscat.hv\n\nWhich for this example returns::\n\n    [-14.839751698875441, -14.612031628695206, ...,  -14.647495031040052, -14.470503894767003]\n\nTo plot the backscatter in the hv polaristation we can use the following command::\n\n    sim.plot('hv')\n\nWhich will return the plot:\n\n.. image:: s1_hv.png\n\nWe can plot any of the output variables using the plot method of the Simulator class, for LAI::\n\n    sim.plot('lai')\n\nReturning:\n\n.. image:: s1_lai.png\n\nHere we can see the effect that leaf area index is having on the simulated observations of backscatter.\n\nSource Code\n-----------\n\nwww.github.com/pyearthsci/signaturesimulator\n\nSupport\n-------\n\nIf you are having issues, please let us know.\nContact: e.pinnington@reading.ac.uk\n\nLicense\n-------\n\nDetails of licensing information. TBC.\n\n",
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        "keywords": "satellite earth science land surface passive optical active microwave sentinel",
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