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SMAP (Soil Moisture Active Passive) data readers

Project description

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SMAP (Soil Moisture Active Passive) data readers.

Works great in combination with pytesmo.

Installation

Setup of a complete environment with conda can be performed using the following commands:

conda create -q -n smap_io-environment numpy h5py pyproj
source activate smap_io-environment
pip install smap_io

Supported Products

  • SPL3SMP: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture

Documentation

Documentation Status

Example

SPL3SMP

After downloading the data you will have a path with subpaths of the format YYYY.MM.DD. Let’s call this path root_path. To read the data of a certain date use the following code:

from smap_io import SPL3SMP_Ds
root_path = os.path.join(os.path.dirname(__file__),
                         'test_data', 'SPL3SMP')
ds = SPL3SMP_Ds(root_path)
image = ds.read(datetime(2015, 4, 1))
assert list(image.data.keys()) == ['soil_moisture']
assert image.data['soil_moisture'].shape == (406, 964)

The returned image is of the type pygeobase.Image. Which is only a small wrapper around a dictionary of numpy arrays.

If you only have a single image you can also read the data directly

from smap_io import SPL3SMP_Img
fname = os.path.join(os.path.dirname(__file__),
                     'test_data', 'SPL3SMP', '2015.04.01',
                     'SMAP_L3_SM_P_20150401_R13080_001.h5')
ds = SPL3SMP_Img(fname)
image = ds.read()
assert list(image.data.keys()) == ['soil_moisture']
assert image.data['soil_moisture'].shape == (406, 964)

Project details


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