:py:mod:`statgis.gee.landsat_functions`
=======================================

.. py:module:: statgis.gee.landsat_functions

.. autoapi-nested-parse::

   Function to process Landsat SR Images.



Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   statgis.gee.landsat_functions.scaler
   statgis.gee.landsat_functions.cloud_mask
   statgis.gee.landsat_functions.rename_bands



.. py:function:: scaler(image: ee.Image) -> ee.Image

   Scale optical bands to surface reflectance values and thermal bands to Kelvin.

   :param image: Landsat SR image.
   :type image: ee.Image

   :returns: **image** -- Image with bands scaled.
   :rtype: ee.Image

   .. rubric:: Example

   Scale an image:

   >>> import ee
   >>> from statgis.gee import landsat_functions
   >>> ee.Initialize()
   >>> poi = ee.Geometry.Point(-74.8180175, 10.9838119)
   >>> image = (
   ...     ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
   ...     .filterBounds(poi)
   ...     .first()
   ... )
   >>> image = landsat_functions.scaler(image)

   Or scaler all image in a collection:

   >>> import ee
   >>> from statgis.gee import landsat_functions
   >>> ee.Initialize()
   >>> poi = ee.Geometry.Point(-74.8180175, 10.9838119)
   >>> image_collection = (
   ...     ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
   ...     .filterBounds(poi)
   ...     .map(landsat_functions.scaler)
   ... )


.. py:function:: cloud_mask(image: ee.Image, mask_snow: bool = False) -> ee.Image

   Mask pixels classified as clouds from QA_PIXEL band.

   :param image: Image to mask.
   :type image: ee.Image
   :param mask_snow: If `True` mask pixels classified as snow.
                     If `False` mask only pixels classified as cloud.
   :type mask_snow: bool (optional)

   :returns: **image** -- Masked image.
   :rtype: ee.Image

   .. rubric:: Example

   >>> import ee
   >>> from statgis.gee import landsat_functions
   >>> ee.Initialize()
   >>> poi = ee.Geometry.Point(-74.8180175, 10.9838119)
   >>> image = (
   ...     ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
   ...     .filterBounds(poi)
   ...     .first()
   ... )
   >>> image = landsat_functions.cloud_mask(image)

   On an image collection:

   >>> import ee
   >>> from statgis.gee import landsat_functions
   >>> ee.Initialize()
   >>> poi = ee.Geometry.Point(-74.8180175, 10.9838119)
   >>> image_collection = (
   ...     ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
   ...     .filterBounds(poi)
   ...     .map(landsat_functions.cloud_mask)
   ... )


.. py:function:: rename_bands(image: ee.Image) -> ee.Image

   Rename the bands from a Landsat Image.

   :param image: Image of interest.
   :type image: ee.Image

   :returns: **image** -- Image with bands renamed
   :rtype: ee.Image

   .. rubric:: Example

   Rename the bands in an image:

   >>> import ee
   >>> from statgis.gee import landsat_functions
   >>> ee.Initialize()
   >>> poi = ee.Geometry.Point(-74.8180175, 10.9838119)
   >>> image = (
   ...     ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
   ...     .filterBounds(poi)
   ...     .first()
   ... )
   >>> image = landsat_functions.rename_bands(image)

   Also we can map the function to an image collection:

   >>> import ee
   >>> from statgis.gee import landsat_functions
   >>> ee.Initialize()
   >>> poi = ee.Geometry.Point(-74.8180175, 10.9838119)
   >>> image_collection = (
   ...     ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
   ...     .filterBounds(poi)
   ...     .map(landsat_functions.rename_bands)
   ... )