This script allows to do atmospheric correction for list of images (or individual images) of Sentinel-2 and Landsat sensors, especifically for images over coastal or oceanic areas, using the GEE Python API in Jupyter Notebook. Some atmospheric correction settings can be modified in the parameters.py module to work with images over inland areas (See line 36 in that module). The script does AC automatically by providing the right satellite mission, list of image ID's, and a specific GEE Asset to export processed images to your personal GEE account. More sensors can be added by modifying the mission_specifics.py and parameters.py modules to properly work with the available collections in GEE and Py6S. Script modified from https://github.com/...
Continuous monitoring of surface water resources is often challenging due to the lack of monitoring ...
Monitoring forest cover change from Earth observation data streams in near-real-time presents a chal...
This dataset was used to produce land cover change analyses for chapters two and three as part of a ...
This script for Google Earth Engine (GEE) platform allows the application of SSEBop model (Operation...
The SatPy package is a python library for reading and manipulating meteorological remote sensing dat...
eemont v0.3.0 :rocket: New Features The tasseledCap() extended method for ee.Image and ee.ImageColl...
This is the first release of the PyGEE Surface Water Toolbox for surface water mapping and time seri...
Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images der...
This is a small python script to subset GEE gridded data products into time series for a given locat...
With public access available for numerous satellite imaging products, modelling in atmospheric and o...
While moving between assets from Planet Inc and Google Earth Engine it was imperative to create a pi...
The necessity of sustainable development for landscapes has emerged as an important theme in recent ...
Continuous monitoring of surface water resources is often challenging due to the lack of monitoring ...
Monitoring forest cover change from Earth observation data streams in near-real-time presents a chal...
This dataset was used to produce land cover change analyses for chapters two and three as part of a ...
This script for Google Earth Engine (GEE) platform allows the application of SSEBop model (Operation...
The SatPy package is a python library for reading and manipulating meteorological remote sensing dat...
eemont v0.3.0 :rocket: New Features The tasseledCap() extended method for ee.Image and ee.ImageColl...
This is the first release of the PyGEE Surface Water Toolbox for surface water mapping and time seri...
Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images der...
This is a small python script to subset GEE gridded data products into time series for a given locat...
With public access available for numerous satellite imaging products, modelling in atmospheric and o...
While moving between assets from Planet Inc and Google Earth Engine it was imperative to create a pi...
The necessity of sustainable development for landscapes has emerged as an important theme in recent ...
Continuous monitoring of surface water resources is often challenging due to the lack of monitoring ...
Monitoring forest cover change from Earth observation data streams in near-real-time presents a chal...
This dataset was used to produce land cover change analyses for chapters two and three as part of a ...