S2-TOA-TO-LAI 2.0.0

Feng Yin

Department of Geography, UCL

ucfafyi@ucl.ac.uk

Here, we use SIAC to do the atmospheric correction of Sentinel 2 TOA reflectance, then use inverse emulator to retrieve LAI from surface recflectance. These code will automatically download Sentinel 2 TOA reflectance data from Copernicus Open Access Hub and do atmospheric correction with SIAC and give per pixel LAI value at 20 meters resolution.

Requirements:

1. A NASA Earthdata username and password and can be applied here.
2. A Copernicus Open Access Hub username and password and can be applied here

Installation:

1. Directly from github to get the most up to date version of it:

pip install https://github.com/MarcYin/S2_TOA_TO_LAI/archive/master.zip

2. Using PyPI (This one is generally related to release)

pip install S2-TOA-TO-LAI

3. Using anaconda from anaconda for 'better' package managements

conda install -c f0xy -c conda-forge s2-toa-to-lai

To save your time for installing GDAL:

conda uninstall gdal libgdal
conda update --all -c conda-forge
conda install -c conda-forge gdal>2.1,<2.4

Usage

1. Using Sentinel 2 tiles directly:

from S2_TOA_TO_LAI import TOA2LAI_S2
TOA2LAI_S2(tiles = ['50SMG'], start='2018-01-02', end='2018-01-03')

2. Using LatLon (Lat first then Lon) and this can be a 2D list of latlon:

from S2_TOA_TO_LAI import TOA2LAI_S2
TOA2LAI_S2(latlon = '35.4, 56.2', start='2018-01-02', end='2018-01-03')

3. Using polygon from string(s) or (a) vector file(s):

from S2_TOA_TO_LAI import TOA2LAI_S2
aoi = 'POLYGON((115.79984234354565 39.41267418434987,115.81853363330639 39.41267418434987,115.81853363330639 39.42542974293974,115.79984234354565 39.42542974293974,115.79984234354565 39.41267418434987))' # or a vector file 
TOA2LAI_S2(aoi = aoi, start='2018-01-02', end='2018-01-03')

You can also specify cloud_cover but this may lead to losing of S2 observations due to a bad cloud mask from L1C data