Metadata-Version: 2.1
Name: S2_TOA_TO_LAI
Version: 2.0.0
Summary: From Sentinel 2 TOA to LAI
Home-page: https://github.com/MarcYin/S2_TOA_TO_LAI
Author: Feng Yin
Author-email: ucfafyi@ucl.ac.uk
License: GNU Affero General Public License v3.0
Description: ### Feng Yin                          
        ### Department of Geography, UCL      
        ### ucfafyi@ucl.ac.uk                 
         
        [![Build Status](https://travis-ci.org/MarcYin/S2_TOA_TO_LAI.svg?branch=master)](https://travis-ci.org/MarcYin/S2_TOA_TO_LAI)
         
        Here, we use [SIAC](https://github.com/multiply-org/atmospheric_correction/) 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](https://scihub.copernicus.eu/) 
        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](https://urs.earthdata.nasa.gov).
        2. A Copernicus Open Access Hub username and password and can be applied [here](https://scihub.copernicus.eu/dhus/#/self-registration) 
         
        ## Installation:
         
        1. Directly from github to get the most up to date version of it:                             
        ```bash                               
        pip install https://github.com/MarcYin/S2_TOA_TO_LAI/archive/master.zip
        ```    
        2. Using PyPI (This one is generally related to release)
        ```bash                               
        pip install S2-TOA-TO-LAI
        ```  
        3. Using anaconda from anaconda for 'better' package managements                               
        ```bash                               
        conda install -c f0xy -c conda-forge s2-toa-to-lai
        ``` 
        
        To save your time for installing GDAL:             
        ```bash
        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:
        ```python
        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:
        ```python
        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):
        ```python                                            
        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*
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
