Zenodo link

README.md

@@ -165,7 +165,7 @@ burntfields rm_blanks \
 
 #### BASMA
 Weekly BASMA layers were created from the Sentinal-2 imagery using the general concept and methods in [Daldegan et. al_2019](#3). 
-Burned endmembers were created for each week by manually identifying 15 charred locations in individual Sentinel-2 images and extracting the band data for each polygon. The endmember spectral library was then built using Principal Component Analysis, using the R script: `LocalScripts/BASMA_endmemberOptimizaton.R` (contributed by Ben Moscona). Our endmember outputs and final weekly layers with the spectral mixture analysis applied are provided in the Zenodo data archive: 10.5281/zenodo.10987987. (band 0 = green vegetation, band 1 = Non-productive vegetation and bare soil, band 2 = Char (burned)). Note that our study period was eight weeks, but Sentinel images for weeks 5 and 6 were too cloudy to produce BASMA layers. Weeks 7 and 8 were renamed to 5 and 6, respectively, for the code to work.   
+Burned endmembers were created for each week by manually identifying 15 charred locations in individual Sentinel-2 images and extracting the band data for each polygon. The endmember spectral library was then built using Principal Component Analysis, using the R script: `LocalScripts/BASMA_endmemberOptimizaton.R` (contributed by Ben Moscona). Our endmember outputs and final weekly layers with the spectral mixture analysis applied are provided in the Zenodo data archive: 10.5281/zenodo.10987987 [here](https://zenodo.org/records/10987987). (band 0 = green vegetation, band 1 = Non-productive vegetation and bare soil, band 2 = Char (burned)). Note that our study period was eight weeks, but Sentinel images for weeks 5 and 6 were too cloudy to produce BASMA layers. Weeks 7 and 8 were renamed to 5 and 6, respectively, for the code to work.   
 
 #### Cloud masking
 For PlanetScope imagery, cloud masks were applied with the **apply_masks.py** function: