Help with efficient automated workflow from SeaDAS to ArcMap
Posted: Tue Jul 31, 2018 11:26 am America/New_York
Hello,
My ultimate goal is to produce a simple SAV suitability model in ArcMap using a combination of MERIS L2 Chlor-A (300m), MERIS L2 KD940 (300m), and MODIS L2 SST4 (1km) for the period of March 2011 – Nov 2011. For the suitability model, I would like to:
a) filter MODIS images by a maximum view angle (say < 40 degrees)
b) mask out cloud cover and low quality pixels in both MERIS and MODIS images
c) georeference, reproject (ultimately to NAD 83 State Plane MD meters), and clip to study area
d) calculate per pixel mean and median yearly values for each variable
e) create a basic suitability model using absolute thresholds for each variable
I am familiar with automating this type of workflow using HDF files in ENVI/IDL, GeoTiff files in ArcMap/Python, or in R. I would prefer to work in Python as much as possible. I am not familiar with working with SeaDAS products, specifically with data in the .bz2 (MERIS) or .x.nc (MODIS) format.
So far, I have managed to download all available L2 MERIS and MODIS images for the study area over the period of interest. I am trying to figure out which steps should be performed in SeaDAS versus ArcMap/Python, and the most logical order of steps. Further, for the portions to be performed in SeaDAS, I'm hoping for help identifying the best tools, as well as how to automate if possible, as there are hundreds of images for the time period.
I have about a month to complete this project, and therefore want to ensure that I am not wasting time using incorrect or inefficient methods.
Thanks!
(I have cross posted this under Non-SeaDAS questions, as I am also seeking advice specific to preparing data to process in ArcMap)
My ultimate goal is to produce a simple SAV suitability model in ArcMap using a combination of MERIS L2 Chlor-A (300m), MERIS L2 KD940 (300m), and MODIS L2 SST4 (1km) for the period of March 2011 – Nov 2011. For the suitability model, I would like to:
a) filter MODIS images by a maximum view angle (say < 40 degrees)
b) mask out cloud cover and low quality pixels in both MERIS and MODIS images
c) georeference, reproject (ultimately to NAD 83 State Plane MD meters), and clip to study area
d) calculate per pixel mean and median yearly values for each variable
e) create a basic suitability model using absolute thresholds for each variable
I am familiar with automating this type of workflow using HDF files in ENVI/IDL, GeoTiff files in ArcMap/Python, or in R. I would prefer to work in Python as much as possible. I am not familiar with working with SeaDAS products, specifically with data in the .bz2 (MERIS) or .x.nc (MODIS) format.
So far, I have managed to download all available L2 MERIS and MODIS images for the study area over the period of interest. I am trying to figure out which steps should be performed in SeaDAS versus ArcMap/Python, and the most logical order of steps. Further, for the portions to be performed in SeaDAS, I'm hoping for help identifying the best tools, as well as how to automate if possible, as there are hundreds of images for the time period.
I have about a month to complete this project, and therefore want to ensure that I am not wasting time using incorrect or inefficient methods.
Thanks!
(I have cross posted this under Non-SeaDAS questions, as I am also seeking advice specific to preparing data to process in ArcMap)