MODIS L2 - aggregation of several daily PIC into a mean composite
Posted: Wed Jun 12, 2024 12:39 pm America/New_York
Hello,
I have downloaded MODIS L2 geophysical variables data for the Southern Ocean to compared with in situ data collected from a cruise in 2016. The MODIS L2 files match the dates of in situ sampling, 10 consecutive days. The data is very patchy so I would like to create a mean composite with the daily data. The downloaded files don't have the same grid extent as I selected swaths with at least 50 % of the area of interest. I wonder if anyone can help me with the following two questions:
* how can I make sure my downloaded files have the same grid so that I can overlay without having to resample them?
* without the same extent, is it possible to aggregate them and what procedure I could use to do it? Can it be done in SeaDAS? If it is only possible with Python, what would be the workflow.
Thank you in advance.
I have downloaded MODIS L2 geophysical variables data for the Southern Ocean to compared with in situ data collected from a cruise in 2016. The MODIS L2 files match the dates of in situ sampling, 10 consecutive days. The data is very patchy so I would like to create a mean composite with the daily data. The downloaded files don't have the same grid extent as I selected swaths with at least 50 % of the area of interest. I wonder if anyone can help me with the following two questions:
* how can I make sure my downloaded files have the same grid so that I can overlay without having to resample them?
* without the same extent, is it possible to aggregate them and what procedure I could use to do it? Can it be done in SeaDAS? If it is only possible with Python, what would be the workflow.
Thank you in advance.