Mean Composite of netcdf images

I am stuck with a problem. I want to create a mean composite of multiple georeferenced .nc satelllite images. 1st problem is findling the study area. The images have my AOI in different position because of swath. Although I cropped the images based on desired lat and lon. Which cuased the 2nd problem. The cropped images have different dimensions. Someone suggested me to use meshgrid. Can someone give a clear vision how to create composite of multiple images

the code I have written so far is here.

fileDirectory = "input folder";
filename = dir(fullfile(fileDirectory, '*_reprojected_chl.nc'));
nFileDirectory = length(filename);


outputFolder = "output folder";


for i=1:nFileDirectory
    nFileRrs = filename(i).name;
    

    % Crop the data 
    min_lat = 26.5;  
    max_lat = 30.5;  
    min_lon = -95; 
    max_lon = -88.5; 

   
    data = ncread(fullfile(fileDirectory, nFileRrs), 'latitude');  % Replace with your actual variable name
    latitude = double(data); 

    data = ncread(fullfile(fileDirectory, nFileRrs), 'longitude');  % Replace with your actual variable name
    longitude = double(data);  
    
    data = ncread(fullfile(fileDirectory, nFileRrs), 'chl_image');  % Replace with your actual variable name
    chl_image = double(data); 

    % Print lat/lon ranges 
    fprintf('File: %s, Latitude Range: %f to %f, Longitude Range: %f to %f\n', nFileRrs, min(latitude(:)), max(latitude(:)), min(longitude(:)), max(longitude(:)));

    
    % Find row and column indices 
    [row_indices, col_indices] = find(latitude >= min_lat & latitude <= max_lat & ...
                                       longitude >= min_lon & longitude <= max_lon);
    clipped_chl_data = chl_image(min(row_indices):max(row_indices), min(col_indices):max(col_indices));
    clipped_latitude = latitude(min(row_indices):max(row_indices), min(col_indices):max(col_indices));
    clipped_longitude = longitude(min(row_indices):max(row_indices), min(col_indices):max(col_indices));
    clipped_chl_data = clipped_chl_data';
    clipped_latitude = clipped_latitude';
    clipped_longitude = clipped_longitude';
    % Get the dimensions of the clipped data
    [r, c] = size(clipped_chl_data);
end

Answer 1

[Udit06]

Hi Arnab,

Based on the code you have provided above, you've successfully looped through your .nc files, read the relevant data (latitude, longitude, and chl_image), and cropped the data to your Area of Interest (AOI). The next step would be to interpolate these cropped images to a common grid and then calculate the mean composite of these images.

You can follow the following steps for the same

1) Define a common grid

min_lat = 26.5;  
max_lat = 30.5;  
min_lon = -95; 
max_lon = -88.5; 

% Define the common grid's latitude and longitude boundaries and resolution
resolution_lat = 0.1; % adjust as per your requirement
resolution_lon = 0.1; % adjust as per your requirement
common_lat = min_lat:resolution_lat:max_lat;
common_lon = min_lon:resolution_lon:max_lon;

% Create the meshgrid for the common grid
[common_lon_grid, common_lat_grid] = meshgrid(common_lon, common_lat);

2) Interpolate each cropped image to the common grid

% Interpolate the current image to the common grid
interpolated_image = interp2(clipped_longitude, clipped_latitude, clipped_chl_data, common_lon_grid, common_lat_grid, 'linear'

You can refer to the following MathWorks documentation to understand more about meshgrid and interp2 functions.

https://www.mathworks.com/help/matlab/ref/meshgrid.html

https://www.mathworks.com/help/matlab/ref/interp2.html

I hope it helps.