How to use least-square fit and gaussian surface for 3D data?

I have followed the mentioned research papers to calculate the heat footprint using gaussian curve fit. (Land surface temperature images are used to calculate the urban heat island and its spatial extent which is called heat footprint.) https://doi.org/10.1016/j.rse.2014.05.005, https://doi.org/10.3390/a8020082, https://doi.org/10.1016/j.apgeog.2021.102496

Unfortunately, none of these authors have any MATLAB file uploaded here that can help me. Also I tried to look for least square planar fit to find the offset of gaussian equation, since I am new on MATLAB, it is getting a little hard to understand which one is correct and how to use all those results.

Also I used the curve fitting tool but it does not have an option to use gaussian for 3D data. Can anybody help me regarding gaussian surface fit for 3D data?

Thank you

[Mathieu NOE]

hello

check these :

Fit 2D gaussian function to data - File Exchange - MATLAB Central (mathworks.com)

Fit 2D Gaussian with Optimization Toolbox - File Exchange - MATLAB Central (mathworks.com)

Fit 1D and 2D gaussian to noisy data - File Exchange - MATLAB Central (mathworks.com)

Answer 1

[Matt J]

This FEX file will fit a Gaussian with a constant background in any dimension,

https://www.mathworks.com/matlabcentral/fileexchange/69116-gaussfitn

[Deepshikha Deo]

Thank you for the answer.

As I checked this FEX file, there are two data: xdata and zdata which are necessary. Thus I created a 4834x2 double type with the name 'xdata' and 4834x1 double type with the name 'zdata' in the workspace. As I run the code I get error

gaussfitn

Not enough input arguments.

Error in gaussfitn (line 58)

[M,N]=size(xdata);

How should I deal with it?

I have attached the csv file I used to create the two matrix. In this csv the first two columns are of xy coordinates and the third column has zvalues (temperature data).

Thank you

[Matt J]

I can't explain it. It runs fine in the online Matlab engine:

[x,y,z]=readvars("JP_LST.csv");

p=gaussfitn([x,y],z)

Local minimum possible.

lsqcurvefit stopped because the final change in the sum of squares relative to 
its initial value is less than the value of the function tolerance.

p = 1×4 cell array
    {[21.9097]}    {[661.5662]}    {2×1 double}    {2×2 double}