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In QGIS I have a raster map showing land, snow and cloud zones, having resp. pixel values 1, 2, and 3. I extracted the mean altitude values (in m) of each zone on the basis of an underlying DEM and Zonal Statistics of the LecoS (Landscape Ecology Statistics) plugin. The results show in a table like:

On the basis of the mean values of zone 1 and 2, using r.rclass from the Processing Toolbox, I reclassified my zones using this reclass rules text file:

2588 thru 4497 = 1

4498 thru 5122 = 12

5123 thru 7533 = 2

(2588 is the minimum altitude of the DEM, 7533 the maximum altitude).

The result is a new raster map with zones land (1), snow (2) and mixed land-snow pixels (12). This works fine in separate steps and manually preparing the reclass rules text file.

However I have thousands of files to process!
How would it be possible to use the output of the table mean values to automatically populate a reclass rules text file, that can then be directly used in r.reclass?

r.reclass doesn't have an option to import values from an outside table file. I guess I need to write a script for this. I have no experience with scripts so far.

First solution is to use r.reclassas a batch process by right-clicking on it in process list

It opens a new window where you can add every raster one by one but it's still a tedious task for thousands of rasters.

For a massive treatment, the best way is to use a python script with rasterio lib. Something like this:

import os, sys, rasterio, glob #import lib
import numpy as np #import lib

rasters_folder = sys.argv[1] #path of raster folder

rasters_list = glob.glob('0/*.tif'.format(rasters_folder)) #create a list of rasters

for raster_path in rasters_list: #for each raster in the list do...
 with rasterio.Env(): #work with rasterio environment
 src = rasterio.open(raster_path) #open the raster
 profil = src.profile #raster profile like height and width

 img = src.read(1) #first band of raster

 #3 reclassment conditions
 img[np.where((2588 < img) & (img <= 4497)) ] = 1
 img[np.where((4497 < img) & (img <= 5122)) ] = 12
 img[np.where((5122 < img) & (img <= 7533)) ] = 2

 #save result in a new raster 
 with rasterio.open('_reclass.tif'.format(os.path.splitext(raster_path)[0]), 'w', **profil) as dst:
 dst.write(img, 1)

Just a follow-up on my above question. I have been able to find a solution by learning more about python scripting. I did the following and it worked (for 1 file):

Maybe not the best code but this is my humble beginner attempt with a (for me) serious script.
In the graphic modeler of QGIS I managed to implement this script and use it for multiple files in batch processing.
I would like to thank everyone that tried to help with this post!

I would do this by writing simple field formulae in Excel. You can then copy and paste for your new tables and do it in such a way that the table or a number of columns within it can be exported as text using space delimiters. Alternatively, wrap that into a Python function for greater automation.