# Reduce image using two image inputs - one with zones the other with data to summarize

I would like to reduce an image where the inputs are two images and the output is a table. One image will have zones (integer values) and the other data to summarize. For each zone in an image, I would like to calculate the mean of values in the other image that overlap that zone. In ArcGIS one could use the tool Zonal Statistics as Table.

https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics-as-table.htm

``````//1- zone image (DEM binned 100m intervals)
//2- data to summarize (mean minimum temperature)
//3- figure out how to summarize mean tmin per dem zone and export as a csv

//zones (dem binned x 100m)
var DEM_binned = image.divide(100).ceil().clip(geometry);
Map.addLayer(DEM_binned, {min:0, max:40, palette:['blue', 'purple', 'green', 'yellow', 'orange', 'red', 'white']}, 'dem');

//data to summarize (mean long-term annual tmin)
var tmin = imageCollection.select('tmin');
var tmin = tmin.mean().clip(geometry);
Map.addLayer(tmin, {min: -5, max: 20, palette:['white', 'blue', 'green', 'yellow', 'red']}, 'tmin')

//get mean tmin per zone as a table
``````

You could try converting your DEM with `.reduceToVectors()` and using this with `.reduceRegions()` to get your mean values. This might be very slow though, because `.reduceToVectors()` can take a while on rasters with a higher resolution.

Instead something like this might be quicker:

``````var bins = ee.List.sequence({
start: -1,
end: 80,
step: 1
})

var scale = tmin.projection().nominalScale()
var tmins = bins.map(function(bin){
Here we define a range of dem values right at the start (-100 to 8,000 m) which should cover all the values we need and then map over this list. You can also use `step` to change the binning. The big advantage here is that we only use `reduceRegion()` on the masked `tmin` raster which has a pretty low resolution. That way the computation time is probably a lot lower than with other methods, however I haven't tested that.