# Moving Average with Low Pass Filter

How to calculate a moving average of a satellite time-series ?

• The idea here is to compute a moving average already on the cloud (not locally) for S5P data.
• Specific request/idea/comment: I thought about using apply_dimension() with filter_temporal() as below:

moving_average ← function(data, context) {
return(p\$filter_temporal(data = data, dimension = “t”))
}

datacube = p\$apply_dimension(process = moving_average,
data = datacube, dimension = “t”
)

the function “moving_average” is not working for apply dimension. Do you have any ideas on how to implement this (this or any
other way)?
The image from Edzer’s book is the main reference for that and it should help find a solution…

In theory, but probably not yet supported, the ‘apply_neighourhood’ process allows you to apply a function, like mean, to a spatiotemporal window that you can specify.
What time window would you like to apply it to?

https://processes.openeo.org/#apply_neighborhood

I also faced this issue some time ago, but didn’t come up with a solution! apply_kernel: apply along temporal dimension? · Issue #324 · Open-EO/openeo-processes · GitHub

Actually I’m develoing an RShiny application that calls openEO processes. The time window would be user defined, but in general monthly / 3 months, something like that.

I gave a look with Matthias at the apply_neighborhood, but the problem is that it expects a two dimensional datacube, so this would not work for time (moving average).

I tried here passing a reduce_dimension process, but I’m not sure what’s the proposal for one dimension.

process1 = function(data, context = NULL) {
reduce1 = p\$reduce_dimension(data = data, dimension = “t”, reducer = mean)
reduce1
}
datacube = p\$apply_neighborhood(data = datacube, size = list(list(
“dimension” = “t”, “value” = “P30D”)), process = process1)

How feasible would it be to implement this? It would be great to have it, as this is the last step I require to finish UC5 for S5P data…

1 Like

I’m not sure how this should look like with apply_neighborhood. If you pass only t as dimension then you’d work on a 1D data cube in the callback, but which process to use then? apply_dimension on t with a mean? This is somewhat weird…

``````process2 = function(data, context = NULL) {
m = p\$mean(data)
return(p\$array_create(data = [m]))
}
process1 = function(data, context = NULL) {
return(p\$apply_dimension(data = data, dimension = “t”, reducer = process2))
}
datacube = p\$apply_neighborhood(data = datacube, size = list(list(“dimension” = “t”, “value” = “P30D”)), process = process1)
``````

I was actually more thinking of something like:

``````datacube = p\$apply_neighborhood(data = datacube, size = list(list(
“dimension” = “t”, “value” = “P30D”),list(
“dimension” = “x”, “value” = “1”, "unit"="px"),list(
“dimension” = “y”, “value” = “1”, "unit"="px")), process = mean)
``````

Of course, this assumes that the backend is allowed to ‘flatten’ the input to mean into a one dimensional array. Would that be ok?
We can indeed support this on vito backend, but do need to plan the work, so due to holidays this could move into september.