Time series instead of temporal aggregates

[philament]

Is it possible to specify that I want to request time series from a collection rather than temporal averages? Running something like

test = ee_grab(data = ee_data_collection(datasetID = "COPERNICUS/S2",
                                         spatialReducer = 'mean',
                                         timeStart = '2019-05-01',
                                         timeEnd = '2019-07-30',
                                         resolution = 10),
               targetArea = 'roi.shp')

always returns the average over the entire time period (presumably because temporalReducer = "mean" by default). How do I average only in space but not in time? I.e. I want to average over my regions of interest for each image but return the time series of that for the specified period.

[arupakaa]

I second this. Very excellent software all around. Hats off. However, I wonder how simple it would be to implement? Does GEE export time series data? What might be a viable workaround? lapply is a bit messy due the need to select options for each loops...

All in all, good work! Look forward to further developments!

[arupakaa]

Got a solution. Ugly, but works. Use lapply, as suggested above. Might need testCase = "n".

Here's an example, grabs weekly data, for each year, over a 10 year time-span; 12 hour run-time:

## set date range and temporal granularity
timeSeq <- seq(as.Date('2008-01-01'),as.Date('2019-01-01'),  ## define date range
               by = 8) ## define temporal granularity (# of days to include per period)

## setup function
processGEE <- function(datasetID=NA, timeSeq=NA, scaleFactor=NA, runTest = F){ ## input time sequence

  if(runTest == T) {i=1:3    ## for debug
  } else {i=1:(length(timeSeq) - 1)}  ## let loop duration for sequence n

  lapply(i, function(i) {

    ee_grab(data = ee_data_collection(datasetID = datasetID, ## select dataset from web
                                      spatialReducer = "mean",  ## select spatial aggregator function
                                      temporalReducer = "mode", ## select temporal aggregation
                                      ## average, cummulative, max, min... other prob not relevan?
                                      timeStart = as.character(timeSeq[[i]]),
                                      timeEnd = as.character(timeSeq[[i+1]]), 
                                      resolution = scaleFactor),
            targetArea = "NUTS-DATA/NUTS_RG_60M_2016_4326_LEVL_3.geojson",  ## download from Euronuts website
            testCase = "n"
    ) %>% as.data.frame()

  }) %>% Reduce(merge, .) %>% st_as_sf()

}