Creates a task to export an EE Image to their EE Assets. This function is a wrapper around ee$batch$Export$image$toAsset(...).

ee_image_to_asset(
image,
assetId = NULL,
overwrite = FALSE,
pyramidingPolicy = NULL,
dimensions = NULL,
region = NULL,
scale = NULL,
crs = NULL,
crsTransform = NULL,
maxPixels = NULL
)

## Arguments

image

The image to be exported.

description

assetId

The destination asset ID.

overwrite

Logical. If TRUE, the assetId will be overwritten if it exists.

pyramidingPolicy

The pyramiding policy to apply to each band in the image, a dictionary keyed by band name. Values must be one of: "mean", "sample", "min", "max", or "mode". Defaults to "mean". A special key, ".default", may be used to change the default for all bands.

dimensions

The dimensions of the exported image. It takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.

region

The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. It can be specified as nested lists of numbers or a serialized string. Defaults to the image's region.

scale

The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified.

crs

The coordinate reference system of the exported image's projection. Defaults to the image's default projection.

crsTransform

A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale, and yTranslation. Defaults to the image's native CRS transform.

maxPixels

The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000. **kwargs: Holds other keyword arguments that may have been deprecated, such as 'crs_transform'.

## Value

Other image export task creator: ee_image_to_drive(), ee_image_to_gcs()

## Examples

if (FALSE) {
library(rgee)
library(stars)
library(sf)

ee_users()
ee_Initialize()

# Define study area (local -> earth engine)
# Communal Reserve Amarakaeri - Peru
rlist <- list(xmin = -71.13, xmax = -70.95,ymin = -12.89, ymax = -12.73)
ROI <- c(rlist$xmin, rlist$ymin,
rlist$xmax, rlist$ymin,
rlist$xmax, rlist$ymax,
rlist$xmin, rlist$ymax,
rlist$xmin, rlist$ymin)
ee_ROI <- matrix(ROI, ncol = 2, byrow = TRUE) %>%
list() %>%
st_polygon() %>%
st_sfc() %>%
st_set_crs(4326) %>%
sf_as_ee()

# Get the mean annual NDVI for 2011
qa <- image$select("pixel_qa") cloud <- qa$bitwiseAnd(32L)$And(qa$bitwiseAnd(128L))$Or(qa$bitwiseAnd(8L))
mask2 <- image$mask()$reduce(ee$Reducer$min())
image <- image$updateMask(cloud$Not())$updateMask(mask2) image$normalizedDifference(list("B4", "B3"))
}

ic_l5 <- ee$ImageCollection("LANDSAT/LT05/C01/T1_SR")$
filterBounds(ee$FeatureCollection(ee_ROI))$
filterDate("2011-01-01", "2011-12-31")$map(cloudMaskL457) # Create simple composite mean_l5 <- ic_l5$mean()$rename("NDVI") mean_l5 <- mean_l5$reproject(crs = "EPSG:4326", scale = 500)
mean_l5_Amarakaeri <- mean_l5$clip(ee_ROI) # Move results from Earth Engine to Drive assetid <- paste0(ee_get_assethome(), '/l5_Amarakaeri') task_img <- ee_image_to_asset( image = mean_l5_Amarakaeri, assetId = assetid, overwrite = TRUE, scale = 500, region = ee_ROI ) task_img$start()
ee_l5 <- ee$Image(assetid) Map$centerObject(ee_l5)