![](../logo.png)
Fill data gaps and smooth outliers in a time series of satellite images
Source:R/rsat_smoothing_images.R
rsat_smoothing_images.Rd
apply_ima
is the implementation of a spatio-temporal method
called Interpolation of Mean Anomalies(IMA) for gap filling and smoothing
satellite data (Militino et al. 2019)
.
smoothing_images
is the implementation of a spatio temporal method
called image mean anomaly (IMA) for gap filling and smoothing satellite
data (Militino et al. 2019)
.
Usage
rsat_smoothing_images(x, method, ...)
# S4 method for class 'rtoi,character'
rsat_smoothing_images(
x,
method,
product = "ALL",
satellite = "ALL",
stage = "ALL",
variable = "ALL",
test.mode = FALSE,
...
)
# S4 method for class 'SpatRaster,character'
rsat_smoothing_images(x, method, ...)
Arguments
- x
rtoi
orRastespatRaster
containing a time series of satellite images.- method
character argument. Defines the method used for processing the images, e.a. "IMA".
- ...
arguments for nested functions:
Img2Fill
avector
defining the images to be filled/smoothed.r.dates
avector
of dates for the layers inx
. Mandatory when layer names ofx
do not contain their capturing dates "YYYYJJJ
" format.nDays
anumeric
argument with the number of previous and subsequent days of the temporal neighborhood.nYears
anumeric
argument with the number of previous and subsequent years of the temporal neighborhood.aFilter
avector
of lower and upper quantiles defining the outliers in the anomalies. Ex. c(0.05,0.95).fact
anumeric
argument specifying the aggregation factor of the anomalies.fun
afunction
used to aggregate the image of anomalies. Bothmean
(default) ormedian
are accepted.snow.mode
logical argument. IfTRUE
, the process is parallelized using the functionalities from the `raster
' package.predictSE
calculate the standard error instead the prediction.factSE
thefact
used in the standard error prediction.out.name
the name of the folder containing the smoothed/filled images when saved in the Hard Disk Device (HDD).only.na
logical argument. IfTRUE
only fills theNA
values.FALSE
by default.
- product
character argument. The name of the product to be processed. Check the name of the parameter with
rsat_list_data
function. Check the name of the parameter withrsat_list_data
function. By default, "ALL".- satellite
character argument. The name of the satellite to be processed. Check the name of the parameter with
rsat_list_data
function. By default, "ALL".- stage
character argument. The name of the processed stage of the data. Check the name of the parameter with
rsat_list_data
function. By default, "ALL".- variable
character argument.The name of the variable to be processed. Check the name of the parameter with
rsat_list_data
function. By default, "ALL".- test.mode
logical argument. If
TRUE
, the function runs some lines to testrsat_smoothing_images
with rtoi object.
Details
This filling/smoothing method was developed by
Militino et al. (2019)
. IMA fills the gaps
borrowing information from an adaptable temporal neighborhood. Two
parameters determine the size of the neighborhood; the number of days
before and after the target image (nDays
) and the number of previous
and subsequent years (nYears
). Both parameters should be adjusted
based on the temporal resolution of the of the time-series of images. We
recommend that the neighborhood extends over days rather than years, when
there is little resemblance between seasons. Also, cloudy series may require
larger neighborhoods.
IMA gives the following steps; (1) creates a representative image from the
temporal neighborhood of the target image (image to be filled/smoothed) e.g.,
doing the mean, median, etc. for each pixel's time-series (fun
), (2)
the target and representative images are subtracted giving an image of
anomalies, (3) the anomalies falling outside the quantile limits
(aFilter
) are considered outliers and therefore removed, (4) it
aggregates the anomaly image into a coarser resolution (fact
) to
reveal potential spatial dependencies, (5) the procedure fits a spatial
model (thin plate splines or TPS) to the anomalies which is then used to
interpolate the values at the original resolution, and (6) the output
is the sum of the interpolated anomalies and the average image.
References
Militino AF, Ugarte MD, Perez-Goya U, Genton MG (2019). “Interpolation of the Mean Anomalies for Cloud-Filling in Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI).” IEEE Transactions on Geoscience and Remote Sensing. (Open-Access). http://dx.doi.org/10.1109/TGRS.2019.2904193.
Examples
if (FALSE) { # \dontrun{
## Smooth data in rtoi
library(rsat)
require(terra)
# create a copy of pamplona in temp file
file.copy(from=system.file("ex/PamplonaDerived",package="rsat"),
to=tempdir(),
recursive = TRUE)
# load example rtoi
pamplona <- read_rtoi(file.path(tempdir(),"PamplonaDerived"))
rsat_smoothing_images(pamplona,
method = "IMA",
variable="NDVI"
)
rsat_list_data(pamplona)
# get smoothed
smoothed <- rsat_get_SpatRaster(pamplona,p="mod09ga",v="NDVI",s="IMA")
plot(smoothed)
# get original
original <- rsat_get_SpatRaster(pamplona,p="mod09ga",v="NDVI",s="variables")
plot(original)
plot(smoothed[[1]]-original[[1]])
## smooth user defined SpatRaster dataset
require(terra)
data(ex.ndvi.navarre)
# load an example of NDVI time series in Navarre
ex.ndvi.navarre <- rast(ex.ndvi.navarre)
# the raster stack with the date in julian format as name
plot(ex.ndvi.navarre)
# smoothin and fill all the time series
tiles.mod.ndvi.filled <- rsat_smoothing_images(ex.ndvi.navarre,
method = "IMA"
)
# show the filled images
plot(tiles.mod.ndvi.filled)
# plot comparison of the cloud and the filled images
tiles.mod.ndvi.comp <- c(
ex.ndvi.navarre[[1]], tiles.mod.ndvi.filled[[1]],
ex.ndvi.navarre[[2]], tiles.mod.ndvi.filled[[2]]
)
plot(tiles.mod.ndvi.comp)
} # }