Merge two data sets (time series or cubes)

To merge two series, we consider that they contain different attributes but refer to the same data cube and spatiotemporal location. This function is useful for merging different bands of the same location. For example, one may want to put the raw and smoothed bands for the same set of locations in the same tibble.

In the case of data cubes, the function merges the images based on the following conditions:

If the two cubes have different bands but compatible timelines, the bands are combined, and the timeline is adjusted to overlap. To create the overlap, we align the timelines like a "zipper": for each interval defined by a pair of consecutive dates in the first timeline, we include matching dates from the second timeline. If the second timeline has multiple dates in the same interval, only the minimum date is kept. This ensures the final timeline avoids duplicates and is consistent. This is useful when merging data from different sensors (e.g., Sentinel-1 with Sentinel-2).
If the bands are the same, the cube will have the combined timeline of both cubes. This is useful for merging data from the same sensors from different satellites (e.g., Sentinel-2A with Sentinel-2B).
otherwise, the function will produce an error.

Usage

sits_merge(data1, data2, ...)

# S3 method for class 'sits'
sits_merge(data1, data2, ..., suffix = c(".1", ".2"))

# S3 method for class 'raster_cube'
sits_merge(data1, data2, ...)

# Default S3 method
sits_merge(data1, data2, ...)

Arguments

data1: Time series (tibble of class "sits") or data cube (tibble of class "raster_cube") .
data2: Time series (tibble of class "sits") or data cube (tibble of class "raster_cube") .
...: Additional parameters
suffix: If data1 and data2 are tibble with duplicate bands, this suffix will be added (character vector).

Value

merged data sets (tibble of class "sits" or tibble of class "raster_cube")

Author

Felipe Carvalho, felipe.carvalho@inpe.br

Felipe Carlos, efelipecarlos@gmail.com

Examples

if (sits_run_examples()) {
    # Retrieve a time series with values of NDVI
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")

    # Filter the point using the Whittaker smoother
    point_whit <- sits_filter(point_ndvi, sits_whittaker(lambda = 3.0))
    # Merge time series
    point_ndvi <- sits_merge(point_ndvi, point_whit, suffix = c("", ".WHIT"))

    # Plot the two points to see the smoothing effect
    plot(point_ndvi)
}