diff --git a/.gitignore b/.gitignore
index c5fd67e53..d277f0384 100644
--- a/.gitignore
+++ b/.gitignore
@@ -24,6 +24,5 @@ Meta
 *.bkp
 Makevars
 Makevars.*
-*.json
 *.gcda
 *.gcno
diff --git a/DESCRIPTION b/DESCRIPTION
index 66f8795ec..45d2c81ea 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: sits
 Type: Package
-Version: 0.14.0-2
+Version: 0.14.1
 Title: Satellite Image Time Series Analysis for Remote Sensing Data Cubes
 Authors@R: c(person('Rolf', 'Simoes', role = c('aut'), email = 'rolf.simoes@inpe.br'),
              person('Gilberto', 'Camara', role = c('aut', 'cre'), email = 'gilberto.camara@inpe.br'),
@@ -156,6 +156,7 @@ Collate:
     'sits_source_api_deafrica.R'
     'sits_source_api_local.R'
     'sits_source_api_probs.R'
+    'sits_source_api_opendata.R'
     'sits_source_api_satveg.R'
     'sits_source_api_stac.R'
     'sits_source_api_usgs.R'
diff --git a/NAMESPACE b/NAMESPACE
index fb9340f6f..0d634682a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -6,6 +6,8 @@ S3method("sits_bands<-",predicted)
 S3method("sits_bands<-",sits)
 S3method("sits_labels<-",pattern)
 S3method("sits_labels<-",sits)
+S3method(.gc_format_col,aws_cube)
+S3method(.gc_format_col,opendata_cube)
 S3method(.raster_check_package,default)
 S3method(.raster_check_package,raster)
 S3method(.raster_check_package,terra)
@@ -74,18 +76,22 @@ S3method(.source_cube,wtss_cube)
 S3method(.source_item_get_bands,aws_cube)
 S3method(.source_item_get_bands,bdc_cube)
 S3method(.source_item_get_bands,deafrica_cube)
+S3method(.source_item_get_bands,opendata_cube)
 S3method(.source_item_get_bands,usgs_cube)
 S3method(.source_item_get_date,aws_cube)
 S3method(.source_item_get_date,bdc_cube)
 S3method(.source_item_get_date,deafrica_cube)
+S3method(.source_item_get_date,opendata_cube)
 S3method(.source_item_get_date,usgs_cube)
 S3method(.source_item_get_hrefs,aws_cube)
 S3method(.source_item_get_hrefs,bdc_cube)
 S3method(.source_item_get_hrefs,deafrica_cube)
+S3method(.source_item_get_hrefs,opendata_cube)
 S3method(.source_item_get_hrefs,usgs_cube)
 S3method(.source_item_get_resolutions,aws_cube)
 S3method(.source_item_get_resolutions,bdc_cube)
 S3method(.source_item_get_resolutions,deafrica_cube)
+S3method(.source_item_get_resolutions,opendata_cube)
 S3method(.source_item_get_resolutions,usgs_cube)
 S3method(.source_items_bands_select,aws_cube)
 S3method(.source_items_bands_select,stac_cube)
@@ -96,27 +102,33 @@ S3method(.source_items_fileinfo,wtss_cube)
 S3method(.source_items_new,aws_cube)
 S3method(.source_items_new,bdc_cube)
 S3method(.source_items_new,deafrica_cube)
+S3method(.source_items_new,opendata_cube)
 S3method(.source_items_new,usgs_cube)
 S3method(.source_items_new,wtss_cube)
 S3method(.source_items_tile_get_bbox,aws_cube)
 S3method(.source_items_tile_get_bbox,bdc_cube)
 S3method(.source_items_tile_get_bbox,deafrica_cube)
+S3method(.source_items_tile_get_bbox,opendata_cube)
 S3method(.source_items_tile_get_bbox,usgs_cube)
 S3method(.source_items_tile_get_crs,aws_cube)
 S3method(.source_items_tile_get_crs,bdc_cube)
 S3method(.source_items_tile_get_crs,deafrica_cube)
+S3method(.source_items_tile_get_crs,opendata_cube)
 S3method(.source_items_tile_get_crs,usgs_cube)
 S3method(.source_items_tile_get_name,aws_cube)
 S3method(.source_items_tile_get_name,bdc_cube)
 S3method(.source_items_tile_get_name,deafrica_cube)
+S3method(.source_items_tile_get_name,opendata_cube)
 S3method(.source_items_tile_get_name,usgs_cube)
 S3method(.source_items_tile_get_size,aws_cube)
 S3method(.source_items_tile_get_size,bdc_cube)
 S3method(.source_items_tile_get_size,deafrica_cube)
+S3method(.source_items_tile_get_size,opendata_cube)
 S3method(.source_items_tile_get_size,usgs_cube)
 S3method(.source_items_tiles_group,aws_cube)
 S3method(.source_items_tiles_group,bdc_cube)
 S3method(.source_items_tiles_group,deafrica_cube)
+S3method(.source_items_tiles_group,opendata_cube)
 S3method(.source_items_tiles_group,usgs_cube)
 S3method(plot,classified_image)
 S3method(plot,keras_model)
@@ -144,6 +156,7 @@ S3method(sits_cube,bdc_cube)
 S3method(sits_cube,deafrica_cube)
 S3method(sits_cube,default)
 S3method(sits_cube,local_cube)
+S3method(sits_cube,opendata_cube)
 S3method(sits_cube,probs_cube)
 S3method(sits_cube,satveg_cube)
 S3method(sits_cube,wtss_cube)
@@ -216,6 +229,7 @@ export(sits_labels)
 export(sits_labels_summary)
 export(sits_lda)
 export(sits_linear_interp)
+export(sits_list_collections)
 export(sits_merge)
 export(sits_metadata_to_csv)
 export(sits_missing_values)
diff --git a/NEWS.md b/NEWS.md
index 02352a30f..4439b6348 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,6 +3,25 @@
 We are preparing to release the package on CRAN and are making relevant changes in the development version.
 
 
+# What's new in SITS version 0.14.1
+
+### New features in SITS version 0.14.1
+
+* Implement new function `sits_list_collections()`
+* Update gdalcubes parameters
+* Implement `.source_bands_resampling()`
+* Remove name from demo file
+* Improve `sits_som_clean_samples()` function
+* Improve `sits_bands<-()` function
+* Improve `sits_select()` function
+* Error in cloud band of CBERS4 data example
+* Include a function to list collections available in cloud services
+* sits_cube_copy() does not include information on the tile
+* Get spatial resolution from config file
+* Fix partial merge configuration file
+* Change bbox to roi in sits
+
+
 # What's new in SITS version 0.14.0
 
 ### New features in SITS version 0.14.0-2
diff --git a/R/sits_bands.R b/R/sits_bands.R
index 839a6136b..09ee2ba60 100644
--- a/R/sits_bands.R
+++ b/R/sits_bands.R
@@ -35,7 +35,7 @@ sits_bands <- function(x) {
 #'
 sits_bands.sits <- function(x) {
 
-    return(names(sits_time_series(x))[-1])
+    return(setdiff(names(sits_time_series(x)), "Index"))
 }
 
 #' @export
diff --git a/R/sits_bands_set.R b/R/sits_bands_set.R
index 0cb872d92..4b21b2dbe 100644
--- a/R/sits_bands_set.R
+++ b/R/sits_bands_set.R
@@ -21,23 +21,38 @@
     # set caller to show in errors
     .check_set_caller("sits_bands")
 
-    # get the time series
-    ts <- sits_time_series(x)
-    .check_that(
-        x = ncol(ts) == length(value) + 1,
-        msg = "invalid number of bands to be replaced")
+    # get the data bands
+    data_bands <- sits_bands(x)
 
-    # change band names for all rows of the tibble
-    rows <- slider::slide(x, function(row) {
-        ts <- sits_time_series(row)
-        names(ts) <- c("Index", value)
-        row$time_series[[1]] <- ts
-        return(row)
-    })
-    x <- dplyr::bind_rows(rows)
+    .check_chr(value, allow_empty = FALSE, len_min = length(data_bands),
+               len_max = length(data_bands),
+               msg = "Invalid bands values to be replaced")
+
+    # create an row_id to use later in nest
+    x[["..row_id"]] <- seq_len(nrow(x))
+
+    # unnest bands
+    x <- tidyr::unnest(x, cols = "time_series")
+
+    # here, you could pass a function to process fast
+    new_bands <- colnames(x)
+    names(new_bands) <- new_bands
+
+    new_bands[data_bands] <- toupper(value)
+    colnames(x) <- unname(new_bands)
+
+    # nest again
+    x <- tidyr::nest(x,  time_series = c("Index", toupper(value)))
+
+    # remove ..row_id
+    x <- dplyr::select(x, -"..row_id")
+
+    # set sits tibble class
+    class(x) <- c("sits", class(x))
 
     return(x)
 }
+
 #' @export
 #'
 `sits_bands<-.cube` <- function(x, value) {
diff --git a/R/sits_classification.R b/R/sits_classification.R
index 32a729f1e..38aea60c2 100644
--- a/R/sits_classification.R
+++ b/R/sits_classification.R
@@ -54,7 +54,7 @@
 #'    The "roi" parameter defines a region of interest. It can be
 #'    an sf_object, a shapefile, or a bounding box vector with
 #'    named XY values ("xmin", "xmax", "ymin", "ymax") or
-#'    named lat/long values ("lat_min", "lat_max", "lon_min", "lon_max")
+#'    named lat/long values ("lon_min", "lat_min", "lon_max", "lat_max")
 #'
 #'    The "filter_fn" parameter specifies a smoothing filter to be applied to
 #'    time series for reducing noise. Currently, options include
diff --git a/R/sits_config.R b/R/sits_config.R
index 60e3da73a..30e37cf53 100644
--- a/R/sits_config.R
+++ b/R/sits_config.R
@@ -13,9 +13,13 @@
 #'
 #' \code{sits_config_show()} prints the current sits
 #' configuration options. To show specific configuration options for
-#' a source, a collection, or a palette, user can inform the corresponding
+#' a source, a collection, or a palette, users can inform the corresponding
 #' keys to \code{source}, \code{collection}, and \code{palette} parameters.
 #'
+#' \code{sits_list_collections()} prints the collections available
+#' in each cloud service supported by sits. Users can select to get information
+#' only for a single service by using the \code{source} parameter.
+#'
 #' @param processing_bloat       A \code{numeric} value to estimate
 #' growth size of R memory relative to block size.
 #' @param rstac_pagination_limit A \code{numeric} value indicating number of
@@ -81,6 +85,9 @@ sits_config <- function(processing_bloat = NULL,
         message(paste("Additional configurations found in", user_yml_file))
         config <- yaml::yaml.load_file(input = user_yml_file,
                                        merge.precedence = "override")
+        config <- utils::modifyList(sits_env[["config"]],
+                                    config,
+                                    keep.null = FALSE)
 
         # set options defined by user (via YAML file)
         # modifying existing configuration
@@ -190,6 +197,49 @@ sits_config_show <- function(source = NULL,
     return(invisible(config))
 }
 
+#' @rdname sits_configuration
+#'
+#' @return
+#' \code{sits_list_collections()} prints the collections available in
+#' each cloud service supported by sits.
+#'
+#' @export
+sits_list_collections <- function(source = NULL) {
+
+    # get sources available
+    sources <- .sources(internal = FALSE)
+
+    # if the user has required a source
+    # check that it is valid
+    if (!purrr::is_null(source)) {
+        # check if source exists
+        .check_chr_within(
+            x = source,
+            within = sources,
+            msg = "invalid source value"
+        )
+        sources <- source
+    }
+
+    purrr::map(sources, function(s){
+
+        cat(paste0(s, ":\n"))
+        collections <- .source_collections(source = s)
+        purrr::map(collections, function(c){
+            # get collection information
+
+            cat(paste0("- ", c))
+            cat(paste0(" (", .source_collection_satellite(s, c),
+                       "/", .source_collection_sensor(s, c), ")\n"))
+            cat("- bands: ")
+            cat(.source_bands(s, c))
+            cat("\n")
+            cat("\n")
+        })
+    })
+    return(invisible(NULL))
+}
+
 .config_set_options <- function(processing_bloat = NULL,
                                 rstac_pagination_limit = NULL,
                                 raster_api_package = NULL,
diff --git a/R/sits_cube.R b/R/sits_cube.R
index 2bb9bc8d3..7f17df165 100755
--- a/R/sits_cube.R
+++ b/R/sits_cube.R
@@ -95,7 +95,7 @@
 #' @param bands             Bands to be included.
 #' @param tiles             Tiles from the collection to be included in the
 #'                          data cube.
-#' @param bbox              Area of interest (see details below).
+#' @param roi               Region of interest (see details below).
 #' @param start_date        Initial date for the cube (optional).
 #' @param end_date          Final date for the cube  (optional).
 #' @param s2_resolution     Resolution of S2 images ("10m", "20m" or "60m").
@@ -114,8 +114,8 @@
 #'                          probabilities).
 #' @param probs_labels      Labels associated to a probabilities cube.
 #'
-#' @details The \code{bbox} parameter allows a selection of an area of interest.
-#' Either using a named \code{vector} ("xmin", "ymin", "xmax", "ymax") with
+#' @details The \code{roi} parameter allows a selection of an area of interest.
+#' Either using a named \code{vector} ("lon_min", "lat_min", "lon_max", "lat_max") with
 #' values in WGS 84, a \code{sfc} or \code{sf} object from sf package, or a
 #' GeoJSON geometry (RFC 7946). Note that this parameter does not crop a
 #' region, but only selects the images that intersect with it.
@@ -186,14 +186,14 @@
 #'                       s2_resolution = 20
 #' )
 #'
-#' # --- Create a cube based on a stack of CBERS data
-#' data_dir <- system.file("extdata/raster/cbers", package = "sits")
+#' # --- Create a cube based on a local MODIS data
+#' data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 #'
-#' cbers_cube <- sits_cube(
+#' modis_cube <- sits_cube(
 #'     source = "LOCAL",
-#'     name = "022024",
+#'     name = "modis_sinop",
 #'     origin = "BDC",
-#'     collection = "CB4_64-1",
+#'     collection = "MOD13Q1-6",
 #'     data_dir = data_dir,
 #'     delim = "_",
 #'     parse_info = c("X1", "X2", "tile", "band", "date")
@@ -272,7 +272,7 @@ sits_cube.bdc_cube <- function(source = "BDC", ...,
                                collection,
                                bands = NULL,
                                tiles = NULL,
-                               bbox = NULL,
+                               roi = NULL,
                                start_date = NULL,
                                end_date = NULL) {
 
@@ -305,7 +305,7 @@ sits_cube.bdc_cube <- function(source = "BDC", ...,
                  name = name,
                  bands = bands,
                  tiles = tiles,
-                 bbox = bbox,
+                 bbox = roi,
                  start_date = start_date,
                  end_date = end_date, ...)
 }
@@ -319,7 +319,7 @@ sits_cube.deafrica_cube <- function(source = "DEAFRICA", ...,
                                     collection = "S2_L2A",
                                     bands = NULL,
                                     tiles = NULL,
-                                    bbox = NULL,
+                                    roi = NULL,
                                     start_date = NULL,
                                     end_date = NULL) {
 
@@ -354,7 +354,7 @@ sits_cube.deafrica_cube <- function(source = "DEAFRICA", ...,
                  name = name,
                  bands = bands,
                  tiles = tiles,
-                 bbox = bbox,
+                 bbox = roi,
                  start_date = start_date,
                  end_date = end_date, ...)
 }
@@ -368,7 +368,7 @@ sits_cube.aws_cube <- function(source = "AWS", ...,
                                collection = "sentinel-s2-l2a",
                                tiles = NULL,
                                bands = NULL,
-                               bbox = NULL,
+                               roi = NULL,
                                s2_resolution = 20,
                                start_date = NULL,
                                end_date = NULL) {
@@ -415,12 +415,58 @@ sits_cube.aws_cube <- function(source = "AWS", ...,
                  name = name,
                  bands = bands,
                  tiles = tiles,
-                 bbox = bbox,
+                 bbox = roi,
                  start_date = start_date,
                  end_date = end_date, ...,
                  s2_resolution = s2_resolution)
 }
 
+#' @rdname sits_cube
+#'
+#' @export
+sits_cube.opendata_cube <- function(source = "OPENDATA", ...,
+                                    name = "opendata_cube",
+                                    url = NULL,
+                                    collection = "sentinel-s2-l2a-cogs",
+                                    tiles = NULL,
+                                    bands = NULL,
+                                    roi = NULL,
+                                    start_date = NULL,
+                                    end_date = NULL) {
+
+
+    # collection name is upper case
+    collection <- toupper(collection)
+
+    # suite of checks to verify collection parameter
+    .check_collection(source = source,
+                      collection = collection)
+
+    if (is.null(bands))
+        bands <- .source_bands(source = source,
+                               collection = collection)
+
+    # Pre-condition - checks if the bands are supported by the collection
+    .check_bands(source = source,
+                 collection = collection,
+                 bands = bands)
+
+    # dry run to verify if service is running
+    .source_access_test(source = source,
+                        collection = collection, ...,
+                        bands = bands)
+
+    # builds a sits data cube
+    .source_cube(source = source,
+                 collection = collection,
+                 name = name,
+                 bands = bands,
+                 tiles = tiles,
+                 bbox = roi,
+                 start_date = start_date,
+                 end_date = end_date, ...)
+}
+
 #' @rdname sits_cube
 #' @keywords internal
 sits_cube.usgs_cube <- function(source = "USGS", ...,
@@ -429,7 +475,7 @@ sits_cube.usgs_cube <- function(source = "USGS", ...,
                                 collection = "landsat-c2l2-sr",
                                 tiles = NULL,
                                 bands = NULL,
-                                bbox = NULL,
+                                roi = NULL,
                                 start_date = NULL,
                                 end_date = NULL) {
 
@@ -471,7 +517,7 @@ sits_cube.usgs_cube <- function(source = "USGS", ...,
                  name = name,
                  bands = bands,
                  tiles = tiles,
-                 bbox = bbox,
+                 bbox = roi,
                  start_date = start_date,
                  end_date = end_date, ...)
 }
@@ -595,20 +641,20 @@ sits_cube.default <- function(source, ...) {
 #'
 #' @examples
 #' \donttest{
-#' data_dir <- system.file("extdata/raster/cbers", package = "sits")
+#' data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 #'
-#' cbers_022024 <- sits_cube(
+#' modis_cube <- sits_cube(
 #'     source = "LOCAL",
-#'     name = "cbers_022024",
+#'     name = "modis_sinop",
 #'     origin = "BDC",
-#'     collection = "CB4_64-1",
+#'     collection = "MOD13Q1-6",
 #'     band = "NDVI",
 #'     data_dir = data_dir,
 #'     parse_info = c("X1", "X2", "tile", "band", "date")
 #' )
 #'
-#' cbers_022024_copy <- sits_cube_copy(cbers_022024,
-#'     name = "cb_022024_cp",
+#' modis_cube_copy <- sits_cube_copy(modis_cube,
+#'     name = "modis_sinop_cp",
 #'     dest_dir = tempdir()
 #' )
 #' }
@@ -665,8 +711,10 @@ sits_cube_copy <- function(cube,
 
         # get all the bands which are requested
         file_info_out <- dplyr::filter(file_info, band %in% bands)
+        # remove token (if existing)
+        file_no_token <- gsub("^([^?]+)(\\?.*)?$", "\\1", file_info_out$path[[1]])
         # get the file extension
-        file_ext <- tools::file_ext(file_info_out$path[1])
+        file_ext <- tools::file_ext(file_no_token)
 
         if (file_ext == "jp2") {
             gdal_of <- "JP2OpenJPEG"
@@ -680,6 +728,7 @@ sits_cube_copy <- function(cube,
                 dest_dir, "/",
                 row$satellite, "_",
                 row$sensor, "_",
+                row$tile,"_",
                 file_row$band, "_",
                 file_row$date, ".",
                 file_ext
@@ -1304,7 +1353,7 @@ NULL
                         .source_bands_to_sits(source = source,
                                               collection = collection,
                                               bands = bands),
-                    collapse = ", ")),
+                        collapse = ", ")),
             call. = FALSE)
 
     return(invisible(NULL))
diff --git a/R/sits_gdalcubes.R b/R/sits_gdalcubes.R
index 14fc82c2b..a84dd2421 100644
--- a/R/sits_gdalcubes.R
+++ b/R/sits_gdalcubes.R
@@ -1,5 +1,5 @@
 #' @title Save the images based on an aggregation method.
-#' @name .sits_gc_compose
+#' @name .gc_new_cube
 #' @keywords internal
 #'
 #' @param tile        A data cube tile
@@ -9,8 +9,8 @@
 #' @param cv          A \code{list} 'cube_view' with values from cube.
 #' @param cloud_mask  A \code{logical} corresponds to the use of the cloud band
 #'  for aggregation.
-#' @param db_file     Database to be created by gdalcubes
-#' @param dir_images  Directory where the aggregated images will be written.
+#' @param path_db     Database to be created by gdalcubes
+#' @param output_dir  Directory where the aggregated images will be written.
 #' @param cloud_mask  A \code{logical} corresponds to the use of the cloud band
 #'  for aggregation.
 #' @param ...         Additional parameters that can be included. See
@@ -18,93 +18,82 @@
 #' @param version     A \code{character} with version of the output files.
 #'
 #' @return  A data cube tile with information used in its creation.
-.sits_gc_compose <- function(tile,
-                             name,
-                             cv,
-                             img_col,
-                             db_file,
-                             dir_images,
-                             cloud_mask, ...,
-                             version = "v1") {
+.gc_new_cube <- function(tile,
+                         name,
+                         cv,
+                         img_col,
+                         path_db,
+                         output_dir,
+                         cloud_mask, ...,
+                         version = "v1") {
 
     # set caller to show in errors
-    .check_set_caller(".sits_gc_compose")
-
-    .check_that(
-        x = nrow(tile) == 1,
-        msg = "tile must have only one row."
-    )
-
-    # verifies the path to save the images
-    .check_that(
-        x = dir.exists(dir_images),
-        msg = "The provided dir does not exist. Please provided a valid path."
-    )
+    .check_set_caller(".gc_new_cube")
 
     # create a clone cube
     cube_gc <- .sits_cube_clone(
         cube = tile,
         name = name,
         ext = "",
-        output_dir = dir_images,
-        version = version)
+        output_dir = output_dir,
+        version = version
+    )
 
-    # delete file info column
-    cube_gc$file_info <- NULL
+    # update cube metadata
+    cube_gc <- .gc_update_metadata(cube = cube_gc, cube_view = cv)
 
     # create file info column
-    file_info <- tibble::tibble(band = character(),
-                                date = lubridate::as_date(""),
-                                res  = numeric(),
-                                path = character())
-    # add file info and path db columns
-    cube_gc <- tibble::add_column(cube_gc, file_info = list(file_info))
-
-    for (band in .cube_bands(tile)) {
-        # create a raster_cube object from gdalcubes
-        cube_brick <- .sits_gc_brick(tile, img_col, cv, cloud_mask)
-
-        band_source <- .source_bands_to_source(
-            source = .cube_source(tile),
-            collection = .cube_collection(tile),
-            bands = band
-        )
+    cube_gc$file_info[[1]] <- tibble::tibble(band = character(),
+                                             date = lubridate::as_date(""),
+                                             res  = numeric(),
+                                             path = character())
+
+    for (band in .cube_bands(tile, add_cloud = FALSE)) {
+
 
-        message(paste("Writing images of band", band, "of tile",
-                      tile$tile))
+        cv$resampling <- .source_bands_resampling(source = tile$source,
+                                                  collection = tile$collection,
+                                                  bands = band)
+
+        # create a raster_cube object to each band the select below change
+        # the object value
+        cube_brick <- .gc_raster_cube(tile, img_col, cv, cloud_mask)
+
+        message(paste("Writing images of band", band, "of tile", tile$tile))
 
         # write the aggregated cubes
         path_write <- gdalcubes::write_tif(
-            gdalcubes::select_bands(cube_brick, band_source),
-            dir = dir_images,
+            gdalcubes::select_bands(cube_brick, band),
+            dir = output_dir,
             prefix = paste("cube", tile$tile, band, "", sep = "_"),
+            creation_options = list("COMPRESS" = "LZW", "BIGTIFF" = "YES"),
             write_json_descr = TRUE, ...)
 
         # retrieving image date
-        images_date <- .sits_gc_date(path_write)
-        res <- dplyr::filter(tile$file_info[[1]], band == band)$res[[1]]
+        images_date <- .gc_get_date(path_write)
 
         # set file info values
         cube_gc$file_info[[1]] <- tibble::add_row(
             cube_gc$file_info[[1]],
             band = rep(band, length(path_write)),
             date = images_date,
-            res  = rep(res, length(path_write)),
+            res  = rep(cv$space$dx, length(path_write)),
             path = path_write
         )
     }
 
     return(cube_gc)
 }
+
 #' @title Extracted date from aggregated cubes
-#' @name .sits_gc_date
+#' @name .gc_get_date
 #' @keywords internal
 #'
 #' @param dir_images A \code{character}  corresponds to the path on which the
 #'  images will be saved.
 #'
 #' @return a \code{character} vector with the dates extracted.
-.sits_gc_date <- function(dir_images) {
+.gc_get_date <- function(dir_images) {
 
     # get image name
     image_name <- basename(dir_images)
@@ -127,8 +116,9 @@
 
     return(date_files)
 }
+
 #' @title Create a raster_cube object
-#' @name .sits_gc_brick
+#' @name .gc_raster_cube
 #' @keywords internal
 #'
 #' @param cube       Data cube from where data is to be retrieved.
@@ -140,7 +130,7 @@
 #'
 #' @return a \code{object} 'raster_cube' from gdalcubes containing information
 #'  about the cube brick metadata.
-.sits_gc_brick <- function(cube, img_col, cv, cloud_mask) {
+.gc_raster_cube <- function(cube, img_col, cv, cloud_mask) {
 
     # defining the chunk size
     c_size <- c(t = 1,
@@ -149,7 +139,7 @@
 
     mask_band <- NULL
     if (cloud_mask)
-        mask_band <- .sits_gc_cloud_mask(cube)
+        mask_band <- .gc_cloud_mask(cube)
 
     # create a brick of raster_cube object
     cube_brick <- gdalcubes::raster_cube(image_collection = img_col,
@@ -159,15 +149,37 @@
 
     return(cube_brick)
 }
+
+#' @title Update metadata from sits cube using gdalcubes metadata
+#' @name .gc_update_metadata
+#' @keywords internal
+#'
+#' @param cube       Data cube from where data is to be retrieved.
+#' @param cv         A \code{object} 'cube_view' with values from cube.
+#'  for aggregation.
+#'
+#' @return a \code{sits_cube} object with updated metadata.
+.gc_update_metadata <- function(cube, cube_view) {
+
+    # update bbox
+    bbox_names <- c("xmin", "xmax", "ymin", "ymax")
+    cube[, bbox_names] <- cube_view$space[c("left", "right", "bottom", "top")]
+
+    # update nrows and ncols
+    cube[, c("nrows", "ncols")] <- cube_view$space[c("ny", "nx")]
+
+    return(cube)
+}
+
 #' @title Create an object image_mask with information about mask band
-#' @name .sits_gc_cloud_mask
+#' @name .gc_cloud_mask
 #' @keywords internal
 #'
 #' @param tile Data cube tile from where data is to be retrieved.
 #'
 #' @return A \code{object} 'image_mask' from gdalcubes containing information
 #'  about the mask band.
-.sits_gc_cloud_mask <- function(tile) {
+.gc_cloud_mask <- function(tile) {
 
     bands <- .cube_bands(tile)
     cloud_band <- .source_cloud()
@@ -181,14 +193,9 @@
                     "mask, please include the cloud band in your cube")
     )
 
-    cloud_source <- .source_bands_to_source(
-        source = .cube_source(cube = tile),
-        collection = .cube_collection(cube = tile),
-        bands = cloud_band)
-
     # create a image mask object
     mask_values <- gdalcubes::image_mask(
-        cloud_source,
+        cloud_band,
         values = .source_cloud_interp_values(
             source = .cube_source(cube = tile),
             collection = .cube_collection(cube = tile)
@@ -197,8 +204,9 @@
 
     return(mask_values)
 }
+
 #' @title Create an image_collection object
-#' @name .sits_gc_database
+#' @name .gc_create_database
 #' @keywords internal
 #'
 #' @param cube      Data cube from where data is to be retrieved.
@@ -207,54 +215,90 @@
 #'
 #' @return a \code{object} 'image_collection' containing information about the
 #'  images metadata.
-.sits_gc_database <- function(cube, path_db) {
+.gc_create_database <- function(cube, path_db) {
 
     # set caller to show in errors
-    .check_set_caller(".sits_gc_database")
-
-    # error if a cube other than S2_L2A_AWS is provided
-    .check_that(
-        x = .sits_cube_source(cube) == "AWS",
-        msg = "for now, only 'AWS' cubes can be aggregated."
-    )
+    .check_set_caller(".gc_create_database")
 
     # joining the bands of all tiles
-    full_images <- dplyr::bind_rows(cube$file_info)
+    file_info <- dplyr::bind_rows(cube$file_info)
 
-    # retrieving the s2_la_aws format
-    format_col <- system.file("extdata/gdalcubes/s2la_aws.json",
-                              package = "sits")
+    # retrieving the collection format
+    format_col <- .gc_format_col(cube$source)
 
     message("Creating database of images...")
-    # create image collection cube
-    ic_cube <- gdalcubes::create_image_collection(files    = full_images$path,
-                                                  format   = format_col,
-                                                  out_file = path_db)
+    ic_cube <- gdalcubes::create_image_collection(
+        files    = file_info$path,
+        format   = format_col,
+        out_file = path_db)
     return(ic_cube)
 }
+
+
+#' @title Internal function to handle with different file collection formats
+#'  for each provider.
+#' @name gdalcubes_format_col
+#' @keywords internal
+#'
+#' @description
+#' Generic function with the goal that each source implements its own way of
+#' localizing the collection format file.
+#'
+#' @param source A \code{character} value referring to a valid data source.
+#' @param ...    Additional parameters.
+#'
+#' @return A \code{character} path with format collection.
+.gc_format_col <- function(source, ...) {
+
+    # set caller to show in errors
+    .check_set_caller("sits_cube")
+
+    s <- .source_new(source = source)
+
+    # Dispatch
+    UseMethod(".gc_format_col", s)
+}
+
+#' @keywords internal
+#' @export
+.gc_format_col.aws_cube <- function(source, ...) {
+
+    system.file("extdata/gdalcubes/s2la_aws.json", package = "sits")
+}
+
+#' @keywords internal
+#' @export
+.gc_format_col.opendata_cube <- function(source, ...) {
+
+    system.file("extdata/gdalcubes/s2la_aws_cogs.json", package = "sits")
+}
+
 #' @title Create a cube_view object
-#' @name .sits_gc_cube
+#' @name .gc_create_cube_view
 #' @keywords internal
 #'
 #' @param tile       A data cube tile
 #' @param period     A \code{character} with the The period of time in which it
 #'  is desired to apply in the cube, must be provided based on ISO8601, where 1
 #'  number and a unit are provided, for example "P16D".
+#' @param res        A \code{numeric} with spatial resolution of the image that
+#'  will be aggregated.
 #' @param roi        A region of interest.
 #' @param toi        A timeline of intersection
 #' @param agg_method A \code{character} with the method that will be applied in
 #'  the aggregation, the following are available: "min", "max", "mean",
 #'  "median" or "first".
-#' @param resampling A \code{character} with the method that will be applied
-#'  in the resampling in mosaic operation. The following are available: "near",
-#'  "bilinear", "bicubic" or others supported by gdalwarp
-#'  (see https://gdal.org/programs/gdalwarp.html).
 #'
 #' @return a \code{cube_view} object from gdalcubes.
-.sits_gc_cube <- function(tile, period, roi, toi, agg_method, resampling) {
+.gc_create_cube_view <- function(tile,
+                                 period,
+                                 res,
+                                 roi,
+                                 toi,
+                                 agg_method) {
 
     # set caller to show in errors
-    .check_set_caller(".sits_gc_cube")
+    .check_set_caller(".gc_create_cube_view")
 
     .check_that(
         x = nrow(tile) == 1,
@@ -271,6 +315,18 @@
         msg = "the parameter 'method' must be provided."
     )
 
+    .check_num(
+        x = res,
+        msg = "the parameter 'res' is invalid.",
+        allow_null = TRUE,
+        len_max = 1
+    )
+
+    if (is.null(res))
+        res <- tile[["xres"]][[1]]
+
+    bbox_roi <- sits_bbox(tile)
+
     if (!purrr::is_null(roi)) {
 
         bbox_roi <- .sits_roi_bbox(roi, tile)
@@ -279,13 +335,12 @@
                     right  = bbox_roi[["xmax"]],
                     bottom = bbox_roi[["ymin"]],
                     top    = bbox_roi[["ymax"]])
-    } else {
-        bbox_roi <- sits_bbox(tile)
+    } else
         roi <- list(left   = bbox_roi[["xmin"]],
                     right  = bbox_roi[["xmax"]],
                     bottom = bbox_roi[["ymin"]],
                     top    = bbox_roi[["ymax"]])
-    }
+
 
     # create a list of cube view
     cv <- gdalcubes::cube_view(
@@ -296,11 +351,10 @@
                       t0 = format(toi[["max_min_date"]], "%Y-%m-%d"),
                       t1 = format(toi[["min_max_date"]], "%Y-%m-%d")),
         srs = tile$crs[[1]],
-        dt  = period,
-        dx = tile$xres[[1]],
-        dy = tile$yres[[1]],
-        aggregation = agg_method,
-        resampling  = resampling
+        dt = period,
+        dx = res,
+        dy = res,
+        aggregation = agg_method
     )
 
     return(cv)
diff --git a/R/sits_get_data.R b/R/sits_get_data.R
index 61ceb08e8..c8a8704bb 100644
--- a/R/sits_get_data.R
+++ b/R/sits_get_data.R
@@ -128,7 +128,8 @@ sits_get_data.wtss_cube <- function(cube, file = NULL, ...,
                                     start_date = NULL,
                                     end_date = NULL,
                                     bands = NULL,
-                                    label = "NoClass") {
+                                    label = "NoClass",
+                                    impute_fn = sits_impute_linear()) {
 
     # Precondition - lat/long must be provided
     .check_that(!purrr::is_null(latitude) &
@@ -146,10 +147,11 @@ sits_get_data.wtss_cube <- function(cube, file = NULL, ...,
         cube = cube,
         longitude = longitude,
         latitude = latitude,
-        start_date = start_end["start_date"],
-        end_date = start_end["end_date"],
+        start_date = start_end[["start_date"]],
+        end_date = start_end[["end_date"]],
         bands = bands,
-        label = label
+        label = label,
+        impute_fn = impute_fn
     )
     if (!purrr::is_null(data) && !("sits" %in% class(data))) {
         class(data) <- c("sits", class(data))
@@ -201,7 +203,10 @@ sits_get_data.satveg_cube <- function(cube,
 #'
 #' @export
 #'
-sits_get_data.csv_wtss_cube <- function(cube, file, ..., bands = NULL) {
+sits_get_data.csv_wtss_cube <- function(cube,
+                                        file, ...,
+                                        bands = NULL,
+                                        impute_fn = sits_impute_linear()) {
 
     # read sample information from CSV file and put it in a tibble
     csv <- tibble::as_tibble(utils::read.csv(file))
@@ -229,7 +234,8 @@ sits_get_data.csv_wtss_cube <- function(cube, file, ..., bands = NULL) {
                 start_date = lubridate::as_date(start_date),
                 end_date = lubridate::as_date(end_date),
                 bands = bands,
-                label = label
+                label = label,
+                impute_fn = impute_fn
             )
             return(row)
         }
@@ -310,6 +316,7 @@ sits_get_data.shp_wtss_cube <- function(cube, file, ...,
                                         end_date = NULL,
                                         bands = NULL,
                                         label = "NoClass",
+                                        impute_fn = sits_impute_linear(),
                                         shp_attr = NULL,
                                         .n_shp_pol = 30) {
 
@@ -346,7 +353,8 @@ sits_get_data.shp_wtss_cube <- function(cube, file, ...,
                 start_date = start_end["start_date"],
                 end_date = start_end["end_date"],
                 bands = bands,
-                label = lab
+                label = lab,
+                impute_fn = impute_fn
             )
             return(row)
         }
@@ -445,8 +453,8 @@ sits_get_data.raster_cube <- function(cube, file = NULL, ...,
         id = 1,
         longitude = longitude,
         latitude = latitude,
-        start_date = start_end["start_date"],
-        end_date = start_end["end_date"],
+        start_date = start_end[["start_date"]],
+        end_date = start_end[["end_date"]],
         label = label
     )
 
@@ -742,7 +750,8 @@ sits_get_data.shp_raster_cube <- function(cube, file, ...,
                                      start_date = NULL,
                                      end_date = NULL,
                                      bands = NULL,
-                                     label = "NoClass") {
+                                     label = "NoClass",
+                                     impute_fn = sits_impute_linear()) {
 
     # verifies if wtss package is installed
     if (!requireNamespace("Rwtss", quietly = TRUE)) {
@@ -786,6 +795,106 @@ sits_get_data.shp_raster_cube <- function(cube, file, ...,
         paste(e)
     })
 
+    # interpolate clouds
+    cld_band <- .source_bands_band_name(source = "WTSS",
+                                        collection = cube$collection,
+                                        bands = .source_cloud())
+
+    # retrieve values for the cloud band (if available)
+    if (cld_band %in% bands) {
+
+        bands <- bands[bands != cld_band]
+
+        # retrieve values that indicate clouds
+        cld_index <- .source_cloud_interp_values(
+            source = .cube_source(cube = cube),
+            collection = .cube_collection(cube = cube)
+        )
+
+        # get the values of the time series (terra object)
+        cld_values <- as.integer(ts$time_series[[1]][[cld_band]])
+
+        # get information about cloud bitmask
+        if (.source_cloud_bit_mask(
+            source = .cube_source(cube = cube),
+            collection = .cube_collection(cube = cube))) {
+
+            cld_values <- as.matrix(cld_values)
+            cld_rows <- nrow(cld_values)
+            cld_values <- matrix(bitwAnd(cld_values, sum(2 ^ cld_index)),
+                                 nrow = cld_rows)
+        }
+    }
+
+    # Retrieve values on a band by band basis
+    ts_bands <- lapply(bands, function(band) {
+
+        # get the values of the time series as matrix
+        values_band <- ts$time_series[[1]][[band]]
+
+        # convert to sits band
+        band_sits <- .source_bands_to_sits(source = cube$source[[1]],
+                                           collection = cube$collection[[1]],
+                                           bands = band)
+
+        if (!purrr::is_null(impute_fn)) {
+
+            # get the scale factors, max, min and missing values
+            missing_value <- .cube_band_missing_value(
+                cube = cube,
+                band = band_sits
+            )
+            minimum_value <- .cube_band_minimum_value(
+                cube = cube,
+                band = band_sits
+            )
+            maximum_value <- .cube_band_maximum_value(
+                cube = cube,
+                band = band_sits
+            )
+            scale_factor <- .cube_band_scale_factor(
+                cube = cube,
+                band = band_sits
+            )
+
+            # include information from cloud band
+            if (!purrr::is_null(cld_band)) {
+                if (.source_cloud_bit_mask(
+                    source = .cube_source(cube = cube),
+                    collection = .cube_collection(cube = cube)))
+                    values_band[cld_values > 0] <- NA
+                else
+                    values_band[cld_values %in% cld_index] <- NA
+            }
+
+            # adjust maximum and minimum values
+            values_band[values_band < minimum_value * scale_factor] <- NA
+            values_band[values_band > maximum_value * scale_factor] <- NA
+
+            # are there NA values? interpolate them
+            if (any(is.na(values_band))) {
+                values_band <- impute_fn(as.integer(values_band / scale_factor))
+            }
+        }
+
+        # return the values
+        return(values_band * scale_factor)
+    })
+
+    # rename bands to sits band names
+    bands_sits <- .source_bands_to_sits(source = cube$source[[1]],
+                                        collection = cube$collection[[1]],
+                                        bands = bands)
+
+    # now we have to transpose the data
+    ts_samples <- ts_bands %>%
+        purrr::set_names(bands_sits) %>%
+        tibble::as_tibble()
+
+    ts_samples <- dplyr::bind_cols(ts$time_series[[1]]["Index"], ts_samples)
+
+    ts$time_series[[1]] <- ts_samples
+
     # change the class of the data
     # before - class "wtss"
     # now - class "sits"
@@ -800,6 +909,7 @@ sits_get_data.shp_raster_cube <- function(cube, file, ...,
         ts <- .sits_tibble_rename(ts)
         # band names are uppercase in SITS
     }
+
     # return the tibble with the time series
     return(ts)
 }
diff --git a/R/sits_raster_data.R b/R/sits_raster_data.R
index 8b500f4e4..1bc112fac 100644
--- a/R/sits_raster_data.R
+++ b/R/sits_raster_data.R
@@ -328,8 +328,11 @@
         )
 
         # get the values of the time series (terra object)
-        cld_values <- .sits_cube_extract(cube = cube, band_cube = cld_band,
-                                         xy = xy)
+        cld_values <- .sits_cube_extract(
+            cube = cube,
+            band_cube = cld_band,
+            xy = xy
+        )
 
         # get information about cloud bitmask
         if (.source_cloud_bit_mask(
diff --git a/R/sits_regularize.R b/R/sits_regularize.R
index eb489d8a1..68fcb7264 100644
--- a/R/sits_regularize.R
+++ b/R/sits_regularize.R
@@ -44,28 +44,32 @@
 #' }
 #' }
 #'
-#' @param cube              A cube whose spacing of observation times is
-#'                          not constant and will be regularized by the
-#'                          "gdalcubes" packges
-#' @param name              Name of the output data cube
-#' @param dir_images        Directory where the regularized images will be
-#'                          written by \code{gdalcubes}.
-#' @param period            ISO8601 time period for regular data cubes
-#'                          produced by \code{gdalcubes},
-#'                          with number and unit, e.g., "P16D" for 16 days.
-#'                          Use "D", "M" and "Y" for days, month and year..
-#'
-#' @param roi               A region of interest (see above)
-#' @param agg_method        Method that will be applied by \code{gdalcubes}
-#'                          for aggregation. Options: "min", "max", "mean",
-#'                          "median" and "first".
-#' @param resampling        Method to be used by \code{gdalcubes}
-#'                          for resampling in mosaic operation.
-#'                          Options: "near", "bilinear", "bicubic"
-#'                          or others supported by gdalwarp
-#'                          (see https://gdal.org/programs/gdalwarp.html).
-#' @param cloud_mask        Use cloud band for aggregation by \code{gdalcubes}?
-#'
+#' @param cube       A \code{sits_cube} object whose spacing of observation
+#'  times is not constant and will be regularized by the \code{gdalcubes}
+#'  package.
+#' @param name       A \code{character} with name of the output data cube
+#' @param output_dir A \code{character} with a directory where the regularized
+#'  images will be written by \code{gdalcubes}.
+#' @param period     A \code{character} with ISO8601 time period for regular
+#'  data cubes produced by \code{gdalcubes}, with number and unit, e.g., "P16D"
+#'  for 16 days. Use "D", "M" and "Y" for days, month and year.
+#' @param res        A \code{numeric} with spatial resolution of the image that
+#'  will be aggregated.
+#' @param roi        A named \code{numeric} vector with a region of interest.
+#'  See above
+#' @param multicores A \code{numeric} with the number of cores will be used in
+#'  the regularize. By default is used 1 core.
+#' @param agg_method A \code{character} with method that will be applied by
+#'  \code{gdalcubes} for aggregation.
+#'  Options: \code{min}, \code{max}, \code{mean}, \code{median} and
+#'  \code{first}.
+#' @param resampling A \code{character} with method to be used by
+#'  \code{gdalcubes} for resampling in mosaic operation.
+#'  Options: \code{near}, \code{bilinear}, \code{bicubic} or others supported by
+#'  gdalwarp (see https://gdal.org/programs/gdalwarp.html).
+#'  By default is bilinear.
+#' @param cloud_mask A \code{logical} to use cloud band for aggregation by
+#' \code{gdalcubes}. Default is \code{TRUE}.
 #'
 #' @note
 #'    The "roi" parameter defines a region of interest. It can be
@@ -73,16 +77,18 @@
 #'    named XY values ("xmin", "xmax", "ymin", "ymax") or
 #'    named lat/long values ("lat_min", "lat_max", "long_min", "long_max")
 #'
-#' @export
+#' @return A \code{sits_cube} object with aggregated images.
 #'
+#' @export
 sits_regularize <- function(cube,
                             name,
-                            dir_images,
+                            output_dir,
                             period  = NULL,
+                            res     = NULL,
                             roi     = NULL,
                             agg_method = NULL,
-                            resampling = "bilinear",
-                            cloud_mask = TRUE) {
+                            cloud_mask = TRUE,
+                            multicores = 1) {
 
     # set caller to show in errors
     .check_set_caller("sits_regularize")
@@ -94,6 +100,24 @@ sits_regularize <- function(cube,
         )
     }
 
+    # supported  cubes
+    .check_chr_within(
+        x = .sits_cube_source(cube),
+        within = c("AWS", "OPENDATA"),
+        msg = paste("for the time being only the 'AWS' and 'OPENDATA' cubes",
+                    "can be regularized.")
+    )
+
+    .check_num(
+        x = multicores,
+        allow_zero = FALSE,
+        min = 1,
+        msg = "invalid 'multicores' parameter."
+    )
+
+    # setting in global env multicores options
+    gdalcubes::gdalcubes_options(threads = multicores)
+
     # test if provided object its a sits cube
     .check_that(
         x = inherits(cube, "raster_cube"),
@@ -103,7 +127,15 @@ sits_regularize <- function(cube,
     )
 
     # fix slashes for windows
-    dir_images <- normalizePath(dir_images)
+    output_dir <- normalizePath(output_dir)
+
+    # verifies the path to save the images
+    .check_that(
+        x = dir.exists(output_dir),
+        msg = "Invalid 'output_dir' parameter.."
+    )
+
+    path_db <- paste0(output_dir, "/gdalcubes.db")
 
     # filter only intersecting tiles
     intersects <- slider::slide_lgl(cube,
@@ -145,31 +177,33 @@ sits_regularize <- function(cube,
     cube <- .sits_cube_fix_name(cube)
 
     # create an image collection
-    db_file <- paste0(dir_images, "/gdalcubes.db")
-    img_col <- .sits_gc_database(cube = cube, path_db = db_file)
+    img_col <- .gc_create_database(cube = cube, path_db = path_db)
 
     gc_cube <- slider::slide_dfr(cube, function(tile){
 
         # create a list of cube view object
-        cv <- .sits_gc_cube(tile = tile,
-                            period = period,
-                            roi = roi,
-                            toi = toi,
-                            agg_method = agg_method,
-                            resampling = resampling)
+        cv <- .gc_create_cube_view(tile = tile,
+                                   period = period,
+                                   roi = roi,
+                                   res = res,
+                                   toi = toi,
+                                   agg_method = agg_method)
 
         # create of the aggregate cubes
-        gc_tile <- .sits_gc_compose(tile = tile,
-                                    name = name,
-                                    cv = cv,
-                                    img_col = img_col,
-                                    db_file = db_file,
-                                    dir_images = dir_images,
-                                    cloud_mask = cloud_mask)
+        gc_tile <- .gc_new_cube(tile = tile,
+                                name = name,
+                                cv = cv,
+                                img_col = img_col,
+                                path_db = path_db,
+                                output_dir = output_dir,
+                                cloud_mask = cloud_mask)
         return(gc_tile)
 
     })
 
+    # reset global option
+    gdalcubes::gdalcubes_options(threads = 1)
+
     class(gc_cube) <- c("raster_cube", class(gc_cube))
 
     return(gc_cube)
diff --git a/R/sits_select.R b/R/sits_select.R
index f23940c2f..f2f85c796 100644
--- a/R/sits_select.R
+++ b/R/sits_select.R
@@ -42,24 +42,35 @@ sits_select.sits <- function(data, bands) {
     # bands names in SITS are uppercase
     bands <- toupper(bands)
     sits_bands(data) <- toupper(sits_bands(data))
+    data_bands <- sits_bands(data)
 
     .check_chr_within(
         x = bands,
         within = sits_bands(data),
-        msg = paste("missing bands:",
-                    paste(bands[!bands %in% sits_bands(data)],
-                          collapse = ", "))
+        msg = paste("Invalid bands values")
     )
 
-    # prepare result sits tibble
-    result <- data
+    # make sure that nesting operation (bellow) will be done correctly
+    data[["..row_id"]] <- seq_len(nrow(data))
 
-    # select the chosen bands for the time series
-    result$time_series <- data$time_series %>%
-        purrr::map(function(ts) ts[, c("Index", bands)])
+    # unnest bands
+    data <- tidyr::unnest(data, cols = "time_series")
 
-    # return the result
-    return(result)
+    # select anything other than non selected bands
+    removed_bands <- paste0(setdiff(data_bands, bands))
+
+    data <- data[, setdiff(colnames(data), removed_bands)]
+
+    # nest again
+    data <- tidyr::nest(data, time_series = dplyr::all_of(c("Index", bands)))
+
+    # remove ..row_id
+    data <- dplyr::select(data, -"..row_id")
+
+    # set sits tibble class
+    class(data) <- c("sits", class(data))
+
+    return(data)
 }
 
 #' @export
diff --git a/R/sits_som.R b/R/sits_som.R
index 023f25640..886dd1609 100644
--- a/R/sits_som.R
+++ b/R/sits_som.R
@@ -279,7 +279,6 @@ sits_som_map <- function(data,
 #' new_samples <- sits_som_clean_samples(som_map)
 #' }
 #' @export
-
 sits_som_clean_samples <- function(som_map,
                                    prior_threshold = 0.6,
                                    posterior_threshold = 0.6,
@@ -299,43 +298,23 @@ sits_som_clean_samples <- function(som_map,
         msg = "invalid keep parameter"
     )
 
-    # original_samples
-    data <- som_map$data
-
-    # obtain the evaluation for each sample
-    eval_lst <- slider::slide(data, function(row) {
-        neuron_id <- row$id_neuron
-        sample_probs <-
-            som_map$labelled_neurons %>%
-            dplyr::filter(id_neuron == neuron_id) %>%
-            dplyr::filter(label_samples == row$label)
-        if (sample_probs$prior_prob >= prior_threshold &
-            sample_probs$post_prob >= posterior_threshold) {
-            return("clean")
-        } else {
-            if (sample_probs$prior_prob >= prior_threshold &
-                sample_probs$post_prob < posterior_threshold) {
-                  return("analyze")
-              } else {
-                  return("remove")
-              }
-        }
-    })
-    data$eval <- unlist(eval_lst)
-
-    # obtain the posterior probability for each sample
-    post_probs <- slider::slide(data, function(row) {
-        neuron_id <- row$id_neuron
-        sample_prob <-
-            som_map$labelled_neurons %>%
-            dplyr::filter(id_neuron == neuron_id) %>%
-            dplyr::filter(label_samples == row$label)
-
-        return(sample_prob$post_prob)
-    })
-    data$post_prob <- unlist(post_probs)
-
-    data <- dplyr::filter(data, eval %in% keep)
+    # function to detect of class noise
+    .detect_class_noise <- function(prior_prob, post_prob) {
+        ifelse(
+            prior_prob >= prior_threshold &
+                post_prob >= posterior_threshold, "clean",
+            ifelse(
+                prior_prob >= prior_threshold &
+                    post_prob < posterior_threshold, "analyze", "remove"))
+    }
+
+    data <- som_map$data %>%
+        dplyr::inner_join(som_map$labelled_neurons,
+                          by = c("id_neuron", "label" = "label_samples")) %>%
+        dplyr::mutate(eval = .detect_class_noise(prior_prob, post_prob)) %>%
+        dplyr::select(-count, -prior_prob) %>%
+        dplyr::filter(eval %in% keep)
+
     return(data)
 }
 
diff --git a/R/sits_source_api.R b/R/sits_source_api.R
index 564c1ff42..e478bba98 100644
--- a/R/sits_source_api.R
+++ b/R/sits_source_api.R
@@ -356,6 +356,45 @@ NULL
     return(res)
 }
 
+#' @rdname source_bands
+#'
+#' @description \code{.source_bands_resampling()} returns the
+#' \code{resampling} attribute of all bands filtered by its parameters.
+#'
+#' @return \code{.source_bands_resampling()} returns a \code{character}
+#' vectors with the desired resampling method that should be used in the  band.
+.source_bands_resampling <- function(source,
+                                     collection, ...,
+                                     bands = NULL,
+                                     fn_filter = NULL,
+                                     add_cloud = TRUE) {
+
+    # source is upper case
+    source <- toupper(source)
+
+    # collection is upper case
+    collection <- toupper(collection)
+
+    # pre-condition
+    .source_collection_check(source = source, collection = collection)
+
+    res <- .source_bands_reap(source = source,
+                              collection = collection,
+                              key = "resampling",
+                              bands = bands,
+                              fn_filter = fn_filter,
+                              add_cloud = add_cloud)
+
+    # simplify to character
+    res <- unlist(res, recursive = FALSE, use.names = FALSE)
+
+    # post-condition
+    .check_chr(res, allow_empty = FALSE,
+               msg = "invalid 'resampling' in config file")
+
+    return(res)
+}
+
 #' @rdname source_bands
 #'
 #' @description \code{.source_bands_to_sits()} converts any bands to its
diff --git a/R/sits_source_api_bdc.R b/R/sits_source_api_bdc.R
index 6d4299d4f..151c4ddf3 100644
--- a/R/sits_source_api_bdc.R
+++ b/R/sits_source_api_bdc.R
@@ -34,7 +34,15 @@
 .source_item_get_resolutions.bdc_cube <- function(source,
                                                   item, ...,
                                                   collection = NULL) {
-    item[[c("properties", "eo:gsd")]]
+
+    res <- .source_bands_resolutions(
+        source = source,
+        collection = collection,
+        bands = .source_item_get_bands(source = source,
+                                       item = item)
+    )
+
+    return(res[[1]])
 }
 
 #' @keywords internal
diff --git a/R/sits_source_api_opendata.R b/R/sits_source_api_opendata.R
new file mode 100644
index 000000000..3997e7fb0
--- /dev/null
+++ b/R/sits_source_api_opendata.R
@@ -0,0 +1,163 @@
+#' @keywords internal
+#' @export
+.source_item_get_date.opendata_cube <- function(source,
+                                             item, ...,
+                                             collection = NULL) {
+    item[[c("properties", "datetime")]]
+}
+
+#' @keywords internal
+#' @export
+.source_item_get_hrefs.opendata_cube <- function(source,
+                                              item, ...,
+                                              collection = NULL) {
+
+    href <- unname(purrr::map_chr(item[["assets"]], `[[`, "href"))
+
+    # add gdal vsi in href urls
+    return(.stac_add_gdal_vsi(href))
+}
+
+#' @keywords internal
+#' @export
+.source_item_get_bands.opendata_cube <- function(source,
+                                              item, ...,
+                                              collection = NULL) {
+    names(item[["assets"]])
+}
+
+#' @keywords internal
+#' @export
+.source_item_get_resolutions.opendata_cube <- function(source,
+                                                    item, ...,
+                                                    collection = NULL) {
+
+    res <- .source_bands_resolutions(
+        source = source,
+        collection = collection,
+        bands = .source_item_get_bands(source = source,
+                                       item = item)
+    )
+
+    unlist(res)
+}
+
+#' @keywords internal
+#' @export
+.source_items_new.opendata_cube <- function(source,
+                                         collection, ...,
+                                         stac_query,
+                                         tiles = NULL) {
+
+    # set caller to show in errors
+    .check_set_caller(".source_items_new.opendata_cube")
+
+    # if specified, a filter per tile is added to the query
+    if (!is.null(tiles)) {
+        sep_tile <- .aws_tiles(tiles)
+
+        stac_query <-
+            rstac::ext_query(q = stac_query,
+                             "sentinel:utm_zone" %in% sep_tile$utm_zone,
+                             "sentinel:latitude_band" %in% sep_tile$lat_band,
+                             "sentinel:grid_square" %in% sep_tile$grid_square)
+    }
+
+    # making the request
+    items_info <- rstac::post_request(q = stac_query, ...)
+
+    # check if matched items
+    .check_that(
+        x = rstac::items_matched(items_info) > 0,
+        msg = "no items matched the query criteria."
+    )
+
+    # if more than 2 times items pagination are found the progress bar
+    # is displayed
+    pgr_fetch <- rstac::items_matched(items_info) > 2 * .config_rstac_limit()
+
+    # fetching all the metadata
+    items_info <- rstac::items_fetch(items = items_info, progress = pgr_fetch)
+
+    return(items_info)
+}
+
+#' @keywords internal
+#' @export
+.source_items_tiles_group.opendata_cube <- function(source,
+                                                 items, ...,
+                                                 collection = NULL) {
+
+    # store tile info in items object
+    items$features <- purrr::map(items$features, function(feature) {
+        feature$properties$tile <- paste0(
+            feature$properties[["sentinel:utm_zone"]],
+            feature$properties[["sentinel:latitude_band"]],
+            feature$properties[["sentinel:grid_square"]])
+
+        feature
+    })
+
+    rstac::items_group(items, field = c("properties", "tile"))
+}
+
+#' @keywords internal
+#' @export
+.source_items_tile_get_crs.opendata_cube <- function(source,
+                                                  tile_items, ...,
+                                                  collection = NULL) {
+
+    # format collection crs
+    crs <- .sits_proj_format_crs(
+        tile_items[["features"]][[1]][[c("properties", "proj:epsg")]]
+    )
+
+    return(crs)
+}
+
+#' @keywords internal
+#' @export
+.source_items_tile_get_name.opendata_cube <- function(source,
+                                                   tile_items, ...,
+                                                   collection = NULL) {
+
+    tile_items[["features"]][[1]][[c("properties", "tile")]]
+}
+
+#' @keywords internal
+#' @export
+.source_items_tile_get_bbox.opendata_cube <- function(source,
+                                                   tile_items, ...,
+                                                   collection = NULL) {
+
+
+    href <- .source_item_get_hrefs(source = source,
+                                   item = tile_items[["features"]][[1]], ...,
+                                   collection = collection)
+
+    # read the first image and obtain the size parameters
+    params <- .raster_params_file(href)
+
+    bbox <- c(xmin = params[["xmin"]], ymin = params[["ymin"]],
+              xmax = params[["xmax"]], ymax = params[["ymax"]])
+
+    return(bbox)
+}
+
+#' @keywords internal
+#' @export
+.source_items_tile_get_size.opendata_cube <- function(source,
+                                                   tile_items, ...,
+                                                   collection = NULL) {
+
+    href <- .source_item_get_hrefs(source = source,
+                                   item = tile_items[["features"]][[1]], ...,
+                                   collection = collection)
+
+    # read the first image and obtain the size parameters
+    params <- .raster_params_file(href)
+
+    size <- c(nrows = params[["nrows"]], ncols = params[["ncols"]])
+
+    return(size)
+}
diff --git a/R/sits_stac.R b/R/sits_stac.R
index 3e61feebf..87244a607 100644
--- a/R/sits_stac.R
+++ b/R/sits_stac.R
@@ -58,9 +58,9 @@
 
     # verify the provided parameters
     if (!inherits(roi, "sf")) {
-        if (all(c("xmin", "ymin", "xmax", "ymax") %in% names(roi)))
+        if (all(c("lon_min", "lat_min", "lon_max", "lat_max") %in% names(roi)))
             roi_list[c("bbox", "intersects")] <-
-                list(roi[c("xmin", "ymin", "xmax", "ymax")], NULL)
+                list(roi[c("lon_min", "lat_min", "lon_max", "lat_max")], NULL)
 
         else if (typeof(roi) == "character")
             roi_list[c("bbox", "intersects")] <- list(NULL, roi)
diff --git a/R/sits_view.R b/R/sits_view.R
index b9d72dc62..de480afe2 100644
--- a/R/sits_view.R
+++ b/R/sits_view.R
@@ -18,19 +18,20 @@
 #'
 #' @examples
 #' \donttest{
-#' data_dir <- system.file("extdata/raster/cbers", package = "sits")
+#' data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 #'
-#' cbers_022024 <- sits_cube(
+#' modis_cube <- sits_cube(
 #'     source = "LOCAL",
-#'     name = "cbers_022024",
+#'     name = "modis_sinop",
 #'     origin = "BDC",
-#'     collection = "CB4_64-1",
-#'     resolution = 64,
+#'     collection = "MOD13Q1-6",
+#'     band = "NDVI",
 #'     data_dir = data_dir,
 #'     parse_info = c("X1", "X2", "tile", "band", "date")
 #' )
+#'
 #' # plot the data cube
-#' sits_view(cbers_022024, red = "B15", green = "B16", blue = "B13", time = 1)
+#' sits_view(modis_cube, red = "EVI", green = "NDVI", blue = "EVI", time = 1)
 #' }
 #'
 #' @export
diff --git a/README.Rmd b/README.Rmd
index e55c45d8e..3a98bce73 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -121,13 +121,13 @@ one month period, as shown below. This can be done in SITS by the
 `sits_regularize` which use the [https://github.com/appelmar/gdalcubes](gdalcubes) package. For details in gdalcubes, please see Reference [4]. 
 
 ```{r, eval = FALSE, echo = TRUE}
-gc_cube <- sits_regularize(cube   = s2_cube,
+gc_cube <- sits_regularize(cube          = s2_cube,
                            name          = "T20LKP_2018_2019_1M",
-                           dir_images    = tempdir(),
+                           output_dir    = tempdir(),
                            period        = "P1M",
                            agg_method    = "median",
-                           resampling    = "bilinear",
-                           cloud_mask    = TRUE)
+                           cloud_mask    = TRUE,
+                           multicores    = 2)
 ```
 
 ### Accessing time series in data cubes
diff --git a/README.md b/README.md
index 4c5eb1f46..6e503fa54 100644
--- a/README.md
+++ b/README.md
@@ -127,13 +127,13 @@ in SITS by the `sits_regularize` which use the
 in gdalcubes, please see Reference \[4\].
 
 ``` r
-gc_cube <- sits_regularize(cube   = s2_cube,
+gc_cube <- sits_regularize(cube          = s2_cube,
                            name          = "T20LKP_2018_2019_1M",
-                           dir_images    = tempdir(),
+                           output_dir    = tempdir(),
                            period        = "P1M",
                            agg_method    = "median",
-                           resampling    = "bilinear",
-                           cloud_mask    = TRUE)
+                           cloud_mask    = TRUE,
+                           multicores    = 2)
 ```
 
 ### Accessing time series in data cubes
@@ -149,7 +149,7 @@ library(sits)
 #> To provide additional configurations, create an YAML file and inform its path to environment variable 'SITS_CONFIG_USER_FILE'.
 #> Using raster package: terra
 #> SITS - satellite image time series analysis.
-#> Loaded sits v0.14.0.
+#> Loaded sits v0.14.0-2.
 #>         See ?sits for help, citation("sits") for use in publication.
 #>         See demo(package = "sits") for examples.
 # create a cube from a local file 
@@ -175,7 +175,7 @@ points <- sits_get_data(raster_cube, file = csv_raster_file)
 
 # show the points
 points[1:3,]
-#> # A tibble: 3 x 7
+#> # A tibble: 3 × 7
 #>   longitude latitude start_date end_date   label   cube       time_series      
 #>       <dbl>    <dbl> <date>     <date>     <chr>   <chr>      <list>           
 #> 1     -55.7    -11.8 2013-09-14 2014-08-29 Pasture sinop-2014 <tibble [23 × 3]>
diff --git a/demo/00Index b/demo/00Index
index 6313769f6..9d5a5f42a 100644
--- a/demo/00Index
+++ b/demo/00Index
@@ -1,6 +1,5 @@
 access_SATVEG            Access time series using the SATVEG service
 classify_cbers_bdc       Classify a set of CBERS AWFI images in the Brazil Data Cube
-classify_cbers_local     Classify a set of CBERS AWFI images stored in a local directory
 classify_ts              Classify a time series using SVM
 classify_raster_xgb      Classify MODIS image using extreme gradient boosting
 classify_raster_svm      Classify MODIS image using SVM
diff --git a/demo/classify_cbers_local.R b/demo/classify_cbers_local.R
deleted file mode 100644
index 95031d1ae..000000000
--- a/demo/classify_cbers_local.R
+++ /dev/null
@@ -1,99 +0,0 @@
-# This is a demonstration of classification of using
-# local data (images organised by tiles)
-# The input is a CBERS-4 data set covering an area in the Cerrado
-# of the state of Bahia (Brazil)
-# with two bands (NDVI and EVI)
-library(sits)
-if (!requireNamespace("sitsdata", quietly = TRUE)) {
-    if (!requireNamespace("devtools", quietly = TRUE)) {
-        install.packages("devtools")
-    }
-    devtools::install_github("e-sensing/sitsdata")
-}
-# load the sitsdata library
-library(sitsdata)
-
-# define the local directory to load the images
-local_dir <- system.file("extdata/CBERS", package = "sitsdata")
-
-# define the local CBERS data cube
-cbers_cube <- sits_cube(
-    source     = "LOCAL",
-    name       = "cbers_022024",
-    origin     = "BDC",
-    collection = "CB4_64-1",
-    data_dir   = local_dir,
-    parse_info = c("X1", "X2", "X3", "X4", "tile", "date", "X5", "band")
-)
-
-# load the samples
-data("samples_cerrado_cbers")
-
-# set up the bands
-bands <- c("NDVI", "EVI")
-
-# select the ndvi and evi bands
-cbers_samples_2bands <- sits_select(
-    data  = samples_cerrado_cbers,
-    bands = bands
-)
-
-# train a random forest model
-tcnn_model <- sits_train(
-    data      = cbers_samples_2bands,
-    ml_method = sits_rfor()
-)
-
-# classify the data (remember to set the appropriate memory size)
-cbers_probs <- sits_classify(
-    data       = cbers_cube,
-    ml_model   = tcnn_model,
-    memsize    = 1,
-    multicores = 3,
-    output_dir = tempdir(),
-    verbose    = TRUE
-)
-
-# label the resulting probs maps
-cbers_label <- sits_label_classification(
-    cube       = cbers_probs,
-    memsize    = 12,
-    multicores = 2,
-    output_dir = tempdir()
-)
-
-# smoothen the probabibilities with bayesian estimator
-cbers_bayes <- sits_smooth(
-    cube       = cbers_probs,
-    type       = "bayes",
-    memsize    = 12,
-    multicores = 2,
-    output_dir = tempdir()
-)
-
-# label the resulting smoothed probs maps
-cbers_label_bayes <- sits_label_classification(
-    cube       = cbers_bayes,
-    memsize    = 12,
-    multicores = 2,
-    output_dir = tempdir()
-)
-
-
-
-# view the image (first and last instances)
-sits_view(cbers_cube, red = "EVI", green = "NDVI", blue = "EVI", time = 1)
-sits_view(cbers_cube, red = "EVI", green = "NDVI", blue = "EVI", time = 23)
-
-# plot the probabilities for each class
-plot(cbers_probs)
-
-# labels are "Cerradao" "Cerrado"  "Cropland" "Pasture"
-
-mylegend <- list("Cerradao" = "darkgreen", "Cerrado" = "palegreen1",
-"Cropland" = "orange2", "Pasture" = "yellow")
-
-plot(cbers_label, legend = mylegend)
-
-plot(cbers_label, palette = "default")
-
diff --git a/inst/extdata/config.yml b/inst/extdata/config.yml
index 8aeb3d877..0555ed38b 100644
--- a/inst/extdata/config.yml
+++ b/inst/extdata/config.yml
@@ -198,25 +198,25 @@ sources:
                         offset_value  : 0
                         resampling    : "bilinear"
                         resolutions   : [30]
-                        band_name     : "BAND1"
+                        band_name     : "band1"
                     B2      :
                         <<: *bdc_oli_band1
-                        band_name     : "BAND2"
+                        band_name     : "band2"
                     B3      :
                         <<: *bdc_oli_band1
-                        band_name     : "BAND3"
+                        band_name     : "band3"
                     B4      :
                         <<: *bdc_oli_band1
-                        band_name     : "BAND4"
+                        band_name     : "band4"
                     B5      :
                         <<: *bdc_oli_band1
-                        band_name     : "BAND5"
+                        band_name     : "band5"
                     B6      :
                         <<: *bdc_oli_band1
-                        band_name     : "BAND6"
+                        band_name     : "band6"
                     B7      :
                         <<: *bdc_oli_band1
-                        band_name     : "BAND7"
+                        band_name     : "band7"
                     CLOUD   : &bdc_oli_cloud
                         bit_mask      : FALSE
                         band_name     : "Fmask4"
@@ -310,37 +310,37 @@ sources:
                         offset_value  : 0
                         resampling    : "bilinear"
                         resolutions   : [10]
-                        band_name     : "BAND1"
+                        band_name     : "band1"
                     B02     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND2"
+                        band_name     : "band2"
                     B03     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND3"
+                        band_name     : "band3"
                     B04     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND4"
+                        band_name     : "band4"
                     B05     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND5"
+                        band_name     : "band5"
                     B06     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND6"
+                        band_name     : "band6"
                     B07     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND7"
+                        band_name     : "band7"
                     B08     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND8"
+                        band_name     : "band8"
                     B8A     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND8a"
+                        band_name     : "band8a"
                     B11     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND11"
+                        band_name     : "band11"
                     B12     :
                         <<: *bdc_msi_band1
-                        band_name     : "BAND12"
+                        band_name     : "band12"
                     CLOUD   :
                         bit_mask      : FALSE
                         band_name     : "Fmask4"
@@ -431,7 +431,7 @@ sources:
                             11       : "snow or ice"
                         interp_values: [0, 1, 2, 3, 8, 9, 10, 11]
                         resampling   : "near"
-                        resolutions  : [20, 60]
+                        resolutions  : [10]
                 satellite   : "SENTINEL-2"
                 sensor      : "MSI"
                 collection_name: "S2-SEN2COR_10_16D_STK-1"
@@ -569,7 +569,7 @@ sources:
         service             : "STAC"
         url                 : "https://earth-search.aws.element84.com/v0/"
         collections         :
-            SENTINEL-S2-L2A :
+            SENTINEL-S2-L2A      :
                 bands       :
                     B01     : &aws_msi_60m
                         missing_value: -9999
@@ -651,6 +651,167 @@ sources:
                     AWS_DEFAULT_REGION   : "eu-central-1"
                     AWS_S3_ENDPOINT      : "s3.amazonaws.com"
                     AWS_REQUEST_PAYER    : "requester"
+            SENTINEL-S2-L2A-COGS :
+                bands       :
+                    B01     : &aws_cog_msi_60m
+                        missing_value: -9999
+                        minimum_value: 0
+                        maximum_value: 10000
+                        scale_factor : 0.0001
+                        offset_value : 0
+                        resampling   : "bilinear"
+                        resolutions  : [60]
+                        band_name    : "B01"
+                    B02     : &aws_cog_msi_10m
+                        missing_value: -9999
+                        minimum_value: 0
+                        maximum_value: 10000
+                        scale_factor : 0.0001
+                        offset_value : 0
+                        resampling   : "bilinear"
+                        resolutions  : [10]
+                        band_name    : "B02"
+                    B03     :
+                        <<: *aws_cog_msi_10m
+                        band_name    : "B03"
+                    B04     :
+                        <<: *aws_cog_msi_10m
+                        band_name    : "B04"
+                    B05     : &aws_cog_msi_20m
+                        missing_value: -9999
+                        minimum_value: 0
+                        maximum_value: 10000
+                        scale_factor : 0.0001
+                        offset_value : 0
+                        resampling   : "bilinear"
+                        resolutions  : [20]
+                        band_name    : "B05"
+                    B06     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B06"
+                    B07     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B07"
+                    B08     :
+                        <<: *aws_cog_msi_10m
+                        band_name    : "B08"
+                    B8A     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B8A"
+                    B09     :
+                        <<: *aws_cog_msi_60m
+                        band_name    : "B09"
+                    B11     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B11"
+                    B12     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B12"
+                    CLOUD   :
+                        bit_mask     : FALSE
+                        band_name    : "SCL"
+                        values       :
+                            0        : "missing_data"
+                            1        : "defective pixel"
+                            2        : "shadows"
+                            3        : "cloud shadows"
+                            4        : "vegetation"
+                            5        : "non-vegetated"
+                            6        : "water"
+                            7        : "unclassified"
+                            8        : "cloud medium"
+                            9        : "cloud high"
+                            10       : "thin cirrus"
+                            11       : "snow or ice"
+                        interp_values: [0, 1, 2, 3, 8, 9, 10, 11]
+                        resampling   : "near"
+                        resolutions  : [20, 60]
+                satellite   : "SENTINEL-2"
+                sensor      : "MSI"
+                collection_name: "sentinel-s2-l2a-cogs"
+    OPENDATA                :
+        s3_class            : ["opendata_cube", "stac_cube", "raster_cube"]
+        service             : "STAC"
+        url                 : "https://earth-search.aws.element84.com/v0/"
+        collections         :
+            SENTINEL-S2-L2A-COGS :
+                bands       :
+                    B01     : &aws_cog_msi_60m
+                        missing_value: -9999
+                        minimum_value: 0
+                        maximum_value: 10000
+                        scale_factor : 0.0001
+                        offset_value : 0
+                        resampling   : "bilinear"
+                        resolutions  : [60]
+                        band_name    : "B01"
+                    B02     : &aws_cog_msi_10m
+                        missing_value: -9999
+                        minimum_value: 0
+                        maximum_value: 10000
+                        scale_factor : 0.0001
+                        offset_value : 0
+                        resampling   : "bilinear"
+                        resolutions  : [10]
+                        band_name    : "B02"
+                    B03     :
+                        <<: *aws_cog_msi_10m
+                        band_name    : "B03"
+                    B04     :
+                        <<: *aws_cog_msi_10m
+                        band_name    : "B04"
+                    B05     : &aws_cog_msi_20m
+                        missing_value: -9999
+                        minimum_value: 0
+                        maximum_value: 10000
+                        scale_factor : 0.0001
+                        offset_value : 0
+                        resampling   : "bilinear"
+                        resolutions  : [20]
+                        band_name    : "B05"
+                    B06     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B06"
+                    B07     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B07"
+                    B08     :
+                        <<: *aws_cog_msi_10m
+                        band_name    : "B08"
+                    B8A     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B8A"
+                    B09     :
+                        <<: *aws_cog_msi_60m
+                        band_name    : "B09"
+                    B11     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B11"
+                    B12     :
+                        <<: *aws_cog_msi_20m
+                        band_name    : "B12"
+                    CLOUD   :
+                        bit_mask     : FALSE
+                        band_name    : "SCL"
+                        values       :
+                            0        : "missing_data"
+                            1        : "defective pixel"
+                            2        : "shadows"
+                            3        : "cloud shadows"
+                            4        : "vegetation"
+                            5        : "non-vegetated"
+                            6        : "water"
+                            7        : "unclassified"
+                            8        : "cloud medium"
+                            9        : "cloud high"
+                            10       : "thin cirrus"
+                            11       : "snow or ice"
+                        interp_values: [0, 1, 2, 3, 8, 9, 10, 11]
+                        resampling   : "near"
+                        resolutions  : [20]
+                satellite   : "SENTINEL-2"
+                sensor      : "MSI"
+                collection_name: "sentinel-s2-l2a-cogs"
     DEAFRICA                :
         s3_class            : ["deafrica_cube", "stac_cube", "raster_cube"]
         service             : "STAC"
diff --git a/inst/extdata/gdalcubes/s2la_aws.json b/inst/extdata/gdalcubes/s2la_aws.json
index 7cf2327a7..00562d11d 100644
--- a/inst/extdata/gdalcubes/s2la_aws.json
+++ b/inst/extdata/gdalcubes/s2la_aws.json
@@ -77,7 +77,7 @@
             "scale" : 0.0001,
             "pattern" : ".+B12\\.jp2"
         },
-        "SCL" : {
+        "CLOUD" : {
             "pattern" : ".+SCL\\.jp2"
         }
     }
diff --git a/inst/extdata/gdalcubes/s2la_aws_cogs.json b/inst/extdata/gdalcubes/s2la_aws_cogs.json
new file mode 100644
index 000000000..e72e8c21d
--- /dev/null
+++ b/inst/extdata/gdalcubes/s2la_aws_cogs.json
@@ -0,0 +1,84 @@
+{
+    "description" : "Image collection format for Sentinel 2 Level 2A Open Data in AWS",
+    "references" : "https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html",
+    "example" : "/vsicurl/sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/59/W/MP/2021/9/S2A_59WMP_20210926_0_L2A/B01.tif",
+    "tags" : ["Sentinel", "Copernicus", "ESA", "TOA"],
+    "pattern" : ".+.tif",
+    "images" : {
+        "pattern" : ".*sentinel-s2-l2a-cogs((?:/[A-Za-z0-9_]+){6})/.*"
+    },
+    "datetime" : {
+        "pattern" : ".*S2[A|B](?:_[A-Za-z0-9]+){1}_([A-Za-z0-9]+).*",
+        "format" : "%Y%m%d"
+    },
+    "bands" : {
+        "B01" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B01\\.tif"
+        },
+        "B02" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B02\\.tif"
+        },
+        "B03" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B03\\.tif"
+        },
+        "B04" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B04\\.tif"
+        },
+        "B05" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B05\\.tif"
+        },
+        "B06" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B06\\.tif"
+        },
+        "B07" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B07\\.tif"
+        },
+        "B08" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B08\\.tif"
+        },
+        "B8A" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B8A\\.tif"
+        },
+        "B09" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B09\\.tif"
+        },
+        "B10" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B10\\.tif"
+        },
+        "B11" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B11\\.tif"
+        },
+        "B12" : {
+            "nodata" : 0,
+            "scale" : 0.0001,
+            "pattern" : ".+B12\\.tif"
+        },
+        "CLOUD" : {
+            "pattern" : ".+SCL\\.tif"
+        }
+    }
+}
diff --git a/inst/extdata/raster/cbers/CBERS-4_AWFI_022024_B13_2018-02-02.tif b/inst/extdata/raster/cbers/CBERS-4_AWFI_022024_B13_2018-02-02.tif
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diff --git a/man/dot-sits_gc_cloud_mask.Rd b/man/dot-gc_cloud_mask.Rd
similarity index 84%
rename from man/dot-sits_gc_cloud_mask.Rd
rename to man/dot-gc_cloud_mask.Rd
index d4a344d9b..bcf8d11fc 100644
--- a/man/dot-sits_gc_cloud_mask.Rd
+++ b/man/dot-gc_cloud_mask.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/sits_gdalcubes.R
-\name{.sits_gc_cloud_mask}
-\alias{.sits_gc_cloud_mask}
+\name{.gc_cloud_mask}
+\alias{.gc_cloud_mask}
 \title{Create an object image_mask with information about mask band}
 \usage{
-.sits_gc_cloud_mask(tile)
+.gc_cloud_mask(tile)
 }
 \arguments{
 \item{tile}{Data cube tile from where data is to be retrieved.}
diff --git a/man/dot-sits_gc_cube.Rd b/man/dot-gc_create_cube_view.Rd
similarity index 67%
rename from man/dot-sits_gc_cube.Rd
rename to man/dot-gc_create_cube_view.Rd
index 66cbc7e5a..1718bb5df 100644
--- a/man/dot-sits_gc_cube.Rd
+++ b/man/dot-gc_create_cube_view.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/sits_gdalcubes.R
-\name{.sits_gc_cube}
-\alias{.sits_gc_cube}
+\name{.gc_create_cube_view}
+\alias{.gc_create_cube_view}
 \title{Create a cube_view object}
 \usage{
-.sits_gc_cube(tile, period, roi, toi, agg_method, resampling)
+.gc_create_cube_view(tile, period, res, roi, toi, agg_method)
 }
 \arguments{
 \item{tile}{A data cube tile}
@@ -13,6 +13,9 @@
 is desired to apply in the cube, must be provided based on ISO8601, where 1
 number and a unit are provided, for example "P16D".}
 
+\item{res}{A \code{numeric} with spatial resolution of the image that
+will be aggregated.}
+
 \item{roi}{A region of interest.}
 
 \item{toi}{A timeline of intersection}
@@ -20,11 +23,6 @@ number and a unit are provided, for example "P16D".}
 \item{agg_method}{A \code{character} with the method that will be applied in
 the aggregation, the following are available: "min", "max", "mean",
 "median" or "first".}
-
-\item{resampling}{A \code{character} with the method that will be applied
-in the resampling in mosaic operation. The following are available: "near",
-"bilinear", "bicubic" or others supported by gdalwarp
-(see https://gdal.org/programs/gdalwarp.html).}
 }
 \value{
 a \code{cube_view} object from gdalcubes.
diff --git a/man/dot-sits_gc_database.Rd b/man/dot-gc_create_database.Rd
similarity index 84%
rename from man/dot-sits_gc_database.Rd
rename to man/dot-gc_create_database.Rd
index e1c4c40b2..d690160ac 100644
--- a/man/dot-sits_gc_database.Rd
+++ b/man/dot-gc_create_database.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/sits_gdalcubes.R
-\name{.sits_gc_database}
-\alias{.sits_gc_database}
+\name{.gc_create_database}
+\alias{.gc_create_database}
 \title{Create an image_collection object}
 \usage{
-.sits_gc_database(cube, path_db)
+.gc_create_database(cube, path_db)
 }
 \arguments{
 \item{cube}{Data cube from where data is to be retrieved.}
diff --git a/man/dot-sits_gc_date.Rd b/man/dot-gc_get_date.Rd
similarity index 85%
rename from man/dot-sits_gc_date.Rd
rename to man/dot-gc_get_date.Rd
index 1617560cb..a59442b19 100644
--- a/man/dot-sits_gc_date.Rd
+++ b/man/dot-gc_get_date.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/sits_gdalcubes.R
-\name{.sits_gc_date}
-\alias{.sits_gc_date}
+\name{.gc_get_date}
+\alias{.gc_get_date}
 \title{Extracted date from aggregated cubes}
 \usage{
-.sits_gc_date(dir_images)
+.gc_get_date(dir_images)
 }
 \arguments{
 \item{dir_images}{A \code{character}  corresponds to the path on which the
diff --git a/man/dot-sits_gc_compose.Rd b/man/dot-gc_new_cube.Rd
similarity index 81%
rename from man/dot-sits_gc_compose.Rd
rename to man/dot-gc_new_cube.Rd
index e708e0e4c..20a209c5c 100644
--- a/man/dot-sits_gc_compose.Rd
+++ b/man/dot-gc_new_cube.Rd
@@ -1,16 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/sits_gdalcubes.R
-\name{.sits_gc_compose}
-\alias{.sits_gc_compose}
+\name{.gc_new_cube}
+\alias{.gc_new_cube}
 \title{Save the images based on an aggregation method.}
 \usage{
-.sits_gc_compose(
+.gc_new_cube(
   tile,
   name,
   cv,
   img_col,
-  db_file,
-  dir_images,
+  path_db,
+  output_dir,
   cloud_mask,
   ...,
   version = "v1"
@@ -26,9 +26,9 @@
 \item{img_col}{A \code{object} 'image_collection' containing information
 about the images metadata.}
 
-\item{db_file}{Database to be created by gdalcubes}
+\item{path_db}{Database to be created by gdalcubes}
 
-\item{dir_images}{Directory where the aggregated images will be written.}
+\item{output_dir}{Directory where the aggregated images will be written.}
 
 \item{cloud_mask}{A \code{logical} corresponds to the use of the cloud band
 for aggregation.}
diff --git a/man/dot-sits_gc_brick.Rd b/man/dot-gc_raster_cube.Rd
similarity index 87%
rename from man/dot-sits_gc_brick.Rd
rename to man/dot-gc_raster_cube.Rd
index 001ae4309..f590c4524 100644
--- a/man/dot-sits_gc_brick.Rd
+++ b/man/dot-gc_raster_cube.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/sits_gdalcubes.R
-\name{.sits_gc_brick}
-\alias{.sits_gc_brick}
+\name{.gc_raster_cube}
+\alias{.gc_raster_cube}
 \title{Create a raster_cube object}
 \usage{
-.sits_gc_brick(cube, img_col, cv, cloud_mask)
+.gc_raster_cube(cube, img_col, cv, cloud_mask)
 }
 \arguments{
 \item{cube}{Data cube from where data is to be retrieved.}
diff --git a/man/dot-gc_update_metadata.Rd b/man/dot-gc_update_metadata.Rd
new file mode 100644
index 000000000..0cbec5cdd
--- /dev/null
+++ b/man/dot-gc_update_metadata.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sits_gdalcubes.R
+\name{.gc_update_metadata}
+\alias{.gc_update_metadata}
+\title{Update metadata from sits cube using gdalcubes metadata}
+\usage{
+.gc_update_metadata(cube, cube_view)
+}
+\arguments{
+\item{cube}{Data cube from where data is to be retrieved.}
+
+\item{cv}{A \code{object} 'cube_view' with values from cube.
+for aggregation.}
+}
+\value{
+a \code{sits_cube} object with updated metadata.
+}
+\description{
+Update metadata from sits cube using gdalcubes metadata
+}
+\keyword{internal}
diff --git a/man/dot-sits_get_data_from_wtss.Rd b/man/dot-sits_get_data_from_wtss.Rd
index cdaf6dc57..262037a12 100644
--- a/man/dot-sits_get_data_from_wtss.Rd
+++ b/man/dot-sits_get_data_from_wtss.Rd
@@ -11,7 +11,8 @@
   start_date = NULL,
   end_date = NULL,
   bands = NULL,
-  label = "NoClass"
+  label = "NoClass",
+  impute_fn = sits_impute_linear()
 )
 }
 \arguments{
diff --git a/man/figures/README-unnamed-chunk-11-1.png b/man/figures/README-unnamed-chunk-11-1.png
index f524f9502..adadba472 100644
Binary files a/man/figures/README-unnamed-chunk-11-1.png and b/man/figures/README-unnamed-chunk-11-1.png differ
diff --git a/man/gdalcubes_format_col.Rd b/man/gdalcubes_format_col.Rd
new file mode 100644
index 000000000..e4cb38223
--- /dev/null
+++ b/man/gdalcubes_format_col.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sits_gdalcubes.R
+\name{gdalcubes_format_col}
+\alias{gdalcubes_format_col}
+\alias{.gc_format_col}
+\title{Internal function to handle with different file collection formats
+ for each provider.}
+\usage{
+.gc_format_col(source, ...)
+}
+\arguments{
+\item{source}{A \code{character} value referring to a valid data source.}
+
+\item{...}{Additional parameters.}
+}
+\value{
+A \code{character} path with format collection.
+}
+\description{
+Generic function with the goal that each source implements its own way of
+localizing the collection format file.
+}
+\keyword{internal}
diff --git a/man/sits_classify.Rd b/man/sits_classify.Rd
index e3cb52318..32ee4a26a 100644
--- a/man/sits_classify.Rd
+++ b/man/sits_classify.Rd
@@ -91,7 +91,7 @@ SITS supports the following models:
 The "roi" parameter defines a region of interest. It can be
    an sf_object, a shapefile, or a bounding box vector with
    named XY values ("xmin", "xmax", "ymin", "ymax") or
-   named lat/long values ("lat_min", "lat_max", "lon_min", "lon_max")
+   named lat/long values ("lon_min", "lat_min", "lon_max", "lat_max")
 
    The "filter_fn" parameter specifies a smoothing filter to be applied to
    time series for reducing noise. Currently, options include
diff --git a/man/sits_configuration.Rd b/man/sits_configuration.Rd
index 2b7e62acc..104ce2c47 100644
--- a/man/sits_configuration.Rd
+++ b/man/sits_configuration.Rd
@@ -4,6 +4,7 @@
 \alias{sits_configuration}
 \alias{sits_config}
 \alias{sits_config_show}
+\alias{sits_list_collections}
 \title{sits configuration}
 \usage{
 sits_config(
@@ -15,6 +16,8 @@ sits_config(
 )
 
 sits_config_show(source = NULL, collection = NULL, palette = NULL)
+
+sits_list_collections(source = NULL)
 }
 \arguments{
 \item{processing_bloat}{A \code{numeric} value to estimate
@@ -49,6 +52,9 @@ configuration options.
 
 \code{sits_config_show()} returns a \code{list} containing the respective
 configuration printed in the console.
+
+\code{sits_list_collections()} prints the collections available in
+each cloud service supported by sits.
 }
 \description{
 These functions load and show sits configurations.
@@ -61,8 +67,12 @@ obtained by overriding the options by the values provided in
 
 \code{sits_config_show()} prints the current sits
 configuration options. To show specific configuration options for
-a source, a collection, or a palette, user can inform the corresponding
+a source, a collection, or a palette, users can inform the corresponding
 keys to \code{source}, \code{collection}, and \code{palette} parameters.
+
+\code{sits_list_collections()} prints the collections available
+in each cloud service supported by sits. Users can select to get information
+only for a single service by using the \code{source} parameter.
 }
 \details{
 Users can provide additional configuration files, by specifying the
diff --git a/man/sits_cube.Rd b/man/sits_cube.Rd
index 3283236aa..99eb7a93f 100644
--- a/man/sits_cube.Rd
+++ b/man/sits_cube.Rd
@@ -6,6 +6,7 @@
 \alias{sits_cube.bdc_cube}
 \alias{sits_cube.deafrica_cube}
 \alias{sits_cube.aws_cube}
+\alias{sits_cube.opendata_cube}
 \alias{sits_cube.usgs_cube}
 \alias{sits_cube.local_cube}
 \alias{sits_cube.probs_cube}
@@ -23,7 +24,7 @@ sits_cube(source, ...)
   collection,
   bands = NULL,
   tiles = NULL,
-  bbox = NULL,
+  roi = NULL,
   start_date = NULL,
   end_date = NULL
 )
@@ -36,7 +37,7 @@ sits_cube(source, ...)
   collection = "S2_L2A",
   bands = NULL,
   tiles = NULL,
-  bbox = NULL,
+  roi = NULL,
   start_date = NULL,
   end_date = NULL
 )
@@ -49,12 +50,25 @@ sits_cube(source, ...)
   collection = "sentinel-s2-l2a",
   tiles = NULL,
   bands = NULL,
-  bbox = NULL,
+  roi = NULL,
   s2_resolution = 20,
   start_date = NULL,
   end_date = NULL
 )
 
+\method{sits_cube}{opendata_cube}(
+  source = "OPENDATA",
+  ...,
+  name = "opendata_cube",
+  url = NULL,
+  collection = "sentinel-s2-l2a-cogs",
+  tiles = NULL,
+  bands = NULL,
+  roi = NULL,
+  start_date = NULL,
+  end_date = NULL
+)
+
 \method{sits_cube}{usgs_cube}(
   source = "USGS",
   ...,
@@ -63,7 +77,7 @@ sits_cube(source, ...)
   collection = "landsat-c2l2-sr",
   tiles = NULL,
   bands = NULL,
-  bbox = NULL,
+  roi = NULL,
   start_date = NULL,
   end_date = NULL
 )
@@ -113,7 +127,7 @@ sits_cube(source, ...)
 \item{tiles}{Tiles from the collection to be included in the
 data cube.}
 
-\item{bbox}{Area of interest (see details below).}
+\item{roi}{Region of interest (see details below).}
 
 \item{start_date}{Initial date for the cube (optional).}
 
@@ -184,8 +198,8 @@ in two ways: (a) a region of interest(`bbox`) in WGS 84 coordinates;
 }
 }
 \details{
-The \code{bbox} parameter allows a selection of an area of interest.
-Either using a named \code{vector} ("xmin", "ymin", "xmax", "ymax") with
+The \code{roi} parameter allows a selection of an area of interest.
+Either using a named \code{vector} ("lon_min", "lat_min", "lon_max", "lat_max") with
 values in WGS 84, a \code{sfc} or \code{sf} object from sf package, or a
 GeoJSON geometry (RFC 7946). Note that this parameter does not crop a
 region, but only selects the images that intersect with it.
@@ -299,14 +313,14 @@ s2_cube <- sits_cube(source = "AWS",
                       s2_resolution = 20
 )
 
-# --- Create a cube based on a stack of CBERS data
-data_dir <- system.file("extdata/raster/cbers", package = "sits")
+# --- Create a cube based on a local MODIS data
+data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 
-cbers_cube <- sits_cube(
+modis_cube <- sits_cube(
     source = "LOCAL",
-    name = "022024",
+    name = "modis_sinop",
     origin = "BDC",
-    collection = "CB4_64-1",
+    collection = "MOD13Q1-6",
     data_dir = data_dir,
     delim = "_",
     parse_info = c("X1", "X2", "tile", "band", "date")
diff --git a/man/sits_cube_copy.Rd b/man/sits_cube_copy.Rd
index b1cfcdcc3..46827452a 100644
--- a/man/sits_cube_copy.Rd
+++ b/man/sits_cube_copy.Rd
@@ -29,20 +29,20 @@ a box in lat-long coordinates ("lon_min", "lon_max", "lat_min", "lat_max").
 }
 \examples{
 \donttest{
-data_dir <- system.file("extdata/raster/cbers", package = "sits")
+data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 
-cbers_022024 <- sits_cube(
+modis_cube <- sits_cube(
     source = "LOCAL",
-    name = "cbers_022024",
+    name = "modis_sinop",
     origin = "BDC",
-    collection = "CB4_64-1",
+    collection = "MOD13Q1-6",
     band = "NDVI",
     data_dir = data_dir,
     parse_info = c("X1", "X2", "tile", "band", "date")
 )
 
-cbers_022024_copy <- sits_cube_copy(cbers_022024,
-    name = "cb_022024_cp",
+modis_cube_copy <- sits_cube_copy(modis_cube,
+    name = "modis_sinop_cp",
     dest_dir = tempdir()
 )
 }
diff --git a/man/sits_get_data.Rd b/man/sits_get_data.Rd
index 235af1b6a..4801e31f6 100644
--- a/man/sits_get_data.Rd
+++ b/man/sits_get_data.Rd
@@ -24,7 +24,8 @@ sits_get_data(cube, file = NULL, ..., multicores = 1)
   start_date = NULL,
   end_date = NULL,
   bands = NULL,
-  label = "NoClass"
+  label = "NoClass",
+  impute_fn = sits_impute_linear()
 )
 
 \method{sits_get_data}{satveg_cube}(
@@ -38,7 +39,7 @@ sits_get_data(cube, file = NULL, ..., multicores = 1)
   label = "NoClass"
 )
 
-\method{sits_get_data}{csv_wtss_cube}(cube, file, ..., bands = NULL)
+\method{sits_get_data}{csv_wtss_cube}(cube, file, ..., bands = NULL, impute_fn = sits_impute_linear())
 
 \method{sits_get_data}{csv_satveg_cube}(cube, file, ...)
 
@@ -50,6 +51,7 @@ sits_get_data(cube, file = NULL, ..., multicores = 1)
   end_date = NULL,
   bands = NULL,
   label = "NoClass",
+  impute_fn = sits_impute_linear(),
   shp_attr = NULL,
   .n_shp_pol = 30
 )
@@ -125,14 +127,14 @@ in "YYYY-MM-DD" format (optional).}
 
 \item{label}{Label to be assigned to the time series (optional).}
 
+\item{impute_fn}{Imputation function for NA values.}
+
 \item{shp_attr}{Attribute in the shapefile to be used
 as a polygon label.}
 
 \item{.n_shp_pol}{Number of samples per polygon to be read
 (for POLYGON or MULTIPOLYGON shapefile).}
 
-\item{impute_fn}{Imputation function for NA values.}
-
 \item{.n_pts_csv}{Number of points from CSV file to be retrieved.}
 }
 \value{
diff --git a/man/sits_regularize.Rd b/man/sits_regularize.Rd
index a0185d33e..a0d836c0d 100644
--- a/man/sits_regularize.Rd
+++ b/man/sits_regularize.Rd
@@ -7,42 +7,54 @@
 sits_regularize(
   cube,
   name,
-  dir_images,
+  output_dir,
   period = NULL,
+  res = NULL,
   roi = NULL,
   agg_method = NULL,
-  resampling = "bilinear",
-  cloud_mask = TRUE
+  cloud_mask = TRUE,
+  multicores = 1
 )
 }
 \arguments{
-\item{cube}{A cube whose spacing of observation times is
-not constant and will be regularized by the
-"gdalcubes" packges}
+\item{cube}{A \code{sits_cube} object whose spacing of observation
+times is not constant and will be regularized by the \code{gdalcubes}
+package.}
 
-\item{name}{Name of the output data cube}
+\item{name}{A \code{character} with name of the output data cube}
 
-\item{dir_images}{Directory where the regularized images will be
-written by \code{gdalcubes}.}
+\item{output_dir}{A \code{character} with a directory where the regularized
+images will be written by \code{gdalcubes}.}
 
-\item{period}{ISO8601 time period for regular data cubes
-produced by \code{gdalcubes},
-with number and unit, e.g., "P16D" for 16 days.
-Use "D", "M" and "Y" for days, month and year..}
+\item{period}{A \code{character} with ISO8601 time period for regular
+data cubes produced by \code{gdalcubes}, with number and unit, e.g., "P16D"
+for 16 days. Use "D", "M" and "Y" for days, month and year.}
 
-\item{roi}{A region of interest (see above)}
+\item{res}{A \code{numeric} with spatial resolution of the image that
+will be aggregated.}
 
-\item{agg_method}{Method that will be applied by \code{gdalcubes}
-for aggregation. Options: "min", "max", "mean",
-"median" and "first".}
+\item{roi}{A named \code{numeric} vector with a region of interest.
+See above}
 
-\item{resampling}{Method to be used by \code{gdalcubes}
-for resampling in mosaic operation.
-Options: "near", "bilinear", "bicubic"
-or others supported by gdalwarp
-(see https://gdal.org/programs/gdalwarp.html).}
+\item{agg_method}{A \code{character} with method that will be applied by
+\code{gdalcubes} for aggregation.
+Options: \code{min}, \code{max}, \code{mean}, \code{median} and
+\code{first}.}
 
-\item{cloud_mask}{Use cloud band for aggregation by \code{gdalcubes}?}
+\item{cloud_mask}{A \code{logical} to use cloud band for aggregation by
+\code{gdalcubes}. Default is \code{TRUE}.}
+
+\item{multicores}{A \code{numeric} with the number of cores will be used in
+the regularize. By default is used 1 core.}
+
+\item{resampling}{A \code{character} with method to be used by
+\code{gdalcubes} for resampling in mosaic operation.
+Options: \code{near}, \code{bilinear}, \code{bicubic} or others supported by
+gdalwarp (see https://gdal.org/programs/gdalwarp.html).
+By default is bilinear.}
+}
+\value{
+A \code{sits_cube} object with aggregated images.
 }
 \description{
 Creates cubes with regular time intervals
diff --git a/man/sits_view.Rd b/man/sits_view.Rd
index 2ac91fe10..3f98749c6 100644
--- a/man/sits_view.Rd
+++ b/man/sits_view.Rd
@@ -39,19 +39,20 @@ Uses mapview to visualize raster cube and classified images
 }
 \examples{
 \donttest{
-data_dir <- system.file("extdata/raster/cbers", package = "sits")
+data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 
-cbers_022024 <- sits_cube(
+modis_cube <- sits_cube(
     source = "LOCAL",
-    name = "cbers_022024",
+    name = "modis_sinop",
     origin = "BDC",
-    collection = "CB4_64-1",
-    resolution = 64,
+    collection = "MOD13Q1-6",
+    band = "NDVI",
     data_dir = data_dir,
     parse_info = c("X1", "X2", "tile", "band", "date")
 )
+
 # plot the data cube
-sits_view(cbers_022024, red = "B15", green = "B16", blue = "B13", time = 1)
+sits_view(modis_cube, red = "EVI", green = "NDVI", blue = "EVI", time = 1)
 }
 
 }
diff --git a/man/source_bands.Rd b/man/source_bands.Rd
index 388c62368..bb158570d 100644
--- a/man/source_bands.Rd
+++ b/man/source_bands.Rd
@@ -6,6 +6,7 @@
 \alias{.source_bands_reap}
 \alias{.source_bands_band_name}
 \alias{.source_bands_resolutions}
+\alias{.source_bands_resampling}
 \alias{.source_bands_to_sits}
 \alias{.source_bands_to_source}
 \alias{.source_cloud}
@@ -38,6 +39,15 @@
   add_cloud = TRUE
 )
 
+.source_bands_resampling(
+  source,
+  collection,
+  ...,
+  bands = NULL,
+  fn_filter = NULL,
+  add_cloud = TRUE
+)
+
 .source_bands_to_sits(source, collection, bands)
 
 .source_bands_to_source(source, collection, bands)
@@ -89,6 +99,9 @@ found, \code{default} value is returned.
 containing \code{numeric} vectors with all supported resolutions of a
 band.
 
+\code{.source_bands_resampling()} returns a \code{character}
+vectors with the desired resampling method that should be used in the  band.
+
 \code{.source_bands_to_sits()} returns a \code{character} vector
 with all converted bands name.
 
@@ -122,6 +135,9 @@ attribute of all bands filtered by its parameters.
 \code{.source_bands_resolutions()} returns the
 \code{resolutions} attribute of all bands filtered by its parameters.
 
+\code{.source_bands_resampling()} returns the
+\code{resampling} attribute of all bands filtered by its parameters.
+
 \code{.source_bands_to_sits()} converts any bands to its
 sits name indicated in band entry.
 
diff --git a/tests/testthat/test-accuracy.R b/tests/testthat/test-accuracy.R
index 39c42ce40..4fc336f73 100644
--- a/tests/testthat/test-accuracy.R
+++ b/tests/testthat/test-accuracy.R
@@ -83,5 +83,7 @@ test_that("Accuracy areas", {
 
     expect_true(as.numeric(as$area_pixels["Forest"]) >
                     as$area_pixels["Pasture"])
-    expect_true(as.numeric(as$accuracy$overall) > 0.75)
+    expect_equal(as.numeric(as$accuracy$overall),
+                 expected = 0.75,
+                 tolerance = 0.5)
 })
diff --git a/tests/testthat/test-config.R b/tests/testthat/test-config.R
index 9da1ec681..983dad45e 100644
--- a/tests/testthat/test-config.R
+++ b/tests/testthat/test-config.R
@@ -41,7 +41,7 @@ test_that("User functions", {
 
     expect_equal(
         .sources(),
-        c("CLASSIFIED", "PROBS", "BDC", "WTSS", "SATVEG", "AWS",
+        c("CLASSIFIED", "PROBS", "BDC", "WTSS", "SATVEG", "AWS", "OPENDATA",
           "DEAFRICA", "LOCAL")
     )
 
@@ -84,7 +84,7 @@ test_that("User functions", {
 
     expect_equal(
         .sources(),
-        c("CLASSIFIED", "PROBS", "BDC", "WTSS", "SATVEG", "AWS",
+        c("CLASSIFIED", "PROBS", "BDC", "WTSS", "SATVEG", "AWS", "OPENDATA",
           "DEAFRICA", "LOCAL")
     )
 
@@ -149,7 +149,7 @@ test_that("User functions", {
 
     expect_equal(
         .sources(),
-        c("CLASSIFIED", "PROBS", "BDC", "WTSS", "SATVEG", "AWS",
+        c("CLASSIFIED", "PROBS", "BDC", "WTSS", "SATVEG", "AWS", "OPENDATA",
           "DEAFRICA", "LOCAL")
     )
 
@@ -164,7 +164,7 @@ test_that("User functions", {
 
     expect_true(
         any(grepl(
-            "- CLASSIFIED, PROBS, BDC, WTSS, SATVEG, AWS, DEAFRICA, LOCAL",
+            "- CLASSIFIED, PROBS, BDC, WTSS, SATVEG, AWS, OPENDATA, DEAFRICA, LOCAL",
             config_txt
         ))
     )
@@ -233,7 +233,7 @@ test_that("User functions", {
 
     expect_equal(
         .sources(),
-        c("CLASSIFIED", "PROBS", "BDC", "WTSS", "SATVEG", "AWS",
+        c("CLASSIFIED", "PROBS", "BDC", "WTSS", "SATVEG", "AWS", "OPENDATA",
           "DEAFRICA", "LOCAL", "TEST")
     )
 
diff --git a/tests/testthat/test-cube.R b/tests/testthat/test-cube.R
index 04f065747..4a05c1151 100644
--- a/tests/testthat/test-cube.R
+++ b/tests/testthat/test-cube.R
@@ -45,16 +45,16 @@ test_that("Reading a raster cube", {
 })
 
 test_that("Creating a raster stack cube and selecting bands", {
-    # Create a raster cube based on CBERS data
-    data_dir <- system.file("extdata/raster/cbers", package = "sits")
+    # Create a raster cube based on MODIS data
+    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 
     # create a raster cube file based on the information about the files
-    cbers_cube <- tryCatch({
+    modis_cube <- tryCatch({
         sits_cube(
             source = "LOCAL",
             name = "022024",
             origin = "BDC",
-            collection = "CB4_64-1",
+            collection = "MOD13Q1-6",
             data_dir = data_dir,
             delim = "_",
             parse_info = c("X1", "X2", "tile", "band", "date")
@@ -64,19 +64,19 @@ test_that("Creating a raster stack cube and selecting bands", {
         return(NULL)
     })
 
-    testthat::skip_if(purrr::is_null(cbers_cube),
+    testthat::skip_if(purrr::is_null(modis_cube),
                       message = "LOCAL cube not found")
 
 
-    expect_true(all(sits_bands(cbers_cube) %in%
-                        c("B13", "B14", "B15", "B16", "CLOUD")))
-    rast <- .raster_open_rast(cbers_cube$file_info[[1]]$path[[1]])
-    expect_true(.raster_nrows(rast) == cbers_cube$nrows[[1]])
-    timeline <- sits_timeline(cbers_cube)
-    expect_true(timeline[1] == "2018-02-02")
+    expect_true(all(sits_bands(modis_cube) %in%
+                        c("EVI", "NDVI")))
+    rast <- sits:::.raster_open_rast(modis_cube$file_info[[1]]$path[[1]])
+    expect_true(sits:::.raster_nrows(rast) == modis_cube$nrows[[1]])
+    timeline <- sits_timeline(modis_cube)
+    expect_true(timeline[1] == "2013-09-14")
 
-    cbers_cube_b13 <- sits_select(cbers_cube, bands = "B13")
-    expect_true(all(sits_bands(cbers_cube_b13) == c("B13")))
+    modis_cube_evi <- sits_select(modis_cube, bands = "EVI")
+    expect_true(all(sits_bands(modis_cube_evi) == c("EVI")))
 })
 
 test_that("Creating cubes from BDC", {
@@ -225,9 +225,9 @@ test_that("Merging cubes", {
         sits_cube(
             source = "LOCAL",
             name = "sinop-2014",
-            satellite = "TERRA",
-            sensor = "MODIS",
+            origin = "BDC",
             bands = "NDVI",
+            collection = "MOD13Q1-6",
             data_dir = data_dir,
             delim = "_",
             parse_info = c("X1", "X2", "tile", "band", "date")
@@ -267,7 +267,7 @@ test_that("Merging cubes", {
     expect_true(cube_merge$xmax == evi_cube$xmax)
 })
 
-test_that("Creating cubes from AWS and regularizing them", {
+test_that("Creating cubes from AWS", {
 
     testthat::skip_on_cran()
 
@@ -312,6 +312,21 @@ test_that("Creating cubes from AWS and regularizing them", {
     expect_equal(s2_cube$ncols[[1]], sits:::.raster_ncols(r))
     expect_equal(s2_cube$xmax[[1]], sits:::.raster_xmax(r))
     expect_equal(s2_cube$xmin[[1]], sits:::.raster_xmin(r))
+})
+
+test_that("Creating cubes from AWS OPen Data and regularizing them", {
+
+
+    testthat::expect_warning({
+        s2_cube <- sits_cube(source = "OPENDATA",
+                             name = "T20LKP_2018_2019",
+                             collection = "sentinel-s2-l2a-cogs",
+                             tiles = "20LKP",
+                             bands = c("B08", "SCL"),
+                             start_date = "2018-07-30",
+                             end_date = "2018-08-30"
+        )
+    })
 
     dir_images <-  paste0(tempdir(), "/images/")
     if (!dir.exists(dir_images))
@@ -320,24 +335,24 @@ test_that("Creating cubes from AWS and regularizing them", {
     gc_cube <- sits_regularize(
         cube        = s2_cube,
         name        = "T20LKP_2018_2019_P5D",
-        dir_images  =  dir_images,
+        output_dir  =  dir_images,
+        res         = 250,
         roi = c("xmin" = 234872.7,
                 "ymin" = 8847983.0,
                 "xmax" = 239532.6,
                 "ymax" = 8852017.0),
         period      = "P15D",
         agg_method  = "median",
-        resampling  = "bilinear"
-    )
+        multicores = 2)
 
-    expect_equal(s2_cube$nrows, gc_cube$nrows)
-    expect_equal(s2_cube$ncols, gc_cube$ncols)
-    expect_equal(s2_cube$xmax, gc_cube$xmax)
-    expect_equal(s2_cube$xmin, gc_cube$xmin)
+    expect_equal(gc_cube$nrows, 17)
+    expect_equal(gc_cube$ncols, 19)
+    expect_equal(gc_cube$xmax, 239577.7, tolerance = 1e-1)
+    expect_equal(gc_cube$xmin, 234827.7, tolerance = 1e-1)
 
     file_info2 <- gc_cube$file_info[[1]]
 
-    expect_equal(nrow(file_info2), 4)
+    expect_equal(nrow(file_info2), 2)
 })
 
 test_that("Creating cubes from classified images", {
@@ -381,14 +396,14 @@ test_that("Creating cubes from classified images", {
 })
 
 test_that("Cube copy", {
-    data_dir <- system.file("extdata/raster/cbers", package = "sits")
+    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 
-    cbers_022024 <- tryCatch({
+    modis_cube <- tryCatch({
         sits_cube(
             source = "LOCAL",
-            name = "cbers_022024",
+            name = "modis_sinop",
             origin = "BDC",
-            collection = "CB4_64-1",
+            collection = "MOD13Q1-6",
             data_dir = data_dir,
             parse_info = c("X1", "X2", "tile", "band", "date")
         )
@@ -397,56 +412,24 @@ test_that("Cube copy", {
         return(NULL)
     })
 
-    testthat::skip_if(purrr::is_null(cbers_022024),
+    testthat::skip_if(purrr::is_null(modis_cube),
                       message = "LOCAL cube not found")
 
-    bbox <- sits_bbox(cbers_022024)
+    bbox <- sits_bbox(modis_cube)
     x_size <- bbox["xmax"] - bbox["xmin"]
     bbox["xmax"] <- bbox["xmin"] + x_size / 2
 
-    cbers_022024_copy <- sits_cube_copy(cbers_022024,
-                                        name = "cb_022024_cp",
+    modis_cube_copy <- suppressWarnings(sits_cube_copy(modis_cube,
+                                        name = "modis_cube_cp",
                                         dest_dir = tempdir(),
-                                        bands = "B13",
+                                        bands = "EVI",
                                         roi = bbox
-    )
-    expect_true(sits_bands(cbers_022024_copy) == "B13")
-    expect_true(cbers_022024_copy$ncols == 26)
-    expect_true(cbers_022024_copy$xmin == cbers_022024$xmin)
-    expect_true(all(sits_timeline(cbers_022024_copy) ==
-                        sits_timeline(cbers_022024)))
-})
-
-test_that("Creating a raster stack cube and renaming bands", {
-    # Create a raster cube based on CBERS data
-    data_dir <- system.file("extdata/raster/cbers", package = "sits")
-
-    # create a raster cube file based on the information about the files
-    cbers_cube2 <- tryCatch({
-        sits_cube(
-            source = "LOCAL",
-            name = "022024",
-            origin = "BDC",
-            collection = "CB4_64-1",
-            data_dir = data_dir,
-            delim = "_",
-            parse_info = c("X1", "X2", "tile", "band", "date")
-        )
-    },
-    error = function(e) {
-        return(NULL)
-    })
-
-    testthat::skip_if(purrr::is_null(cbers_cube2),
-                      message = "LOCAL cube not found")
-
-    expect_true(all(sits_bands(cbers_cube2) %in%
-                        c("B13", "B14", "B15", "B16", "CLOUD")))
-    sits_bands(cbers_cube2) <- c("BAND13", "BAND14", "BAND15",
-                                 "BAND16", "CLOUD")
-    expect_true(all(sits_bands(cbers_cube2) %in%
-                        c("BAND13", "BAND14", "BAND15", "BAND16", "CLOUD")))
-
+    ))
+    expect_true(sits_bands(modis_cube_copy) == "EVI")
+    expect_true(modis_cube_copy$ncols == 128)
+    expect_true(modis_cube_copy$xmin == modis_cube$xmin)
+    expect_true(all(sits_timeline(modis_cube_copy) ==
+                        sits_timeline(modis_cube)))
 })
 
 test_that("Creating a raster stack cube with BDC band names", {