diff --git a/DESCRIPTION b/DESCRIPTION
index 3ecc5df..52e7657 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -41,5 +41,5 @@ Suggests:
 LinkingTo: Rcpp,
     RcppArmadillo
 License: GPL (>=3)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 LazyData: true
diff --git a/R/RStoolbox.R b/R/RStoolbox.R
index d79ed63..9ac197a 100644
--- a/R/RStoolbox.R
+++ b/R/RStoolbox.R
@@ -62,7 +62,7 @@
 #' @importFrom grDevices hsv
 #' @useDynLib RStoolbox
 #' @importFrom Rcpp sourceCpp
-#' @docType package
+#' @docType _PACKAGE
 #' @name RStoolbox
 NULL
 
@@ -100,5 +100,17 @@ NULL
 #' @keywords datasets
 #' @name lsat
 #' @examples
-#' ggRGB(lsat, stretch = "lin")
+#' ggRGB(lsat, stretch = "sqrt")
+NULL
+
+#' Sentinel 2 MSI L2A Scene
+#'
+#' Contains all 13 bands in already converted spectral reflectances
+#'
+#' @usage sen2
+#' @docType data
+#' @keywords datasets
+#' @name sen2
+#' @examples
+#' ggRGB(sen2, r=4, g=3, b=2, stretch = "lin")
 NULL
diff --git a/R/classifyQA.R b/R/classifyQA.R
index 1478bfb..ade05e0 100644
--- a/R/classifyQA.R
+++ b/R/classifyQA.R
@@ -2,7 +2,7 @@
 #' 
 #' extracts five classes from QA band: background, cloud, cirrus, snow and water.
 #' 
-#' @param img RasterLayer or SpatRaster. Landsat 8 OLI QA band.
+#' @param img SpatRaster. Landsat 8 OLI QA band.
 #' @param type Character. Classes which should be returned. One or more of c("background", "cloud", "cirrus","snow", "water").
 #' @param confLayers Logical. Return one layer per class classified by confidence levels, i.e. cloud:low, cloud:med, cloud:high.
 #' @param ... further arguments passed to \link[terra]{writeRaster}
diff --git a/R/cloudMask.R b/R/cloudMask.R
index 0ee5bf0..0a729ba 100644
--- a/R/cloudMask.R
+++ b/R/cloudMask.R
@@ -4,7 +4,7 @@
 #' and thermal band for semi-automated creation of a cloud mask. Since it relies on thermal information it doesn't work well for sensors without
 #' thermal bands. 
 #' 
-#' @param x RasterBrick or RasterStack or SpatRaster with reflectance and brightness temperature OR the mask of a previous run of \code{cloudMask} with \code{returnDiffLayer=TRUE}.
+#' @param x SpatRaster with reflectance and brightness temperature OR the mask of a previous run of \code{cloudMask} with \code{returnDiffLayer=TRUE}.
 #' @param threshold Numeric. cloud detection threshold. If not provided it will be guessed. Everything *below* this threshold will be considered a cloud pixel (unless it is removed by filtering afterwards).
 #' @param blue Character or integer. Bandname or number for the blue band
 #' @param tir Character or integer. Bandname or number for the thermal band
diff --git a/R/coregisterImages.R b/R/coregisterImages.R
index 14559ca..e133a11 100644
--- a/R/coregisterImages.R
+++ b/R/coregisterImages.R
@@ -3,8 +3,8 @@
 #' Shifts an image to match a reference image. Matching is based on maximum
 #' mutual information. 
 #' 
-#' @param img SpatRaster or SpatRaster. Image to shift to match reference image. \code{img} and \code{ref} must have equal numbers of bands.
-#' @param ref SpatRaster or SpatRaster. Reference image. \code{img} and \code{ref} must have equal numbers of bands.
+#' @param img SpatRaster. Image to shift to match reference image. \code{img} and \code{ref} must have equal numbers of bands.
+#' @param ref SpatRaster. Reference image. \code{img} and \code{ref} must have equal numbers of bands.
 #' @param shift Numeric or matrix. If numeric, then shift is the maximal absolute radius (in pixels of \code{img} resolution) which \code{img} is shifted (\code{seq(-shift, shift, by=shiftInc)}). 
 #'  If shift is a matrix it must have two columns (x shift and y shift), then only these shift values will be tested.
 #' @param shiftInc Numeric. Shift increment (in pixels, but not restricted to integer). Ignored if \code{shift} is a matrix.
diff --git a/R/estimateHaze.R b/R/estimateHaze.R
index e7f4c5c..b673c39 100644
--- a/R/estimateHaze.R
+++ b/R/estimateHaze.R
@@ -2,7 +2,7 @@
 #' 
 #' estimates the digital number (DN) pixel value of *dark* objects for the visible wavelength range.
 #' 
-#' @param x RasterLayer or SpatRaster or a previous result from \code{estimateHaze} with \code{returnTables = TRUE} from which to estimate haze
+#' @param x SpatRaster or a previous result from \code{estimateHaze} with \code{returnTables = TRUE} from which to estimate haze
 #' @param hazeBands Integer or Character. Band number or bandname from which to estimate atmospheric haze (optional if x contains only one layer)
 #' @param darkProp Numeric. Proportion of pixels estimated to be dark.
 #' @param maxSlope Logical. Use \code{darkProp} only as an upper boundary and search for the DN of maximum slope in the histogram below this value.
diff --git a/R/fCover.R b/R/fCover.R
index 269f9c0..faedd48 100755
--- a/R/fCover.R
+++ b/R/fCover.R
@@ -3,8 +3,8 @@
 #' fCover takes a classified high resolution image, e.g. vegetation and non-vegetation based on Landsat and calculates cover fractions for
 #' pixels of a coarser resolution, e.g. MODIS.
 #' 
-#' @param classImage high resolution RasterLayer or SpatRaster containing a landcover classification, e.g. as obtained by \link{superClass}.
-#' @param predImage coarse resolution RasterLayer or SpatRaster for which fractional cover will be estimated.
+#' @param classImage high resolution SpatRaster containing a landcover classification, e.g. as obtained by \link{superClass}.
+#' @param predImage coarse resolution SpatRaster for which fractional cover will be estimated.
 #' @param nSamples Integer. Number of pixels to sample from predImage to train the regression model
 #' @param classes Integer. Classes for which fractional cover should be estimated (one or more). 
 #' @param model Character. Which model to fit for image regression. See \link[caret]{train} for options. Defaults to randomForest ('rf')
diff --git a/R/getValidation.R b/R/getValidation.R
index 0144a10..40ad874 100755
--- a/R/getValidation.R
+++ b/R/getValidation.R
@@ -11,7 +11,7 @@
 #' @examples
 #' library(pls)
 #' ## Fit classifier (splitting training into 70\% training data, 30\% validation data)
-#' train <- readRDS(system.file("external/trainingPoints.rds", package="RStoolbox"))
+#' train <- readRDS(system.file("external/trainingPoints_lsat.rds", package="RStoolbox"))
 #' SC   <- superClass(rlogo, trainData = train, responseCol = "class",
 #'                     model="pls", trainPartition = 0.7)
 #' ## Independent testset-validation
diff --git a/R/ggR.R b/R/ggR.R
index 75d3688..cb58d11 100644
--- a/R/ggR.R
+++ b/R/ggR.R
@@ -2,7 +2,7 @@
 #' 
 #' Plot single layer imagery in grey-scale. Can be used with a SpatRaster.
 #' 
-#' @param img raster
+#' @param img SpatRaster
 #' @param layer Character or numeric. Layername or number. Can be more than one layer, in which case each layer is plotted in a subplot.
 #' @param maxpixels Integer. Maximal number of pixels to sample.
 #' @param ext Extent object to crop the image
diff --git a/R/ggRGB.R b/R/ggRGB.R
index 0578e68..91c47bf 100644
--- a/R/ggRGB.R
+++ b/R/ggRGB.R
@@ -4,7 +4,7 @@
 #' 
 #' Functionality is based on \code{\link[terra]{plotRGB}} from the raster package.
 #' 
-#' @param img RasterStack or RasterBrick
+#' @param img SpatRaster
 #' @param r Integer or character. Red layer in x. Can be set to \code{NULL}, in which case the red channel will be set to zero.
 #' @param g Integer or character. Green layer in x. Can be set to \code{NULL}, in which case the green channel will be set to zero.
 #' @param b Integer or character. Blue layer in x. Can be set to \code{NULL}, in which case the blue channel will be set to zero.
diff --git a/R/histMatch.R b/R/histMatch.R
index 278d365..16a120d 100644
--- a/R/histMatch.R
+++ b/R/histMatch.R
@@ -3,8 +3,8 @@
 #' Performs image to image contrast adjustments based on histogram matching using empirical cumulative
 #'  distribution functions from both images.
 #' 
-#' @param x RasterLayer or SpatRaster. Source raster which is to be modified.
-#' @param ref RasterLayer or SpatRaster. Reference raster, to which x will be matched.
+#' @param x SpatRaster. Source raster which is to be modified.
+#' @param ref SpatRaster. Reference raster, to which x will be matched.
 #' @param xmask RasterLayer or SpatRaster. Mask layer for \code{x} to exclude pixels which might distort the histogram, i.e. are not present in \code{ref}. Any NA pixel in \code{xmask} will be ignored (\code{maskvalue = NA}).
 #' @param refmask RasterLayer or SpatRaster. Mask layer for \code{ref}. Any NA pixel in \code{refmask} will be ignored (\code{maskvalue = NA}).
 #' @param nSamples Integer. Number of random samples from each image to build the histograms.
diff --git a/R/internalFunctions.R b/R/internalFunctions.R
index da7a505..611eb77 100644
--- a/R/internalFunctions.R
+++ b/R/internalFunctions.R
@@ -57,7 +57,7 @@
 }
 
 #' Run raster functions
-#' @param SpatRaster Object
+#' @param raster SpatRaster
 #' @param rasterFun function. E.g. predict, calc, overlay 
 #' @param args list. arguments to be passed to rasterFun.
 #' @param wrArgs arguments to be passed to rasterFun, typically to writeRaster
diff --git a/R/mesma.R b/R/mesma.R
index a9e6f1d..f1dbc39 100644
--- a/R/mesma.R
+++ b/R/mesma.R
@@ -2,7 +2,7 @@
 #' 
 #' \code{mesma} performs a multiple endmember spectral mixture analysis on a multiband raster image.
 #' 
-#' @param img RasterLayer or RasterBrick or SpatRaster. Remote sensing imagery (usually hyperspectral).
+#' @param img SpatRaster. Remote sensing imagery (usually hyperspectral).
 #' @param em Matrix or data.frame with spectral endmembers. Rows represent a single endmember of a class, columns represent the spectral bands (i.e. columns correspond to number of bands in \code{img}). Number of rows needs to be > 1.
 #' @param method Character. Select an unmixing method. Currently, only "NNLS" is implemented. Default is "NNLS".
 #' \itemize{
diff --git a/R/oneHotEncode.R b/R/oneHotEncode.R
index 3e73df4..3b5270b 100644
--- a/R/oneHotEncode.R
+++ b/R/oneHotEncode.R
@@ -2,7 +2,7 @@
 #' 
 #' Splits a categorical raster layer (or a vector) into a multilayer raster (or matrix).
 #' 
-#' @param img RasterLayer or SpatRaster or integer/numeric vector containing multiple classes
+#' @param img SpatRaster or integer/numeric vector containing multiple classes
 #' @param classes integer: vector of classes which should be extracted
 #' @param background integer: background value (default = 0)
 #' @param foreground integer: foreground value (default = 1)
diff --git a/R/panSharpen.R b/R/panSharpen.R
index 6b35397..51ccdda 100644
--- a/R/panSharpen.R
+++ b/R/panSharpen.R
@@ -3,8 +3,8 @@
 #' provides different methods for pan sharpening a coarse resolution (typically multispectral) image with 
 #' a higher reolution panchromatic image. Values of the pan-chromatic and multispectral images must be of the same scale, (e.g. from 0:1, or all DNs from 0:255)
 #' 
-#' @param img RasterLayer or SpatRaster. Coarse resolution multispectral image
-#' @param pan RasterLayer or SpatRaster. High resolution image, typically panchromatic.
+#' @param img SpatRaster. Coarse resolution multispectral image
+#' @param pan SpatRaster. High resolution image, typically panchromatic.
 #' @param method Character. Choose method from c("pca", "ihs", "brovey").
 #' @param r Character or Integer. Red band in \code{img}. Only relevant if \code{method!='pca'}
 #' @param g Character or Integer. Green band in \code{img}. Only relevant if \code{method!='pca'}
diff --git a/R/pifMatch.R b/R/pifMatch.R
index 1dadfc1..4f55e2f 100755
--- a/R/pifMatch.R
+++ b/R/pifMatch.R
@@ -2,8 +2,8 @@
 #' 
 #' Match one scene to another based on linear regression of pseudo-invariant features (PIF).
 #' 
-#' @param img RasterStack or RasterBrick or SpatRaster. Image to be adjusted.
-#' @param ref RasterStack or RasterBrick or SpatRaster. Reference image.
+#' @param img SpatRaster. Image to be adjusted.
+#' @param ref SpatRaster. Reference image.
 #' @param method Method to calculate pixel similarity. Options: euclidean distance ('ed'), spectral angle ('sam') or pearson correlation coefficient ('cor').
 #' @param quantile Numeric. Threshold quantile used to identify PIFs
 #' @param returnPifMap Logical. Return a binary raster map ot pixels which were identified as pesudo-invariant features.
diff --git a/R/rasterCVA.R b/R/rasterCVA.R
index 5093fe0..92c29cb 100644
--- a/R/rasterCVA.R
+++ b/R/rasterCVA.R
@@ -3,8 +3,8 @@
 #' Calculates angle and magnitude of change vectors. 
 #' Dimensionality is limited to two bands per image. 
 #' 
-#' @param x RasterBrick or RasterStack or SpatRaster with two layers. This will be the reference/origin for the change calculations. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.
-#' @param y RasterBrick or RasterStack or SpatRaster with two layers. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.
+#' @param x SpatRaster with two layers. This will be the reference/origin for the change calculations. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.
+#' @param y SpatRaster with two layers. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.
 #' @param tmf Numeric. Threshold median factor (optional). Used to calculate a threshold magnitude for which pixels are considered stable, i.e. no change. Calculated as \code{tmf * mean(magnitude[magnitude > 0])}.
 #' @param nct Numeric. No-change threshold (optional). Alternative to \code{tmf}. Sets an absolute threshold. Change magnitudes below \code{nct} are considered stable and set to NA.
 #' @param ... further arguments passed to writeRaster
diff --git a/R/rasterEntropy.R b/R/rasterEntropy.R
index bc38d25..5635aeb 100644
--- a/R/rasterEntropy.R
+++ b/R/rasterEntropy.R
@@ -5,7 +5,7 @@
 #' 
 #' Entropy is calculated as -sum(p log(p)); p being the class frequency per pixel.
 #' 
-#' @param img RasterStack or RasterBrick or SpatRaster
+#' @param img SpatRaster
 #' @param ... additional arguments passed to writeRaster
 #' @return
 #' SpatRaster "entropy"
diff --git a/R/rasterPCA.R b/R/rasterPCA.R
index 37c7e19..8102015 100644
--- a/R/rasterPCA.R
+++ b/R/rasterPCA.R
@@ -1,6 +1,6 @@
 #' Principal Component Analysis for Rasters
 #' 
-#' Calculates R-mode PCA for RasterBricks or RasterStacks and returns a RasterBrick with multiple layers of PCA scores. 
+#' Calculates R-mode PCA for SpatRasters and returns a SpatRaster with multiple layers of PCA scores.
 #' 
 #' Internally rasterPCA relies on the use of \link[stats]{princomp} (R-mode PCA). If nSamples is given the PCA will be calculated
 #' based on a random sample of pixels and then predicted for the full raster. If nSamples is NULL then the covariance matrix will be calculated
@@ -14,7 +14,7 @@
 #' Standardised PCA (SPCA) can be useful if imagery or bands of different dynamic ranges are combined. SPC uses the correlation matrix instead of the covariance matrix, which
 #' has the same effect as using normalised bands of unit variance. 
 #' 
-#' @param img RasterLayer or SpatRaster.
+#' @param img SpatRaster.
 #' @param nSamples Integer or NULL. Number of pixels to sample for PCA fitting. If NULL, all pixels will be used.
 #' @param nComp Integer. Number of PCA components to return.
 #' @param spca Logical. If \code{TRUE}, perform standardized PCA. Corresponds to centered and scaled input image. This is usually beneficial for equal weighting of all layers. (\code{FALSE} by default)
diff --git a/R/rescaleImage.R b/R/rescaleImage.R
index 8dc4ca7..c71f35f 100644
--- a/R/rescaleImage.R
+++ b/R/rescaleImage.R
@@ -8,7 +8,7 @@
 #' By providing `xmin = -1` and `xmax = 1` the values can be rescaled to any other range,
 #' e.g. 1 to 100 while comparability to other rescaled NDVI scenes is retained. 
 #' 
-#' @param x patRaster or numeric vector. Image to normalise.
+#' @param x SpatRaster or numeric vector. Image to normalise.
 #' @param y SpatRaster or numeric vector. Reference image. Optional. Used to extract min and max values if ymin or ymax are missing.
 #' @param xmin Numeric. Min value of x. Either a single value or one value per layer in x. If xmin is not provided it will be extracted from x.
 #' @param xmax Numeric. Max value of x. Either a single value or one value per layer in x. If xmax is not provided it will be extracted from x.
diff --git a/R/sam.R b/R/sam.R
index c646afe..4f180b4 100644
--- a/R/sam.R
+++ b/R/sam.R
@@ -2,7 +2,7 @@
 #' 
 #' Calculates the angle in spectral space between pixels and a set of reference spectra (endmembers) for image classification based on spectral similarity.
 #' 
-#' @param img RasterBrick or RasterStack or SpatRaster. Remote sensing imagery.
+#' @param img SpatRaster. Remote sensing imagery.
 #' @param em Matrix or data.frame with endmembers. Each row should contain the endmember spectrum of a class, i.e. columns correspond to bands in \code{img}. It is reccomended to set the rownames to class names.
 #' @param angles Logical. If \code{TRUE} a RasterBrick containing each one layer per endmember will be returned containing the spectral angles.
 #' @param ... further arguments to be passed to \code{\link[terra]{writeRaster}}
diff --git a/R/spectralIndices.R b/R/spectralIndices.R
index f20cef4..5af8970 100644
--- a/R/spectralIndices.R
+++ b/R/spectralIndices.R
@@ -2,7 +2,7 @@
 #' 
 #' Calculate a suite of multispectral indices such as NDVI, SAVI etc. in an efficient way.
 #' 
-#' @param img SpatRaster, Typically remote sensing imagery, which is to be classified.
+#' @param img SpatRaster. Typically remote sensing imagery, which is to be classified.
 #' @param blue Character or integer. Blue band. 
 #' @param green Character or integer. Green band. 
 #' @param red Character or integer. Red band. 
diff --git a/R/superClass.R b/R/superClass.R
index 025c6b2..5b1151d 100644
--- a/R/superClass.R
+++ b/R/superClass.R
@@ -70,7 +70,7 @@
 #' library(randomForest)
 #' library(e1071)
 #' library(terra)
-#' train <- readRDS(system.file("external/trainingPoints.rds", package="RStoolbox"))
+#' train <- readRDS(system.file("external/trainingPoints_lsat.rds", package="RStoolbox"))
 #' 
 #' ## Plot training data
 #' olpar <- par(no.readonly = TRUE) # back-up par
@@ -498,7 +498,7 @@ superClass <- function(img, trainData, valData = NULL, responseCol = NULL,
 #' @export 
 #' @examples 
 #' ## Load training data
-#' train <- readRDS(system.file("external/trainingPoints.rds", package="RStoolbox"))
+#' train <- readRDS(system.file("external/trainingPoints_lsat.rds", package="RStoolbox"))
 #' 
 #' ## Fit classifier 
 #' SC       <- superClass(rlogo, trainData = train, responseCol = "class",
@@ -527,7 +527,6 @@ predict.superClass <- function(object, img, predType = "raw", filename = NULL, d
     
     wrArgs          <- c(list(...), list(filename = filename, datatype = datatype))
     wrArgs$filename <- filename ## remove filename from args if is.null(filename) --> standard writeRaster handling applies
-    print(img)
     .paraRasterFun(img, rasterFun=terra::predict, args = list(model=model, type = predType, index = probInd, na.rm = TRUE), wrArgs = wrArgs)
     
 }
diff --git a/R/tasseledCap.R b/R/tasseledCap.R
index d0c241a..43588d0 100644
--- a/R/tasseledCap.R
+++ b/R/tasseledCap.R
@@ -3,7 +3,7 @@
 #' Calculates brightness, greenness and wetness from multispectral imagery.
 #' Currently implemented Landsat 4 TM, Landsat 5 TM, Landsat 7ETM+, Landsat 8 OLI, MODIS, QuickBird, Spot5 and RapidEye.
 #' 
-#' @param img RasterBrick or RasterStack or SpatRaster. Input image. Band order must correspond to sensor specifications (see Details and Examples)
+#' @param img SpatRaster. Input image. Band order must correspond to sensor specifications (see Details and Examples)
 #' @param sat Character. Sensor; one of: c("Landsat4TM", "Landsat5TM", "Landsat7ETM", "Landsat8OLI", "MODIS", "QuickBird", "Spot5", "RapidEye"). Case is irrelevant.
 #' @param ... Further arguments passed to writeRaster.
 #' @export 
diff --git a/R/validateMap.R b/R/validateMap.R
index 55a4e3e..8d9181e 100644
--- a/R/validateMap.R
+++ b/R/validateMap.R
@@ -3,7 +3,7 @@
 #'
 #' validate a map from a classification or regression model. This can be useful to update the accuracy assessment after filtering, e.g. for a minimum mapping unit.
 #'  
-#' @param map RasterLayer or SpatRaster. The classified map.
+#' @param map SpatRaster. The classified map.
 #' @param valData sf object with validation data (POLYGONs or POINTs).
 #' @param nSamplesV Integer. Number of pixels to sample for validation (only applies to polygons).
 #' @param responseCol Character. Column containing the validation data in attribute table of \code{valData}.
@@ -17,7 +17,7 @@
 #' library(terra)
 #' 
 #' ## Training data
-#' poly     <- readRDS(system.file("external/trainingPolygons.rds", package="RStoolbox"))
+#' poly     <- readRDS(system.file("external/trainingPolygons_lsat.rds", package="RStoolbox"))
 #' 
 #' ## Split training data in training and validation set (50%-50%)
 #' splitIn   <- createDataPartition(poly$class, p = .5)[[1]]
diff --git a/R/zzz.R b/R/zzz.R
index bccf384..41bdbd3 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -12,10 +12,16 @@
   lsat_rs <- terra::readRDS(system.file("external", "lsat.rds", package = pkgname))
   assign("lsat", lsat_rs, envir=as.environment("package:RStoolbox"))
 
+  srtml_rs <- terra::readRDS(system.file("external", "srtm_lsat.rds", package = pkgname))
+  assign("srtm", srtml_rs, envir=as.environment("package:RStoolbox"))
+
   rlogo_rs <- terra::readRDS(system.file("external", "rlogo.rds", package = pkgname))
   assign("rlogo", rlogo_rs, envir=as.environment("package:RStoolbox"))
 
-  srtm_rs <- terra::readRDS(system.file("external", "srtm.rds", package = pkgname))
-  assign("srtm", srtm_rs, envir=as.environment("package:RStoolbox"))
+  sen2_rs <- terra::readRDS(system.file("external", "sen2.rds", package = pkgname))
+  assign("sen2", sen2_rs, envir=as.environment("package:RStoolbox"))
+
+  srtms_rs <- terra::readRDS(system.file("external", "srtm_sen2.rds", package = pkgname))
+  assign("srtm_sen2", srtms_rs, envir=as.environment("package:RStoolbox"))
 
 }
diff --git a/inst/external/sen2.rds b/inst/external/sen2.rds
new file mode 100644
index 0000000..cc44bf0
Binary files /dev/null and b/inst/external/sen2.rds differ
diff --git a/inst/external/srtm.rds b/inst/external/srtm_lsat.rds
similarity index 100%
rename from inst/external/srtm.rds
rename to inst/external/srtm_lsat.rds
diff --git a/inst/external/srtm_sen2.rds b/inst/external/srtm_sen2.rds
new file mode 100644
index 0000000..740ab77
Binary files /dev/null and b/inst/external/srtm_sen2.rds differ
diff --git a/inst/external/trainingPoints.rds b/inst/external/trainingPoints_lsat.rds
similarity index 100%
rename from inst/external/trainingPoints.rds
rename to inst/external/trainingPoints_lsat.rds
diff --git a/inst/external/trainingPoints_sen2.rds b/inst/external/trainingPoints_sen2.rds
new file mode 100644
index 0000000..673e647
Binary files /dev/null and b/inst/external/trainingPoints_sen2.rds differ
diff --git a/inst/external/trainingPolygons.rds b/inst/external/trainingPolygons_lsat.rds
similarity index 100%
rename from inst/external/trainingPolygons.rds
rename to inst/external/trainingPolygons_lsat.rds
diff --git a/inst/external/trainingPolygons_sen2.rds b/inst/external/trainingPolygons_sen2.rds
new file mode 100644
index 0000000..2a70969
Binary files /dev/null and b/inst/external/trainingPolygons_sen2.rds differ
diff --git a/man/RStoolbox.Rd b/man/RStoolbox.Rd
index d050715..e855708 100755
--- a/man/RStoolbox.Rd
+++ b/man/RStoolbox.Rd
@@ -1,6 +1,6 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/RStoolbox.R
-\docType{package}
+\docType{_PACKAGE}
 \name{RStoolbox}
 \alias{RStoolbox}
 \title{RStoolbox: A Collection of Remote Sensing Tools}
diff --git a/man/classifyQA.Rd b/man/classifyQA.Rd
index 382d2e6..17e915a 100644
--- a/man/classifyQA.Rd
+++ b/man/classifyQA.Rd
@@ -14,7 +14,7 @@ classifyQA(
 )
 }
 \arguments{
-\item{img}{RasterLayer or SpatRaster. Landsat 8 OLI QA band.}
+\item{img}{SpatRaster. Landsat 8 OLI QA band.}
 
 \item{type}{Character. Classes which should be returned. One or more of c("background", "cloud", "cirrus","snow", "water").}
 
diff --git a/man/cloudMask.Rd b/man/cloudMask.Rd
index 0bcf1a9..05f3301 100755
--- a/man/cloudMask.Rd
+++ b/man/cloudMask.Rd
@@ -15,7 +15,7 @@ cloudMask(
 )
 }
 \arguments{
-\item{x}{RasterBrick or RasterStack or SpatRaster with reflectance and brightness temperature OR the mask of a previous run of \code{cloudMask} with \code{returnDiffLayer=TRUE}.}
+\item{x}{SpatRaster with reflectance and brightness temperature OR the mask of a previous run of \code{cloudMask} with \code{returnDiffLayer=TRUE}.}
 
 \item{threshold}{Numeric. cloud detection threshold. If not provided it will be guessed. Everything *below* this threshold will be considered a cloud pixel (unless it is removed by filtering afterwards).}
 
diff --git a/man/coregisterImages.Rd b/man/coregisterImages.Rd
index c26c2ba..6fd4008 100644
--- a/man/coregisterImages.Rd
+++ b/man/coregisterImages.Rd
@@ -19,9 +19,9 @@ coregisterImages(
 )
 }
 \arguments{
-\item{img}{SpatRaster or SpatRaster. Image to shift to match reference image. \code{img} and \code{ref} must have equal numbers of bands.}
+\item{img}{SpatRaster. Image to shift to match reference image. \code{img} and \code{ref} must have equal numbers of bands.}
 
-\item{ref}{SpatRaster or SpatRaster. Reference image. \code{img} and \code{ref} must have equal numbers of bands.}
+\item{ref}{SpatRaster. Reference image. \code{img} and \code{ref} must have equal numbers of bands.}
 
 \item{shift}{Numeric or matrix. If numeric, then shift is the maximal absolute radius (in pixels of \code{img} resolution) which \code{img} is shifted (\code{seq(-shift, shift, by=shiftInc)}). 
 If shift is a matrix it must have two columns (x shift and y shift), then only these shift values will be tested.}
diff --git a/man/estimateHaze.Rd b/man/estimateHaze.Rd
index eccc8cd..f56b39a 100644
--- a/man/estimateHaze.Rd
+++ b/man/estimateHaze.Rd
@@ -14,7 +14,7 @@ estimateHaze(
 )
 }
 \arguments{
-\item{x}{RasterLayer or SpatRaster or a previous result from \code{estimateHaze} with \code{returnTables = TRUE} from which to estimate haze}
+\item{x}{SpatRaster or a previous result from \code{estimateHaze} with \code{returnTables = TRUE} from which to estimate haze}
 
 \item{hazeBands}{Integer or Character. Band number or bandname from which to estimate atmospheric haze (optional if x contains only one layer)}
 
diff --git a/man/fCover.Rd b/man/fCover.Rd
index 102caf3..aea6068 100755
--- a/man/fCover.Rd
+++ b/man/fCover.Rd
@@ -22,9 +22,9 @@ fCover(
 )
 }
 \arguments{
-\item{classImage}{high resolution RasterLayer or SpatRaster containing a landcover classification, e.g. as obtained by \link{superClass}.}
+\item{classImage}{high resolution SpatRaster containing a landcover classification, e.g. as obtained by \link{superClass}.}
 
-\item{predImage}{coarse resolution RasterLayer or SpatRaster for which fractional cover will be estimated.}
+\item{predImage}{coarse resolution SpatRaster for which fractional cover will be estimated.}
 
 \item{nSamples}{Integer. Number of pixels to sample from predImage to train the regression model}
 
diff --git a/man/getValidation.Rd b/man/getValidation.Rd
index ddf807f..fd0d676 100755
--- a/man/getValidation.Rd
+++ b/man/getValidation.Rd
@@ -25,7 +25,7 @@ Extract validation results from superClass objects
 \examples{
 library(pls)
 ## Fit classifier (splitting training into 70\\% training data, 30\\% validation data)
-train <- readRDS(system.file("external/trainingPoints.rds", package="RStoolbox"))
+train <- readRDS(system.file("external/trainingPoints_lsat.rds", package="RStoolbox"))
 SC   <- superClass(rlogo, trainData = train, responseCol = "class",
                     model="pls", trainPartition = 0.7)
 ## Independent testset-validation
diff --git a/man/ggR.Rd b/man/ggR.Rd
index c49521f..5359fcc 100644
--- a/man/ggR.Rd
+++ b/man/ggR.Rd
@@ -22,7 +22,7 @@ ggR(
 )
 }
 \arguments{
-\item{img}{raster}
+\item{img}{SpatRaster}
 
 \item{layer}{Character or numeric. Layername or number. Can be more than one layer, in which case each layer is plotted in a subplot.}
 
diff --git a/man/ggRGB.Rd b/man/ggRGB.Rd
index ebb4734..b8c9e08 100644
--- a/man/ggRGB.Rd
+++ b/man/ggRGB.Rd
@@ -25,7 +25,7 @@ ggRGB(
 )
 }
 \arguments{
-\item{img}{RasterStack or RasterBrick}
+\item{img}{SpatRaster}
 
 \item{r}{Integer or character. Red layer in x. Can be set to \code{NULL}, in which case the red channel will be set to zero.}
 
diff --git a/man/histMatch.Rd b/man/histMatch.Rd
index ad32558..c30de9d 100644
--- a/man/histMatch.Rd
+++ b/man/histMatch.Rd
@@ -18,9 +18,9 @@ histMatch(
 )
 }
 \arguments{
-\item{x}{RasterLayer or SpatRaster. Source raster which is to be modified.}
+\item{x}{SpatRaster. Source raster which is to be modified.}
 
-\item{ref}{RasterLayer or SpatRaster. Reference raster, to which x will be matched.}
+\item{ref}{SpatRaster. Reference raster, to which x will be matched.}
 
 \item{xmask}{RasterLayer or SpatRaster. Mask layer for \code{x} to exclude pixels which might distort the histogram, i.e. are not present in \code{ref}. Any NA pixel in \code{xmask} will be ignored (\code{maskvalue = NA}).}
 
diff --git a/man/lsat.Rd b/man/lsat.Rd
index 1375beb..d1fae63 100644
--- a/man/lsat.Rd
+++ b/man/lsat.Rd
@@ -12,6 +12,6 @@ Subset of Landsat 5 TM Scene: LT52240631988227CUB02
 Contains all seven bands in DN format.
 }
 \examples{
-ggRGB(lsat, stretch = "lin")
+ggRGB(lsat, stretch = "sqrt")
 }
 \keyword{datasets}
diff --git a/man/mesma.Rd b/man/mesma.Rd
index 1db9b40..f7d4b83 100644
--- a/man/mesma.Rd
+++ b/man/mesma.Rd
@@ -7,7 +7,7 @@
 mesma(img, em, method = "NNLS", iterate = 400, tolerance = 1e-08, ..., verbose)
 }
 \arguments{
-\item{img}{RasterLayer or RasterBrick or SpatRaster. Remote sensing imagery (usually hyperspectral).}
+\item{img}{SpatRaster. Remote sensing imagery (usually hyperspectral).}
 
 \item{em}{Matrix or data.frame with spectral endmembers. Rows represent a single endmember of a class, columns represent the spectral bands (i.e. columns correspond to number of bands in \code{img}). Number of rows needs to be > 1.}
 
diff --git a/man/oneHotEncode.Rd b/man/oneHotEncode.Rd
index 454152a..cdf8a81 100644
--- a/man/oneHotEncode.Rd
+++ b/man/oneHotEncode.Rd
@@ -7,7 +7,7 @@
 oneHotEncode(img, classes, background = 0, foreground = 1, na.rm = FALSE, ...)
 }
 \arguments{
-\item{img}{RasterLayer or SpatRaster or integer/numeric vector containing multiple classes}
+\item{img}{SpatRaster or integer/numeric vector containing multiple classes}
 
 \item{classes}{integer: vector of classes which should be extracted}
 
diff --git a/man/panSharpen.Rd b/man/panSharpen.Rd
index 0c45486..d186f8d 100644
--- a/man/panSharpen.Rd
+++ b/man/panSharpen.Rd
@@ -7,9 +7,9 @@
 panSharpen(img, pan, r, g, b, pc = 1, method = "brovey", norm = TRUE)
 }
 \arguments{
-\item{img}{RasterLayer or SpatRaster. Coarse resolution multispectral image}
+\item{img}{SpatRaster. Coarse resolution multispectral image}
 
-\item{pan}{RasterLayer or SpatRaster. High resolution image, typically panchromatic.}
+\item{pan}{SpatRaster. High resolution image, typically panchromatic.}
 
 \item{r}{Character or Integer. Red band in \code{img}. Only relevant if \code{method!='pca'}}
 
diff --git a/man/pifMatch.Rd b/man/pifMatch.Rd
index f0a30b6..c567241 100644
--- a/man/pifMatch.Rd
+++ b/man/pifMatch.Rd
@@ -15,9 +15,9 @@ pifMatch(
 )
 }
 \arguments{
-\item{img}{RasterStack or RasterBrick or SpatRaster. Image to be adjusted.}
+\item{img}{SpatRaster. Image to be adjusted.}
 
-\item{ref}{RasterStack or RasterBrick or SpatRaster. Reference image.}
+\item{ref}{SpatRaster. Reference image.}
 
 \item{method}{Method to calculate pixel similarity. Options: euclidean distance ('ed'), spectral angle ('sam') or pearson correlation coefficient ('cor').}
 
diff --git a/man/rasterCVA.Rd b/man/rasterCVA.Rd
index d732039..54b7a97 100644
--- a/man/rasterCVA.Rd
+++ b/man/rasterCVA.Rd
@@ -7,9 +7,9 @@
 rasterCVA(x, y, tmf = NULL, nct = NULL, ...)
 }
 \arguments{
-\item{x}{RasterBrick or RasterStack or SpatRaster with two layers. This will be the reference/origin for the change calculations. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.}
+\item{x}{SpatRaster with two layers. This will be the reference/origin for the change calculations. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.}
 
-\item{y}{RasterBrick or RasterStack or SpatRaster with two layers. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.}
+\item{y}{SpatRaster with two layers. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.}
 
 \item{tmf}{Numeric. Threshold median factor (optional). Used to calculate a threshold magnitude for which pixels are considered stable, i.e. no change. Calculated as \code{tmf * mean(magnitude[magnitude > 0])}.}
 
diff --git a/man/rasterEntropy.Rd b/man/rasterEntropy.Rd
index 99c78f2..26be715 100644
--- a/man/rasterEntropy.Rd
+++ b/man/rasterEntropy.Rd
@@ -7,7 +7,7 @@
 rasterEntropy(img, ...)
 }
 \arguments{
-\item{img}{RasterStack or RasterBrick or SpatRaster}
+\item{img}{SpatRaster}
 
 \item{...}{additional arguments passed to writeRaster}
 }
diff --git a/man/rasterPCA.Rd b/man/rasterPCA.Rd
index 047db48..ed3d99a 100644
--- a/man/rasterPCA.Rd
+++ b/man/rasterPCA.Rd
@@ -14,7 +14,7 @@ rasterPCA(
 )
 }
 \arguments{
-\item{img}{RasterLayer or SpatRaster.}
+\item{img}{SpatRaster.}
 
 \item{nSamples}{Integer or NULL. Number of pixels to sample for PCA fitting. If NULL, all pixels will be used.}
 
@@ -31,7 +31,7 @@ Takes effect only if nSamples is NULL.}
 Returns a named list containing the PCA model object ($model) and a SpatRaster with the principal component layers ($object).
 }
 \description{
-Calculates R-mode PCA for RasterBricks or RasterStacks and returns a RasterBrick with multiple layers of PCA scores.
+Calculates R-mode PCA for SpatRasters and returns a SpatRaster with multiple layers of PCA scores.
 }
 \details{
 Internally rasterPCA relies on the use of \link[stats]{princomp} (R-mode PCA). If nSamples is given the PCA will be calculated
diff --git a/man/rescaleImage.Rd b/man/rescaleImage.Rd
index ed70a1b..66717f8 100644
--- a/man/rescaleImage.Rd
+++ b/man/rescaleImage.Rd
@@ -7,7 +7,7 @@
 rescaleImage(x, y, xmin, xmax, ymin, ymax, forceMinMax = FALSE, ...)
 }
 \arguments{
-\item{x}{patRaster or numeric vector. Image to normalise.}
+\item{x}{SpatRaster or numeric vector. Image to normalise.}
 
 \item{y}{SpatRaster or numeric vector. Reference image. Optional. Used to extract min and max values if ymin or ymax are missing.}
 
diff --git a/man/sam.Rd b/man/sam.Rd
index 87e114c..e80ea13 100644
--- a/man/sam.Rd
+++ b/man/sam.Rd
@@ -7,7 +7,7 @@
 sam(img, em, angles = FALSE, ...)
 }
 \arguments{
-\item{img}{RasterBrick or RasterStack or SpatRaster. Remote sensing imagery.}
+\item{img}{SpatRaster. Remote sensing imagery.}
 
 \item{em}{Matrix or data.frame with endmembers. Each row should contain the endmember spectrum of a class, i.e. columns correspond to bands in \code{img}. It is reccomended to set the rownames to class names.}
 
diff --git a/man/spectralIndices.Rd b/man/spectralIndices.Rd
index 8a1ce61..33cec27 100755
--- a/man/spectralIndices.Rd
+++ b/man/spectralIndices.Rd
@@ -28,7 +28,7 @@ spectralIndices(
 )
 }
 \arguments{
-\item{img}{SpatRaster, Typically remote sensing imagery, which is to be classified.}
+\item{img}{SpatRaster. Typically remote sensing imagery, which is to be classified.}
 
 \item{blue}{Character or integer. Blue band.}
 
diff --git a/man/superClass.Rd b/man/superClass.Rd
index bcb6201..c906079 100755
--- a/man/superClass.Rd
+++ b/man/superClass.Rd
@@ -117,7 +117,7 @@ library(caret)
 library(randomForest)
 library(e1071)
 library(terra)
-train <- readRDS(system.file("external/trainingPoints.rds", package="RStoolbox"))
+train <- readRDS(system.file("external/trainingPoints_lsat.rds", package="RStoolbox"))
 
 ## Plot training data
 olpar <- par(no.readonly = TRUE) # back-up par
diff --git a/man/tasseledCap.Rd b/man/tasseledCap.Rd
index f27ff74..1997671 100644
--- a/man/tasseledCap.Rd
+++ b/man/tasseledCap.Rd
@@ -7,7 +7,7 @@
 tasseledCap(img, sat, ...)
 }
 \arguments{
-\item{img}{RasterBrick or RasterStack or SpatRaster. Input image. Band order must correspond to sensor specifications (see Details and Examples)}
+\item{img}{SpatRaster. Input image. Band order must correspond to sensor specifications (see Details and Examples)}
 
 \item{sat}{Character. Sensor; one of: c("Landsat4TM", "Landsat5TM", "Landsat7ETM", "Landsat8OLI", "MODIS", "QuickBird", "Spot5", "RapidEye"). Case is irrelevant.}
 
diff --git a/man/validateMap.Rd b/man/validateMap.Rd
index e14c7b0..5d1a527 100644
--- a/man/validateMap.Rd
+++ b/man/validateMap.Rd
@@ -14,7 +14,7 @@ validateMap(
 )
 }
 \arguments{
-\item{map}{RasterLayer or SpatRaster. The classified map.}
+\item{map}{SpatRaster. The classified map.}
 
 \item{valData}{sf object with validation data (POLYGONs or POINTs).}
 
@@ -37,7 +37,7 @@ library(caret)
 library(terra)
 
 ## Training data
-poly     <- readRDS(system.file("external/trainingPolygons.rds", package="RStoolbox"))
+poly     <- readRDS(system.file("external/trainingPolygons_lsat.rds", package="RStoolbox"))
 
 ## Split training data in training and validation set (50\%-50\%)
 splitIn   <- createDataPartition(poly$class, p = .5)[[1]]
diff --git a/prep-release/makeRelease.sh b/prep-release/makeRelease.sh
index dc8ab4c..d1eae84 100755
--- a/prep-release/makeRelease.sh
+++ b/prep-release/makeRelease.sh
@@ -32,7 +32,7 @@ Rscript -e "library(devtools); library(methods); document(); install()"
 
 
 ## Re-build example data
-tmstr=$(git log -1 --format=%ct data/ inst/external/trainingPoints.rds inst/external/landsat/)
+tmstr=$(git log -1 --format=%ct data/ inst/external/trainingPoints_lsat.rds inst/external/landsat/)
 texmpl=$(git log example-data -1  --format=%ct)
 if [ $(( tmstr > texmpl )) -eq 0 ]
 then
@@ -45,7 +45,7 @@ then
 
     ## Back to master
     git checkout master
-    git checkout example-data data/rlogo.rda data/srtm.rda  data/lsat.rda inst/external/landsat inst/external/trainingPolygons.rds
+    git checkout example-data data/rlogo.rda data/srtm.rda  data/lsat.rda inst/external/landsat inst/external/trainingPolygons_lsat.rds
     Rscript -e "library(devtools); library(methods); document()"
     git commit -a -m "Automatic commit: Pull example data from branch example-data"
 else 
diff --git a/tests/testthat/test-getValidation.R b/tests/testthat/test-getValidation.R
index 74be44e..163d8de 100755
--- a/tests/testthat/test-getValidation.R
+++ b/tests/testthat/test-getValidation.R
@@ -1,7 +1,7 @@
 context("getValidation")
 suppressPackageStartupMessages(library("randomForest"))
 
-train <- readRDS(system.file("external/trainingPoints.rds", package="RStoolbox"))
+train <- readRDS(system.file("external/trainingPoints_lsat.rds", package="RStoolbox"))
 train$num <- rnorm(nrow(train))
 class   <- superClass(rlogo, trainData = train, responseCol = "class", tuneLength = 1, trainPartition = 0.7, predict = FALSE)
 reg     <- superClass(rlogo, trainData = train, responseCol = "num", tuneLength = 1, trainPartition = 0.7, predict = FALSE, mode = "regression")
diff --git a/tests/testthat/test-ggplot.R b/tests/testthat/test-ggplot.R
index 8737fa1..8716fc6 100644
--- a/tests/testthat/test-ggplot.R
+++ b/tests/testthat/test-ggplot.R
@@ -4,7 +4,7 @@ library(terra)
 test_that("ggR returns proper ggplot2 classes or data.frames", {
   suppressWarnings({
     tests  <- expand.grid(forceCat = c(TRUE, FALSE), anno = c(TRUE, FALSE), ggLayer = c(TRUE, FALSE), ggObj = c(TRUE,FALSE))
-    builds <- lapply(1:nrow(tests), function(i) ggR(rlogo, forceCat = tests$forceCat[i], ggObj = tests$ggObj[i], geom_raster = !tests$anno[i], ggLayer = tests$ggLayer[i]))
+    builds <- lapply(seq_len(nrow(tests)), function(i) ggR(rlogo, forceCat = tests$forceCat[i], ggObj = tests$ggObj[i], geom_raster = !tests$anno[i], ggLayer = tests$ggLayer[i]))
     tinfo  <- paste0("forceCat=", tests[,1], ", anno=", tests[,2], ", ggLayer=", tests[,3], ", ggObj=", tests[,4])
 
     ## Annotation vs geom_raster
@@ -13,7 +13,7 @@ test_that("ggR returns proper ggplot2 classes or data.frames", {
     ## ggLayers
     if(!inherits(builds[[which(with(tests, ggObj & ggLayer))[1]]], "ggproto")){
       ## Current ggplot2 release version
-      for(s in which(with(tests, ggObj & ggLayer))) expect_is(builds[[s]], c("proto"), info = tinfo[s])
+      for(s in which(with(tests, ggObj & ggLayer))) expect_is(builds[[s]], "proto", info = tinfo[s])
       for(s in which(with(tests, ggObj & ggLayer & anno)))  expect_equal(builds[[s]]$geom$objname, "raster_ann", info = tinfo[s])
       for(s in which(with(tests, ggObj & ggLayer & !anno))) expect_equal(builds[[s]]$geom$objname, "raster", info = tinfo[s])
     } else {
@@ -60,7 +60,7 @@ test_that("ggR works with single valued rasters", {
 test_that("ggRGB returns proper ggplot2 classes or data.frames", {
 
   tests  <- expand.grid(anno = c(TRUE, FALSE), ggLayer = c(TRUE, FALSE), ggObj = c(TRUE,FALSE), stretch=c("sqrt", "hist", "log", "lin"))
-  builds <- lapply(1:nrow(tests), function(i) ggRGB(rlogo, ggObj = tests$ggObj[i], geom_raster = !tests$anno[i], ggLayer = tests$ggLayer[i], stretch = tests$stretch[i] ))
+  builds <- lapply(seq_len(nrow(tests)), function(i) ggRGB(rlogo, ggObj = tests$ggObj[i], geom_raster = !tests$anno[i], ggLayer = tests$ggLayer[i], stretch = tests$stretch[i] ))
   tinfo <- paste0("anno=", tests$anno, ", ggLayer=", tests$ggLayer, ", ggObj=", tests$ggObj)
 
   ## Stand-alone
@@ -69,7 +69,7 @@ test_that("ggRGB returns proper ggplot2 classes or data.frames", {
   ## ggLayers
   if(!inherits(builds[[which(with(tests, ggObj & ggLayer))[1]]], "ggproto")){
     ## Current ggplot2 release version
-    for(s in which(with(tests, ggObj & ggLayer))) expect_is(builds[[s]], c("proto"), info = tinfo[s])
+    for(s in which(with(tests, ggObj & ggLayer))) expect_is(builds[[s]], "proto", info = tinfo[s])
     for(s in which(with(tests, ggObj & ggLayer & anno)))  expect_equal(builds[[s]]$geom$objname, "raster_ann", info = tinfo[s])
     for(s in which(with(tests, ggObj & ggLayer & !anno))) expect_equal(builds[[s]]$geom$objname, "raster", info = tinfo[s])
   } else {
diff --git a/tests/testthat/test-internalFunctions.R b/tests/testthat/test-internalFunctions.R
index 2b0e90b..59d1273 100644
--- a/tests/testthat/test-internalFunctions.R
+++ b/tests/testthat/test-internalFunctions.R
@@ -2,7 +2,7 @@ context("internal functions")
 
 terra <- rast(ncol = 5, nrow=5, vals = 1)
 
-sf <- readRDS(system.file("external/trainingPolygons.rds", package="RStoolbox"))
+sf <- readRDS(system.file("external/trainingPolygons_lsat.rds", package="RStoolbox"))
 
 test_that("Loaded as sf", {
     expect_is(sf, c("sf", "data.frame"))
diff --git a/tests/testthat/test-saveReadRSTBX.R b/tests/testthat/test-saveReadRSTBX.R
index 5e7d399..82b4668 100644
--- a/tests/testthat/test-saveReadRSTBX.R
+++ b/tests/testthat/test-saveReadRSTBX.R
@@ -2,7 +2,7 @@ context("save and read RSTBX objects")
 library(terra)
 
 ## Create RSTBX object
-train <- readRDS(system.file("external/trainingPoints.rds", package="RStoolbox"))
+train <- readRDS(system.file("external/trainingPoints_lsat.rds", package="RStoolbox"))
 sc <- superClass(rlogo, train, tuneLength = 1, resp="class")
 
 ## Save and re-import
diff --git a/tests/testthat/test-superClass.R b/tests/testthat/test-superClass.R
index e6a490c..092031e 100644
--- a/tests/testthat/test-superClass.R
+++ b/tests/testthat/test-superClass.R
@@ -9,7 +9,7 @@ lsat_t <- lsat
 lsat_t <- lsat_t[[1:4]]
 ## Set-up test data
 set.seed(1)
-poly     <- readRDS(system.file("external/trainingPolygons.rds", package="RStoolbox"))
+poly     <- readRDS(system.file("external/trainingPolygons_lsat.rds", package="RStoolbox"))
 poly$res <- as.numeric(poly$class)
 poly <- st_as_sf(poly)
 pts <- st_join(st_as_sf(st_sample(poly, 100, type = "regular")), poly)
diff --git a/tests/testthat/test-validateMap.R b/tests/testthat/test-validateMap.R
index 306f71f..1418494 100644
--- a/tests/testthat/test-validateMap.R
+++ b/tests/testthat/test-validateMap.R
@@ -4,7 +4,7 @@ lsat_t <- lsat_t[[1:4]]
 
 ## Set-up test data
 set.seed(1)
-poly     <- readRDS(system.file("external/trainingPolygons.rds", package="RStoolbox"))
+poly     <- readRDS(system.file("external/trainingPolygons_lsat.rds", package="RStoolbox"))
 poly$classNum <- as.numeric(poly$class)
 
 sc <- superClass(lsat_t, trainData = poly, nSamples = 50, responseCol = "class", model = "mlc", trainPartition = 0.7, predict = TRUE)