diff --git a/analysis/spatial_upscaling_classification.R b/analysis/spatial_upscaling_classification.R
new file mode 100644
index 0000000..ece27fa
--- /dev/null
+++ b/analysis/spatial_upscaling_classification.R
@@ -0,0 +1,85 @@
+# Basic xgboost model with limited
+# hyperparameter tuning
+
+# load the ecosystem
+library(tidymodels)
+library(ranger)
+library(dplyr)
+library(terra)
+library(ggplot2)
+library(rnaturalearth)
+library(tidyterra)
+set.seed(0)
+
+# read in precompiled model
+model <- readRDS(
+  here::here("data/classification_model.rds")
+)
+
+files <- data.frame(
+  file = list.files("data-raw/modis_data_spatial/","*.tif", full.names = TRUE)
+)
+
+doys <- 180:300
+
+lapply(doys, function(doy){
+
+  files <- files |>
+    dplyr::filter(
+      grepl(sprintf("doy2018%s",as.character(doy)),file)
+    )
+
+  r <- terra::rast(files$file)
+
+  # the model only works when variable names
+  # are consistent we therefore rename them
+  band_names <- data.frame(
+    name = names(r)
+  ) |>
+    mutate(
+      date = as.Date(substr(name, 40, 46), format = "%Y%j"),
+      name = substr(name, 13, 35)
+    )
+
+  # reassign the names of the terra image stack
+  names(r) <- band_names$name
+
+  # return probabilities, where each class is
+  # associated with a layer in an image stack
+  # and the probabilities reflect the probabilities
+  # of the classification for said layer
+  p <- terra::predict(
+    r,
+    model
+  )
+
+  # grab country polygons from world map
+  # restrict to selected country
+  country <- ne_countries(
+    scale = 50,
+    returnclass = "sf"
+  ) |>
+    dplyr::filter(
+      sovereignt %in%  c("Switzerland","Germany","Austria")
+    ) |>
+    sf::st_union() |>
+    sf::st_as_sf()
+
+  p <- ggplot() +
+    geom_spatraster(
+      data = p
+    ) +
+    scale_fill_viridis_c(
+      limits = c(0, 1.5)
+    ) +
+    geom_sf(data = country, colour = "white", fill = NA) +
+    labs(
+      title = doy
+    )
+
+  ggsave(
+    filename = sprintf("manuscript/classification/%s.png", as.character(doy)),
+    p, width = 5,
+    height = 5
+    )
+})
diff --git a/analysis/spatial_upsacling.R b/analysis/spatial_upscaling_regression.R
similarity index 90%
rename from analysis/spatial_upsacling.R
rename to analysis/spatial_upscaling_regression.R
index e19a23a..01f6f1d 100644
--- a/analysis/spatial_upsacling.R
+++ b/analysis/spatial_upscaling_regression.R
@@ -69,11 +69,13 @@ lapply(doys, function(doy){
     geom_spatraster(
       data = p
     ) +
-    scale_fill_viridis_c() +
+    scale_fill_viridis_c(
+      limits = c(0, 1.5)
+    ) +
     geom_sf(data = country, colour = "white", fill = NA) +
     labs(
       title = doy
     )
 
-  ggsave(filename = sprintf("manuscript/%s.png", as.character(doy)), p, width = 5, height = 5)
+  ggsave(filename = sprintf("manuscript/regression/%s.png", as.character(doy)), p, width = 5, height = 5)
 })