diff --git a/data/downloads/odp-ftp-soil-moisture.sh b/data/downloads/odp-ftp-soil-moisture.sh
index cd0cf3869..0564b97e0 100755
--- a/data/downloads/odp-ftp-soil-moisture.sh
+++ b/data/downloads/odp-ftp-soil-moisture.sh
@@ -1,20 +1,12 @@
 #!/usr/bin/env bash
 
-BASE_URL="ftp://anon-ftp.ceda.ac.uk/neodc/esacci/soil_moisture/data/daily_files/COMBINED/v04.5"
-START_DATE=1978-11-01
-OUTPUT_FODLER=./download/soil
+BASE_URL="https://owncloud.tuwien.ac.at/index.php/s/bY8j2kBgZlkqFYC/download?path=%2F&files=ESACCI-SOILMOISTURE-L3S-SSMV-MONTHLY_MEAN-COMBINED-19781101-20191231-fv04.7.nc"
+OUTPUT_FODLER=./download/soil_moisture
 
 mkdir -p $OUTPUT_FODLER
 
-for i in {0..481}
-do
-  NEXT_YEAR=$(date +%Y -d "$START_DATE + $i month")
-  NEXT_MONTH=$(date +%Y%m%d -d "$START_DATE + $i month")
-  FILENAME=$OUTPUT_FODLER/$(date +%Y%m%d -d "$START_DATE + $i month").nc
-  FTP_URL="$BASE_URL/$NEXT_YEAR/ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-$NEXT_MONTH"000000-fv04.5.nc
-  echo $FTP_URL
+FILENAME=$OUTPUT_FODLER/sm.nc
+curl --silent $BASE_URL > $FILENAME
 
-  curl --silent $FTP_URL > $FILENAME
+python ./data/split-time-dim.py --file $FILENAME --variable sm_mean
 
-  python ./data/drop-unused-vars.py --file $FILENAME --variable sm
-done
diff --git a/data/gdal-colors/colors-sm.txt b/data/gdal-colors/colors-sm.txt
deleted file mode 100644
index d9efd8df3..000000000
--- a/data/gdal-colors/colors-sm.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-0.4 12 61 153
-0.2 133 200 250
-0.1 240 240 140
-0.001 146 78 7
-0 0 0 0 0
-nv 0 0 0 0
diff --git a/data/split-time-dim.py b/data/split-time-dim.py
new file mode 100644
index 000000000..84228ae29
--- /dev/null
+++ b/data/split-time-dim.py
@@ -0,0 +1,20 @@
+import os
+import xarray as xr
+import pandas as pd
+from datetime import datetime
+from argparse import ArgumentParser
+
+parser = ArgumentParser()
+parser.add_argument("-f", "--file", dest="file")
+parser.add_argument("-v", "--variable", dest="variable")
+args = parser.parse_args()
+
+ds = xr.open_dataset(args.file, decode_coords=True, decode_cf=True)
+da = ds[args.variable]
+
+for n in range(len(da.time)):
+  da_slice = da.isel(time=n)
+  date = pd.to_datetime(str(da_slice.time.values))
+  filename = date.strftime('./download/soil_moisture/%Y%m%d.nc')
+  print(filename)
+  da_slice.to_dataset().to_netcdf(filename, format='NETCDF4', mode='w')