diff --git a/unit_02/05_c-and-l-band-comparison.ipynb b/unit_02/05_c-and-l-band-comparison.ipynb
index 2e51bb0..5531458 100644
--- a/unit_02/05_c-and-l-band-comparison.ipynb
+++ b/unit_02/05_c-and-l-band-comparison.ipynb
@@ -11,21 +11,558 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rioxarray\n",
+    "from pathlib import Path\n",
+    "import xarray as xr\n",
+    "import dask\n",
+    "import datetime as dt\n",
+    "import matplotlib.pyplot as plt\n",
+    "import holoviews as hv\n",
+    "import hvplot.xarray\n",
+    "import numpy as np\n",
+    "import glob\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s1_path = Path('/home/ljovic/Desktop/Material_for_inclass_notebooks_24/S1')\n",
+    "alos_path = Path('/home/ljovic/Desktop/Material_for_inclass_notebooks_24/ALOS')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_path = \"/home/ljovic/Desktop/Material_for_inclass_notebooks_24/S1/D20220719_052728--_GMR------_S1AIWGRDH1VVD_168_A0105_EU010M_E047N013T1.tif\"\n",
+    "\n",
+    "ds = xr.open_dataset(file_path, engine='rasterio').squeeze()\n",
+    "ds\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "downsampled_data = ds.coarsen(x=10, y=10, boundary='trim').mean()\n",
+    "\n",
+    "#downsampled_data = downsampled_data.isel(band=0)\n",
+    "\n",
+    "downsampled_data['band_data'].plot(figsize=(10, 8), robust=True)\n",
+    "plt.title(\"Downsampled Data\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(s1_path.glob('*.tif'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def s1_preprocess(x):\n",
+    "    \n",
+    "    filename = Path(x.encoding[\"source\"]).name\n",
+    "\n",
+    "    date_str = filename.split('_')[0][1:]\n",
+    "    time_str = filename.split('_')[1][:6]\n",
+    "    datetime_str = date_str + time_str\n",
+    "    \n",
+    "    date = pd.to_datetime(datetime_str, format='%Y%m%d%H%M%S')\n",
+    "    x = x.expand_dims(dim={'time': [date]})\n",
+    "\n",
+    "    band_name = filename.split(\"_\")[3][10:12]\n",
+    "\n",
+    "    return x.rename({\"band_data\": band_name}).squeeze(\"band\")#.drop_vars(\"band\")\n",
+    "\n",
+    "s1_ds = xr.open_mfdataset(\n",
+    "    f\"{s1_path}/*.tif\", \n",
+    "    engine=\"rasterio\",\n",
+    "    concat_dim=\"time\",\n",
+    "    combine='nested',\n",
+    "    preprocess=s1_preprocess\n",
+    "    )\n",
+    "\n",
+    "s1_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def alos_preprocess(x):\n",
+    "    filename = Path(x.encoding[\"source\"]).name\n",
+    "    \n",
+    "    scale = 100\n",
+    "    date_str = filename.split('_')[0][15:22]\n",
+    "    date = pd.to_datetime(date_str, format='%y%m%d')\n",
+    "    \n",
+    "    x = x.assign_coords({\"time\": date}).expand_dims(\"time\")\n",
+    "\n",
+    "    if \"HV\" in filename:\n",
+    "        x = x.rename({\"band_data\": \"HV\"})\n",
+    "\n",
+    "    elif \"HH\" in filename:\n",
+    "        x = x.rename({\"band_data\": \"HH\"})\n",
+    "    \n",
+    "    x = x.squeeze('band').chunk(chunks={'x': 3000, 'y': 3000})\n",
+    "\n",
+    "    #if \"band_data\" in x.variables:\n",
+    "    #    x = x.drop_vars(\"band_data\")\n",
+    "    \n",
+    "    return x * 1/scale # check for other notebooks!!!!!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alos_path = '/home/ljovic/Desktop/Material_for_inclass_notebooks_24/ALOS'\n",
+    "\n",
+    "alos_ds = xr.open_mfdataset(\n",
+    "    #file_list,\n",
+    "    f\"{alos_path}/**/*SLP.tif\", \n",
+    "    engine='rasterio', \n",
+    "    concat_dim='time',\n",
+    "    combine='nested',\n",
+    "    preprocess=alos_preprocess,\n",
+    "    \n",
+    ")\n",
+    "\n",
+    "alos_ds = alos_ds.groupby('time').mean(skipna=True)\n",
+    "alos_ds\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ds = ds.dropna(dim='time')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alos_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = alos_ds.isel(time=1).HH.coarsen(x=10, y=10, boundary='trim').mean()\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "downsampled_data = alos_ds.HH.coarsen(x=10, y=10, boundary='trim').mean()\n",
+    "\n",
+    "downsampled_data.plot(col='time', col_wrap=4, robust=True, figsize=(16, 8))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alos_ds.isel(x=5000, y=5000).HV.plot(marker=\"o\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alos_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alos_ds_reprojected = alos_ds.rio.reproject_match(s1_ds)\n",
+    "alos_ds_reprojected"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#alos_ds_reprojected.HH.isel(time=1).plot(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alos_ds_reprojected.HH.isel(time=1).plot.imshow(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s1_ds.VV.isel(time=1).plot.imshow(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(len(ds['time'])):\n",
+    "    \n",
+    "    plt.figure(figsize=(10, 8))\n",
+    "    \n",
+    "    x = ds.isel(time=i).HH # HH or HV\n",
+    "    downsampled_data = x.coarsen(x=10, y=10, boundary='trim').mean()\n",
+    "    downsampled_data.plot(robust=True)\n",
+    "    \n",
+    "    plt.title(ds['time'].values[i])\n",
+    "    plt.xlabel('X Coordinate')\n",
+    "    plt.ylabel('Y Coordinate')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load(regular_expression, base_path):\n",
+    "\n",
+    "    paths = [file for file in base_path.glob(regular_expression)]\n",
+    "    array_list = []\n",
+    "\n",
+    "    for file in paths:\n",
+    "        \n",
+    "        filename = file.name\n",
+    "\n",
+    "        if \"ALOS\" in str(file.parents[0]):\n",
+    "            time_str = filename.split(\"_\")[0]\n",
+    "            time_str = time_str.split(\"-\")[1]\n",
+    "            time_str = \"20\" + time_str\n",
+    "\n",
+    "        elif \"S1\" in str(file.parents[0]):\n",
+    "            time_str = filename[1:9]\n",
+    "        \n",
+    "        date = dt.datetime.strptime(time_str,'%Y%m%d')\n",
+    "        \n",
+    "        data = rioxarray.open_rasterio(file, chunks=True)\n",
+    "        data = data.squeeze().expand_dims(dim={'time':[date.date()]})\n",
+    "        data.name = \"HH\"\n",
+    "\n",
+    "        array_list.append(data)\n",
+    "    \n",
+    "    return xr.concat(array_list, dim=\"time\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sentinel-1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s1 = load('*.tif', s1_path)\n",
+    "s1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for time_index, time_value in enumerate(s1['time'].values):\n",
+    "    data = s1.sel(time=time_value)\n",
+    "\n",
+    "    downsampled_data = data.coarsen(x=5, y=5, boundary='trim').mean()\n",
+    "    no_data_value = downsampled_data.attrs.get('_FillValue')\n",
+    "    downsampled_data = downsampled_data.where(downsampled_data != no_data_value)\n",
+    "\n",
+    "    downsampled_data.plot(figsize=(10, 8), robust=True)\n",
+    "    plt.title(f'HV: {time_value}')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "downsampled_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ALOS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alos_HH = load('**/*HH_SLP.tif', alos_path)\n",
+    "alos_HV = load('**/*HV_SLP.tif', alos_path)\n",
+    "\n",
+    "alos_data = {'HH': alos_HH,\n",
+    "        'HV': alos_HV}\n",
+    "\n",
+    "alos_ds = xr.Dataset(alos_data)\n",
+    "alos_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for time_index, time_value in enumerate(alos_ds['time'].values):\n",
+    "    data = alos_ds['HV'].sel(time=time_value)\n",
+    "\n",
+    "    downsampled_data = data.coarsen(x=5, y=5, boundary='trim').mean()\n",
+    "    downsampled_data = downsampled_data.where(downsampled_data != 0)\n",
+    "\n",
+    "    downsampled_data.plot(figsize=(10, 8), robust=True)\n",
+    "    plt.title(f'HV: {time_value}')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hv.extension('bokeh')\n",
+    "vmin, vmax = np.nanpercentile(downsampled_data.values, [2, 98])\n",
+    "\n",
+    "interactive_plot = downsampled_data.hvplot.image(\n",
+    "    x='x', \n",
+    "    y='y', \n",
+    "    cmap='viridis',\n",
+    "    clim=(vmin, vmax), \n",
+    "    width=800, \n",
+    "    height=600, \n",
+    "    title=f'HV: {time_value}'\n",
+    ").opts(tools=['hover', 'tap'])\n",
+    "\n",
+    "hv.output(interactive_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "interactive_plot = downsampled_data.hvplot.image(\n",
+    "    x='x', \n",
+    "    y='y', \n",
+    "    cmap='viridis',\n",
+    "    clim=(vmin, vmax), \n",
+    "    width=800, \n",
+    "    height=600, \n",
+    "    title=f'HV: {time_value}'\n",
+    ").opts(tools=['hover', 'tap'])\n",
+    "\n",
+    "hv.output(interactive_plot)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_index = 1500\n",
+    "y_index = 1500\n",
+    "\n",
+    "pixel_values = []\n",
+    "time_values = []\n",
+    "\n",
+    "for time_value in alos_ds['time'].values:\n",
+    "    data = alos_ds['HV'].sel(time=time_value)\n",
+    "\n",
+    "    downsampled_data = data.coarsen(x=5, y=5, boundary='trim').mean()\n",
+    "    downsampled_data = downsampled_data.where(downsampled_data != 0)\n",
+    "\n",
+    "    if downsampled_data.shape[0] > x_index and downsampled_data.shape[1] > y_index:\n",
+    "        pixel_value = downsampled_data.isel(x=x_index, y=y_index).values\n",
+    "        pixel_values.append(pixel_value)\n",
+    "        time_values.append(time_value)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(time_values, pixel_values, marker='o', linestyle='-')\n",
+    "plt.title(f'Time Series for Pixel at X:{x_index}, Y:{y_index}')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Pixel Value')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_time_value = alos_ds['time'].values[-1]\n",
+    "data = alos_ds['HV'].sel(time=last_time_value)\n",
+    "\n",
+    "downsampled_data = data.coarsen(x=5, y=5, boundary='trim').mean()\n",
+    "downsampled_data = downsampled_data.where(downsampled_data != 0)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "im = downsampled_data.plot(ax=ax, cmap='gray', robust=True, add_colorbar=True)\n",
+    "\n",
+    "if downsampled_data.sizes['x'] > x_index and downsampled_data.sizes['y'] > y_index:\n",
+    "\n",
+    "    ax.plot(x_index + downsampled_data.x.min().item(),\n",
+    "            y_index + downsampled_data.y.min().item(),\n",
+    "            'ro', markersize=5)\n",
+    "\n",
+    "plt.title(f'HV: {last_time_value}')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "downsampled_data.x.min().item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import rioxarray\n",
+    "from pyproj import CRS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the WGS84 projection using its EPSG code\n",
+    "wgs84_crs = CRS.from_string(\"EPSG:4326\")\n",
+    "\n",
+    "# Assuming alos_HH and alos_HV are already loaded as xarray.DataArray\n",
+    "alos_HH_reprojected = alos_HH.rio.reproject(wgs84_crs)\n",
+    "alos_HV_reprojected = alos_HV.rio.reproject(wgs84_crs)\n",
+    "\n",
+    "# Create a new dataset with the reprojected data\n",
+    "alos_data_reprojected = {\n",
+    "    'HH': alos_HH_reprojected,\n",
+    "    'HV': alos_HV_reprojected\n",
+    "}\n",
+    "\n",
+    "alos_ds_reprojected = xr.Dataset(alos_data_reprojected)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alos_ds_reprojected\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for time_index, time_value in enumerate(alos_ds_reprojected['time'].values):\n",
+    "    data = alos_ds['HV'].sel(time=time_value)\n",
+    "    \n",
+    "    downsampled_data = data.coarsen(x=5, y=5, boundary='trim').mean()\n",
+    "    downsampled_data = downsampled_data.where(downsampled_data != 0)\n",
+    "    \n",
+    "    downsampled_data.plot(figsize=(10, 8), robust=True)\n",
+    "    plt.title(f'HV: {time_value}')\n",
+    "    plt.show()\n"
+   ]
   }
  ],
  "metadata": {
   "dependency_resolution_engine": "pipenv",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "05_c-and-l-band-comparison",
    "language": "python",
-   "name": "python3"
+   "name": "05_c-and-l-band-comparison"
   },
   "language_info": {
    "codemirror_mode": {
@@ -37,7 +574,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.14"
   },
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diff --git a/unit_02/05_c-and-l-band-comparison.yml b/unit_02/05_c-and-l-band-comparison.yml
index 4c3b58c..de9ce7e 100644
--- a/unit_02/05_c-and-l-band-comparison.yml
+++ b/unit_02/05_c-and-l-band-comparison.yml
@@ -6,3 +6,4 @@ dependencies:
   - pip
   - mamba
   - jupyter
+  - eomaps