update.

code/python_remote_sensing/time_series_processing_xee.ipynb

@@ -235,7 +235,9 @@
       "source": [
         "### Aggregating the Time-Series\n",
         "\n",
-        "We have a irregular time-series with a lot of noise. Let's create a monthly NDVI time-series by computing the monthly average NDVI. We can use XArray's `resample()` method to aggregate the time-series by Months and coompute the mean. We specify `MS` to indicate aggregating by month start."
+        "We have a irregular time-series with a lot of noise. Let's create a monthly NDVI time-series by computing the monthly average NDVI. We can use XArray's `resample()` method to aggregate the time-series by Months and coompute the mean. We specify *time* as `MS` to indicate aggregating by month start and *label* as `left` to indicate we want the start of the month as our series labels.\n",
+        "\n",
+        "Reference [Pandas Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)"
       ],
       "metadata": {
         "id": "h1BNPkpox4GW"
@@ -244,7 +246,7 @@
     {
       "cell_type": "code",
       "source": [
-        "time_series_aggregated = original_time_series.resample(time='MS').mean()\n",
+        "time_series_aggregated = original_time_series.resample(time='MS', label='left').mean()\n",
         "time_series_aggregated"
       ],
       "metadata": {
@@ -271,7 +273,7 @@
       "metadata": {
         "id": "-5J0g36ZljdQ"
       },
-      "execution_count": null,
+      "execution_count": 46,
       "outputs": []
     },
     {
@@ -296,9 +298,9 @@
           "height": 673
         },
         "id": "aim3uBoq66C0",
-        "outputId": "abab27ca-6200-4c1c-f2e9-3fae9e661d21"
+        "outputId": "aa490d4f-ba09-4253-ef46-9a4313b41d91"
       },
-      "execution_count": 17,
+      "execution_count": 47,
       "outputs": [
         {
           "output_type": "display_data",
@@ -352,6 +354,19 @@
         "  image.rio.to_raster(output_path, driver='COG')\n",
         "  print(f'Created file at {output_path}')"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Exercise\n",
+        "\n",
+        "Aggregate the time-series to 15-day interval. Apply gap-filling to fill missing values.\n",
+        "\n",
+        "*Hint: Refer to Reference [Pandas Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) for appropriate string value for the time parameter.*"
+      ],
+      "metadata": {
+        "id": "KcwzF0PxlsrG"
+      }
     }
   ]
 }