\n",
+ "\n",
+ "\n",
+ "# **1.0 Access SMAP data with Python**\n",
+ "\n",
+ "
\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4101ae06-3984-435c-abcc-f6346d15069b",
+ "metadata": {},
+ "source": [
+ "## **1. Tutorial Introduction/Overview**\n",
+ "\n",
+ "We will use the `earthaccess` library to authenticate with our Earthdata Login credentials and to search for and bulk download SMAP data. For this tutorial we wil use SPL3SMP version 008 as an example, but the same method can be applied to any other SMAP data sets archived at NSIDC. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd6c0128-efe4-4fab-8721-55fc366e3c7e",
+ "metadata": {},
+ "source": [
+ "### **Credits**\n",
+ "\n",
+ "This tutorial is based on the notebooks originally provided to NSIDC by Adam Purdy. Jennifer Roebuck of NSIDC updated the tutorials to include the latest version of SMAP data and use `earthaccess` for authentication, seatching for and downloading the data in order to incorporate it into the NSIDC-Data-Tutorials repo. \n",
+ "\n",
+ "For questions regarding the notebook, or to report problems, please create a new issue in the [NSIDC-Data-Tutorials repo](https://github.com/nsidc/NSIDC-Data-Tutorials/issues)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a57c664e-76f9-416e-ae03-75dce51b3cb7",
+ "metadata": {},
+ "source": [
+ "### **Learning Goals**\n",
+ "\n",
+ "After completing this notebook you will be able to use the `earthaccess` library to:\n",
+ "1. Authenticate with your Earthdata Login credentials.\n",
+ "2. Search for SMAP data.\n",
+ "3. Bulk download SMAP data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "015703a9-f02a-42f4-8ff0-3b002bf4f2f5",
+ "metadata": {},
+ "source": [
+ "### **Prerequisites**\n",
+ "\n",
+ "1. An Earthdata Login is required for data access. If you don't have one, you can register for one [here](https://urs.earthdata.nasa.gov/).\n",
+ "2. A .netrc file, that contains your Earthdata Login credentials, in your home directory. The current recommended practice for authentication is to create a .netrc file in your home directory following these [instructions](https://nsidc.org/data/user-resources/help-center/programmatic-data-access-guide).\n",
+ "3. The nsidc-tutorials environment is set up and activated. This [README](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/README.md) has setup instructions.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c45f3276-1172-4bfb-8389-e9d3cbbe88f4",
+ "metadata": {},
+ "source": [
+ "### **Time requirement**\n",
+ "\n",
+ "Allow 5 to 10 minutes to complete this tutorial."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53b77eb5-d5ed-4ddd-8fb1-6c69618d7852",
+ "metadata": {},
+ "source": [
+ "## **2. Tutorial steps**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7820a737-33f0-4470-b9a4-03c5c4f0354c",
+ "metadata": {},
+ "source": [
+ "### **Import libraries**\n",
+ "We need just two libraries, `os` for creating the directory to store the downloaded data in and `earthaccess` to authenticate, search for and download the data. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "059690ab-7dff-45c9-816a-6060a191f550",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Import libraries \n",
+ "\n",
+ "import os # needed to create the directory to store the downloaded data\n",
+ "import earthaccess # used for authentication and searching for downloading the data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1966ffa6-a5f2-4520-a8dc-f37678a2cf7a",
+ "metadata": {},
+ "source": [
+ "### **Authenticate**\n",
+ "\n",
+ "The first step is to authenticate using our Earthdata Login credentials. The `login` method will automatically search for these credentials as environment variables or in a `.netrc` files, and if those aren't available it will prompt us to enter our username and password. We use a `.netrc` strategy. A `.netrc` file is a text file located in our home directory that contains login information for remote machines. If we don't have a `.netrc` file, `login` can create one for us:\n",
+ "```\n",
+ "earthaccess.login(strategy='interactive',persist=True)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d47aa955-3d91-4418-85f9-5772f400f712",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "auth = earthaccess.login()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95e2532d-219b-4b9d-b5b9-b43c95b1aa7d",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### **Search for SPL3SMP data using spatial and temporal filters**\n",
+ "We will use the `search_data` method from the `earthaccess` library and the following variabes to search for granules within the SPL3SMP data set:\n",
+ "* `short_name` - this is the data set ID e.g. SPL3SMP. It can be found in the data set title on the data set landing page.\n",
+ "* `version` - data set version number, also included in the data set title.\n",
+ "* `cloud_hosted` - NSIDC is in the process of migrating data sets to the cloud. The data set we are interested is currently still archived on-premises so we will set this to False.\n",
+ "* `temporal` - set a temporal filter by specifying a start and end date in the format YYYY-MM-DD. In this tutorial we will look for data for the month of March 2017.\n",
+ "\n",
+ "It will output the number of granules that meet the search criteria."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d66e54ff-71dc-422c-9e8a-5b154fa0dbf7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Search for SPL3SMP files \n",
+ "\n",
+ "results = earthaccess.search_data(\n",
+ " short_name = 'SPL3SMP',\n",
+ " version = '008',\n",
+ " cloud_hosted = False,\n",
+ " temporal = ('2017-03-01','2017-03-31')\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7307f44-93cd-49b0-aa11-ae85aca29722",
+ "metadata": {},
+ "source": [
+ "### **Download the data** \n",
+ "Now that we have found the granules that meet our search criteria, we can download them using the `download` method from `earthaccess`. First, we will create a new directory to save the files in."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80d938ed-4fe6-4bff-b71a-cce39e7a9bd4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "this_dir = os.getcwd()\n",
+ "DATA_DIR = os.path.join(this_dir, 'data/L3_SM_P')\n",
+ "\n",
+ "if not os.path.exists(DATA_DIR):\n",
+ " os.makedirs(DATA_DIR)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c0fc789-ac80-474a-9928-8d9d4564ceac",
+ "metadata": {},
+ "source": [
+ "Now we will download the SPL3SMP data for March 2017."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "467ece65-932a-46e1-9f4c-1b47b628266b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "smap_files = earthaccess.download(results,DATA_DIR)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "910b2ef6-3e14-475e-b689-77bda4c1814e",
+ "metadata": {},
+ "source": [
+ "## **3. Learning outcomes recap (optional)**\n",
+ "\n",
+ "1. Authenticate with your Earthdata Login credentials.\n",
+ "2. Search for SMAP data.\n",
+ "3. Bulk download SMAP data.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b6b4172-7ba8-451d-9051-912aea174adf",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SMAP/01_download_smap_data_rendered.ipynb b/notebooks/SMAP/01_download_smap_data_rendered.ipynb
new file mode 100644
index 0000000..5bb92d1
--- /dev/null
+++ b/notebooks/SMAP/01_download_smap_data_rendered.ipynb
@@ -0,0 +1,313 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e86eaecf-a612-4dbb-8bdc-5b5dfddf65b9",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
+ "\n",
+ "# **1.0 Access SMAP data with Python**\n",
+ "\n",
+ "
\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4101ae06-3984-435c-abcc-f6346d15069b",
+ "metadata": {},
+ "source": [
+ "## **1. Tutorial Introduction/Overview**\n",
+ "\n",
+ "We will use the `earthaccess` library to authenticate with our Earthdata Login credentials and to search for and bulk download SMAP data. For this tutorial we wil use SPL3SMP version 008 as an example, but the same method can be applied to any other SMAP data sets archived at NSIDC. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd6c0128-efe4-4fab-8721-55fc366e3c7e",
+ "metadata": {},
+ "source": [
+ "### **Credits**\n",
+ "\n",
+ "This tutorial is based on the notebooks originally provided to NSIDC by Adam Purdy. Jennifer Roebuck of NSIDC updated the tutorials to include the latest version of SMAP data and use `earthaccess` for authentication, seatching for and downloading the data in order to incorporate it into the NSIDC-Data-Tutorials repo. \n",
+ "\n",
+ "For questions regarding the notebook, or to report problems, please create a new issue in the [NSIDC-Data-Tutorials repo](https://github.com/nsidc/NSIDC-Data-Tutorials/issues)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a57c664e-76f9-416e-ae03-75dce51b3cb7",
+ "metadata": {},
+ "source": [
+ "### **Learning Goals**\n",
+ "\n",
+ "After completing this notebook you will be able to use the `earthaccess` library to:\n",
+ "1. Authenticate with your Earthdata Login credentials.\n",
+ "2. Search for SMAP data.\n",
+ "3. Bulk download SMAP data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "015703a9-f02a-42f4-8ff0-3b002bf4f2f5",
+ "metadata": {},
+ "source": [
+ "### **Prerequisites**\n",
+ "\n",
+ "1. An Earthdata Login is required for data access. If you don't have one, you can register for one [here](https://urs.earthdata.nasa.gov/).\n",
+ "2. A .netrc file, that contains your Earthdata Login credentials, in your home directory. The current recommended practice for authentication is to create a .netrc file in your home directory following these [instructions](https://nsidc.org/data/user-resources/help-center/programmatic-data-access-guide).\n",
+ "3. The nsidc-tutorials environment is set up and activated. This [README](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/README.md) has setup instructions.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c45f3276-1172-4bfb-8389-e9d3cbbe88f4",
+ "metadata": {},
+ "source": [
+ "### **Time requirement**\n",
+ "\n",
+ "Allow 5 to 10 minutes to complete this tutorial."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53b77eb5-d5ed-4ddd-8fb1-6c69618d7852",
+ "metadata": {},
+ "source": [
+ "## **2. Tutorial steps**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7820a737-33f0-4470-b9a4-03c5c4f0354c",
+ "metadata": {},
+ "source": [
+ "### **Import libraries**\n",
+ "We need just two libraries, `os` for creating the directory to store the downloaded data in and `earthaccess` to authenticate, search for and download the data. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "059690ab-7dff-45c9-816a-6060a191f550",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Import libraries \n",
+ "\n",
+ "import os # needed to create the directory to store the downloaded data\n",
+ "import earthaccess # used for authentication and searching for downloading the data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1966ffa6-a5f2-4520-a8dc-f37678a2cf7a",
+ "metadata": {},
+ "source": [
+ "### **Authenticate**\n",
+ "\n",
+ "The first step is to authenticate using our Earthdata Login credentials. The `login` method will automatically search for these credentials as environment variables or in a `.netrc` files, and if those aren't available it will prompt us to enter our username and password. We use a `.netrc` strategy. A `.netrc` file is a text file located in our home directory that contains login information for remote machines. If we don't have a `.netrc` file, `login` can create one for us:\n",
+ "```\n",
+ "earthaccess.login(strategy='interactive',persist=True)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d47aa955-3d91-4418-85f9-5772f400f712",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "EARTHDATA_USERNAME and EARTHDATA_PASSWORD are not set in the current environment, try setting them or use a different strategy (netrc, interactive)\n",
+ "You're now authenticated with NASA Earthdata Login\n",
+ "Using token with expiration date: 08/26/2023\n",
+ "Using .netrc file for EDL\n"
+ ]
+ }
+ ],
+ "source": [
+ "auth = earthaccess.login()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95e2532d-219b-4b9d-b5b9-b43c95b1aa7d",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### **Search for SPL3SMP data using spatial and temporal filters**\n",
+ "We will use the `search_data` method from the `earthaccess` library and the following variabes to search for granules within the SPL3SMP data set:\n",
+ "* `short_name` - this is the data set ID e.g. SPL3SMP. It can be found in the data set title on the data set landing page.\n",
+ "* `version` - data set version number, also included in the data set title.\n",
+ "* `cloud_hosted` - NSIDC is in the process of migrating data sets to the cloud. The data set we are interested is currently still archived on-premises so we will set this to False.\n",
+ "* `temporal` - set a temporal filter by specifying a start and end date in the format YYYY-MM-DD. In this tutorial we will look for data for the month of March 2017.\n",
+ "\n",
+ "It will output the number of granules that meet the search criteria."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d66e54ff-71dc-422c-9e8a-5b154fa0dbf7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Granules found: 31\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Search for SPL3SMP files \n",
+ "\n",
+ "results = earthaccess.search_data(\n",
+ " short_name = 'SPL3SMP',\n",
+ " version = '008',\n",
+ " cloud_hosted = False,\n",
+ " temporal = ('2017-03-01','2017-03-31')\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7307f44-93cd-49b0-aa11-ae85aca29722",
+ "metadata": {},
+ "source": [
+ "### **Download the data** \n",
+ "Now that we have found the granules that meet our search criteria, we can download them using the `download` method from `earthaccess`. First, we will create a new directory to save the files in."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "80d938ed-4fe6-4bff-b71a-cce39e7a9bd4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "this_dir = os.getcwd()\n",
+ "DATA_DIR = os.path.join(this_dir, 'data/L3_SM_P')\n",
+ "\n",
+ "if not os.path.exists(DATA_DIR):\n",
+ " os.makedirs(DATA_DIR)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c0fc789-ac80-474a-9928-8d9d4564ceac",
+ "metadata": {},
+ "source": [
+ "Now we will download the SPL3SMP data for March 2017."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "467ece65-932a-46e1-9f4c-1b47b628266b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Getting 31 granules, approx download size: 0.93 GB\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "bd55adc8cc1b42d19658cbcc885b9c79",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "SUBMITTING | : 0%| | 0/31 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8b79c01960104821b455036763cf918a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "PROCESSING | : 0%| | 0/31 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "3506297cb88848cfa74828041418ee31",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "COLLECTING | : 0%| | 0/31 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "smap_files = earthaccess.download(results,DATA_DIR)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "910b2ef6-3e14-475e-b689-77bda4c1814e",
+ "metadata": {},
+ "source": [
+ "## **3. Learning outcomes recap (optional)**\n",
+ "\n",
+ "1. Authenticate with your Earthdata Login credentials.\n",
+ "2. Search for SMAP data.\n",
+ "3. Bulk download SMAP data.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b6b4172-7ba8-451d-9051-912aea174adf",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SMAP/02_read_and_plot_smap_data.ipynb b/notebooks/SMAP/02_read_and_plot_smap_data.ipynb
new file mode 100644
index 0000000..0883445
--- /dev/null
+++ b/notebooks/SMAP/02_read_and_plot_smap_data.ipynb
@@ -0,0 +1,549 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e86eaecf-a612-4dbb-8bdc-5b5dfddf65b9",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4101ae06-3984-435c-abcc-f6346d15069b",
+ "metadata": {},
+ "source": [
+ "## **1. Overview**\n",
+ "\n",
+ "We will read in the SMAP data that was downloaded using the 1.0 Download SMAP data notebook. We will then create a map with SMAP data and plot a time-series at a location on Earth. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd6c0128-efe4-4fab-8721-55fc366e3c7e",
+ "metadata": {},
+ "source": [
+ "### **Credits**\n",
+ "\n",
+ "This tutorial is based on the notebooks originally provided to NSIDC by Adam Purdy. Jennifer Roebuck of NSIDC updated the tutorials to include the latest version of SMAP data and use earthaccess for authentication, seatching for and downloading the data in order to incorporate it into the NSIDC-Data-Tutorials repo. \n",
+ "\n",
+ "For questions regarding the notebook, or to report problems, please create a new issue in the [NSIDC-Data-Tutorials repo](https://github.com/nsidc/NSIDC-Data-Tutorials/issues)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a57c664e-76f9-416e-ae03-75dce51b3cb7",
+ "metadata": {},
+ "source": [
+ "### **Learning Goals**\n",
+ "\n",
+ "1. Read in SMAP data and navigate the metadata\n",
+ "2. Create a map with SMAP data\n",
+ "3. Plot a time-series at a location on Earth. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "015703a9-f02a-42f4-8ff0-3b002bf4f2f5",
+ "metadata": {},
+ "source": [
+ "### **Prerequisites**\n",
+ "\n",
+ "1. The nsidc-tutorials environment is set up and activated. This [README](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/README.md) has setup instructions.\n",
+ "2. SMAP data that were downloaded in the previous notebook tutorial 1.0 Download SMAP data. \n",
+ "3. The EASE-Grid 2.0 longitude and latitude data sets. The binary format of these files have been provided within this repo for use in this tutorial, but please note they are also available in NetCDF format from the NSIDC website at this [page](https://nsidc.org/data/NSIDC-0772/versions/1). \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c45f3276-1172-4bfb-8389-e9d3cbbe88f4",
+ "metadata": {},
+ "source": [
+ "### **Time requirement**\n",
+ "\n",
+ "Allow 15 to 20 minutes to complete this tutorial."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53b77eb5-d5ed-4ddd-8fb1-6c69618d7852",
+ "metadata": {},
+ "source": [
+ "## **2. Tutorial steps**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7820a737-33f0-4470-b9a4-03c5c4f0354c",
+ "metadata": {},
+ "source": [
+ "### **Import libraries**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "059690ab-7dff-45c9-816a-6060a191f550",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Import libraries \n",
+ "import datetime as dt\n",
+ "import glob\n",
+ "import h5py\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cartopy.crs as ccrs\n",
+ "import numpy as np\n",
+ "import os\n",
+ "import pandas as pd\n",
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1966ffa6-a5f2-4520-a8dc-f37678a2cf7a",
+ "metadata": {},
+ "source": [
+ "### **Read SMAP data and navigate metadata**\n",
+ "\n",
+ "First we will navigate to the directory with the data we want to use. Then we will generate a list of the files and print out the name of one of the files."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d47aa955-3d91-4418-85f9-5772f400f712",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "this_dir = os.getcwd()\n",
+ "L3_SM_P_dir = os.path.join(this_dir, 'data/L3_SM_P/')\n",
+ "\n",
+ "flist = glob.glob(os.path.join(L3_SM_P_dir, '*.h5'))\n",
+ " \n",
+ "filename = flist[0]; \n",
+ "print(\"File we are using: \" + filename + '\\n')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95e2532d-219b-4b9d-b5b9-b43c95b1aa7d",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Now we will use `h5py.File()`to open the file. Then we can look at the folders within the HDF5 file to access the data we want. The cell below will print out a list of variables within one of the folders in the HDF5 file, the Soil_Moisture_Retrieval_Data_AM folder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d66e54ff-71dc-422c-9e8a-5b154fa0dbf7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f = h5py.File(filename, 'r')\n",
+ "print('By using the command h5py.File() a filehandle is returned:')\n",
+ "print(f); print('\\n')\n",
+ "\n",
+ "print(\"Now lets look at the groups within the file to access:\")\n",
+ "i=0;\n",
+ "for key in f.keys():\n",
+ " print(str(i)+ '\\t'+key)\n",
+ " i+=1\n",
+ "group_id=list(f.keys())[1];# < Lets focus on the AM overpass for this example\n",
+ "print('\\n')\n",
+ "i=0\n",
+ "print(\"Now lets look at the variables within the filegroup **Soil_Moisture_Retrieval_Data_AM** to access the actual data:\")\n",
+ "for var in list(f[group_id].keys()):\n",
+ " print(str(i)+'\\t'+var)\n",
+ " i+=1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7307f44-93cd-49b0-aa11-ae85aca29722",
+ "metadata": {},
+ "source": [
+ "Now that we know the variables within the Soil_Moisture_Retrieval_Data_AM folder lets grab the data we want to plot. First we need to take a look at the extent of the data in terms of the number of rows and columns, we will need this later when opening the EASE-Grid 2.0 lat and lon files."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80d938ed-4fe6-4bff-b71a-cce39e7a9bd4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print('The extent of the data in rows and columns is: '+str(f[group_id][list(f[group_id].keys())[0]].shape))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c0fc789-ac80-474a-9928-8d9d4564ceac",
+ "metadata": {},
+ "source": [
+ "Based on the list above we can find the index number of the variable we want to plot. For example, soil_moisture is at index 26 in the Soil_Moisture_Retrievel_Data_AM folder. We will use this index value in the cell below to read in the data associated with the soil_moisture variable. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "467ece65-932a-46e1-9f4c-1b47b628266b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "var_id = list(f[group_id].keys())[26] # soil_moisture\n",
+ "sm_data = f[group_id][var_id][:,:]\n",
+ "sm_ds = f[group_id][var_id]\n",
+ "print('Data are returned as '+str(type(sm_data)) + ' something easy to work with in python.')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "84d934ed-c3d2-45e6-8d96-b6c9c227db32",
+ "metadata": {},
+ "source": [
+ "This cell plots the out the retrieval quality flag values. For a further explanation of these values, see the third notebook titled '3.0 SMAP Quality Flags'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8304ecdd-58cc-44ee-b2c9-6c9ca0b5c8b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ret_flag_L3_P = f[group_id]['retrieval_qual_flag'][:,:]\n",
+ "print(np.unique(ret_flag_L3_P))\n",
+ "print(type(ret_flag_L3_P))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "910b2ef6-3e14-475e-b689-77bda4c1814e",
+ "metadata": {},
+ "source": [
+ "### Create a map with SMAP data\n",
+ "\n",
+ "Lets quickly plot the soil_moisture variable data to get a sense of what we are working with."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b6b4172-7ba8-451d-9051-912aea174adf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.imshow(sm_data)\n",
+ "cbar = plt.colorbar(orientation = 'horizontal')\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "740a8856-0c47-4053-985c-8b8dd201c5d2",
+ "metadata": {},
+ "source": [
+ "We need to ignore the null values to get a better idea of the range of values in the data set. \n",
+ "\n",
+ "You can find details on what values are used to represent null in the metadata of the supplemental documents, which are available from the data set landing [page](https://nsidc.org/data/SPL3SMP).\n",
+ "\n",
+ "For the soil_moisture variable and most SMAP data sets the null value is -9999. To confirm this is the case we can look at the attributes for the 'soil_moisture' variable. The following cell will find the '_FillValue'_ attribute for the soil_moisture variable and print out its value (in this case it will be -9999)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44106db1-01aa-4d26-99b6-c1530af3c554",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f[group_id][var_id].attrs['_FillValue'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "596bbaf0-c33b-4cd4-94ca-98bb834e0195",
+ "metadata": {},
+ "source": [
+ "Now we will replace the FillValue of -9999 with NaN and plot the soil moisture variable again, and set the color bar range to 0 - 0.55. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "69eca887-c762-45e3-879f-3026a8f51d05",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sm_data[sm_data==f[group_id][var_id].attrs['_FillValue']]=np.nan;\n",
+ "plt.imshow(sm_data,vmin=0.,vmax=0.55, cmap = 'terrain_r');\n",
+ "cbar = plt.colorbar(orientation='horizontal')\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49d9fbc8-69f5-419a-bc78-1c33f57a4d01",
+ "metadata": {},
+ "source": [
+ "Lets add some coastlines to this plot, and geolocate the data using the coordinates in the EASE-Grid 2.0 latitude and longitude files. First, we will read in the EASE Grid 2.0 latitude and longitude data sets. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a3cf1f80-a58f-4818-87da-b3ba16a4c861",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read binary files and reshape to correct size\n",
+ "lats = np.fromfile('EASE2_M36km.lats.964x406x1.double', \n",
+ " dtype=np.float64).reshape((406,964))#< reshape to dimensions above\n",
+ "lons = np.fromfile('EASE2_M36km.lons.964x406x1.double', \n",
+ " dtype=np.float64).reshape((406,964))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8a7f186c-2fb7-40b3-9a4b-06a510a956e7",
+ "metadata": {},
+ "source": [
+ "Now we will use `cartopy` to plot the soil_moisture variable on a basemap in the Robinson projection. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7b3f885e-8761-42ec-8688-e4e8da312427",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = plt.figure(figsize=(10,6))\n",
+ "ax = plt.axes(projection=ccrs.Robinson())\n",
+ "ax.coastlines()\n",
+ "p = plt.pcolormesh(lons, lats, sm_data, transform=ccrs.PlateCarree(), clim=(0.,0.55), cmap='terrain_r')\n",
+ "cbar = fig.colorbar(p, location='bottom', pad=0.05)\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0233479-75bf-44d0-b969-1bc9c4b1d4b8",
+ "metadata": {},
+ "source": [
+ "Awesome! We have made a global plot, now lets see if we can streamline this to process more data and create a time series. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d27d4eb-6582-44c7-a0e8-f1847e769640",
+ "metadata": {},
+ "source": [
+ "### Plot a time-series at a location in Earth\n",
+ "\n",
+ "Lets navigate back to the L3_SM_P directory which contains all the files we downloaded and print out a list of all these files "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "95e1ffc2-70a4-4beb-b09e-06ac37d6bb37",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for fName in flist:\n",
+ " print(fName)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6996b015-54c1-4aac-89c4-bedb8498022f",
+ "metadata": {},
+ "source": [
+ "Now we will make a function to load these files, and read in the soil_moisture and retrieval_qual_flag variables. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9fd5d171-79d6-4c32-82cd-49d8e097f5a3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def read_SML3P(filepath):\n",
+ " ''' This function extracts lat, lon and soil moisture from SMAP L3 P HDF5 file.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " filepath : str\n",
+ " File path of a SMAP L3 HDF5 file\n",
+ " Returns\n",
+ " -------\n",
+ " soil_moisture_am: numpy.array\n",
+ " ''' \n",
+ " with h5py.File(filepath, 'r') as f:\n",
+ " # Extract data info\n",
+ " group_id_am = 'Soil_Moisture_Retrieval_Data_AM'\n",
+ " var_id_am = 'soil_moisture'\n",
+ " flag_id_am = 'retrieval_qual_flag'\n",
+ " soil_moisture_am = f[group_id_am][var_id_am][:,:]\n",
+ " flag_am = f[group_id_am][flag_id_am][:,:]\n",
+ " soil_moisture_am[soil_moisture_am==-9999.0]=np.nan;\n",
+ " soil_moisture_am[(flag_am>>0)&1==1]=np.nan\n",
+ " filename = os.path.basename(filepath)\n",
+ " yyyymmdd= filename.split('_')[4]\n",
+ " yyyy = int(yyyymmdd[0:4]); mm = int(yyyymmdd[4:6]); dd = int(yyyymmdd[6:8])\n",
+ " date=dt.datetime(yyyy,mm,dd)\n",
+ " return soil_moisture_am,date"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40e84063-d082-457c-a525-42dee9a303a6",
+ "metadata": {},
+ "source": [
+ "Now we will test that this function works by loading the first file in the list and plotting the soil moisture variable. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ae0d56d-9c21-4a3d-ac41-b08db0ec9e79",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sm_test,date = read_SML3P(flist[0])\n",
+ "plt.imshow(sm_test)\n",
+ "cbar = plt.colorbar(orientation='horizontal')\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')\n",
+ "plt.title(date)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bb0ed7f-76ec-47aa-bcae-85256771ea9e",
+ "metadata": {},
+ "source": [
+ "That was just one file, now we will load all 31 files to create a 3D array (soil moisture values over 31 days). The cell below will output the size of this array."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6643683e-d762-4e66-8ef5-169a2e34c625",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sm_data_3d = np.empty([sm_data.shape[0],sm_data.shape[1],len(flist)])\n",
+ "times = []\n",
+ "print('sm_data_3d has dimensions '+str(sm_data_3d.shape))\n",
+ "i=0\n",
+ "for fName in flist:\n",
+ " sm_data_3d[:,:,i],time_i = read_SML3P(fName)\n",
+ " times.append(time_i)\n",
+ " i+=1\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09dfcb9b-7d9d-4412-b7e3-be66a862b055",
+ "metadata": {},
+ "source": [
+ "Next we will calculate the mean soil moisture over 31 days and plot it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "08d51de5-b934-4cb2-af7a-d3eaae101c27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sm_mean = np.nanmean(sm_data_3d,2)\n",
+ "sm_mean.shape\n",
+ "plt.imshow(sm_mean,vmin=0.,vmax=0.55,cmap='terrain_r')\n",
+ "cbar = plt.colorbar(orientation='horizontal')\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "003af2be-a78d-4fe7-84a8-3be2252c635b",
+ "metadata": {},
+ "source": [
+ "Lastly, we can select a region within this map and plot the average soil moisture over time for this region."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3103952a-9b6f-4b35-a1e7-bdcb8222b316",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "N_lat = 37.5; \n",
+ "S_lat = 33\n",
+ "W_lon = -113.5\n",
+ "E_lon = -110.0\n",
+ "\n",
+ "subset = (latsS_lat)&(lons>W_lon)&(lons\n",
+ "\n",
+ "\n",
+ "# **2.0 Read and Plot SMAP data**\n",
+ "\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4101ae06-3984-435c-abcc-f6346d15069b",
+ "metadata": {},
+ "source": [
+ "## **1. Overview**\n",
+ "\n",
+ "We will read in the SMAP data that was downloaded using the 1.0 Download SMAP data notebook. We will then create a map with SMAP data and plot a time-series at a location on Earth. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd6c0128-efe4-4fab-8721-55fc366e3c7e",
+ "metadata": {},
+ "source": [
+ "### **Credits**\n",
+ "\n",
+ "This tutorial is based on the notebooks originally provided to NSIDC by Adam Purdy. Jennifer Roebuck of NSIDC updated the tutorials to include the latest version of SMAP data and use earthaccess for authentication, seatching for and downloading the data in order to incorporate it into the NSIDC-Data-Tutorials repo. \n",
+ "\n",
+ "For questions regarding the notebook, or to report problems, please create a new issue in the [NSIDC-Data-Tutorials repo](https://github.com/nsidc/NSIDC-Data-Tutorials/issues)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a57c664e-76f9-416e-ae03-75dce51b3cb7",
+ "metadata": {},
+ "source": [
+ "### **Learning Goals**\n",
+ "\n",
+ "1. Read in SMAP data and navigate the metadata\n",
+ "2. Create a map with SMAP data\n",
+ "3. Plot a time-series at a location on Earth. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "015703a9-f02a-42f4-8ff0-3b002bf4f2f5",
+ "metadata": {},
+ "source": [
+ "### **Prerequisites**\n",
+ "\n",
+ "1. The nsidc-tutorials environment is set up and activated. This [README](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/README.md) has setup instructions.\n",
+ "2. SMAP data that were downloaded in the previous notebook tutorial 1.0 Download SMAP data. \n",
+ "3. The EASE-Grid 2.0 longitude and latitude data sets. The binary format of these files have been provided within this repo for use in this tutorial, but please note they are also available in NetCDF format from the NSIDC website at this [page](https://nsidc.org/data/NSIDC-0772/versions/1). \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c45f3276-1172-4bfb-8389-e9d3cbbe88f4",
+ "metadata": {},
+ "source": [
+ "### **Time requirement**\n",
+ "\n",
+ "Allow 15 to 20 minutes to complete this tutorial."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53b77eb5-d5ed-4ddd-8fb1-6c69618d7852",
+ "metadata": {},
+ "source": [
+ "## **2. Tutorial steps**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7820a737-33f0-4470-b9a4-03c5c4f0354c",
+ "metadata": {},
+ "source": [
+ "### **Import libraries**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "059690ab-7dff-45c9-816a-6060a191f550",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Import libraries \n",
+ "import datetime as dt\n",
+ "import glob\n",
+ "import h5py\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cartopy.crs as ccrs\n",
+ "import numpy as np\n",
+ "import os\n",
+ "import pandas as pd\n",
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1966ffa6-a5f2-4520-a8dc-f37678a2cf7a",
+ "metadata": {},
+ "source": [
+ "### **Read SMAP data and navigate metadata**\n",
+ "\n",
+ "First we will navigate to the directory with the data we want to use. Then we will generate a list of the files and print out the name of one of the files."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d47aa955-3d91-4418-85f9-5772f400f712",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "this_dir = os.getcwd()\n",
+ "L3_SM_P_dir = os.path.join(this_dir, 'data/L3_SM_P/')\n",
+ "\n",
+ "flist = glob.glob(os.path.join(L3_SM_P_dir, '*.h5'))\n",
+ " \n",
+ "filename = flist[0]; \n",
+ "print(\"File we are using: \" + filename + '\\n')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95e2532d-219b-4b9d-b5b9-b43c95b1aa7d",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Now we will use `h5py.File()`to open the file. Then we can look at the folders within the HDF5 file to access the data we want. The cell below will print out a list of variables within one of the folders in the HDF5 file, the Soil_Moisture_Retrieval_Data_AM folder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d66e54ff-71dc-422c-9e8a-5b154fa0dbf7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "By using the command h5py.File() a filehandle is returned:\n",
+ "\n",
+ "\n",
+ "\n",
+ "Now lets look at the groups within the file to access:\n",
+ "0\tMetadata\n",
+ "1\tSoil_Moisture_Retrieval_Data_AM\n",
+ "2\tSoil_Moisture_Retrieval_Data_PM\n",
+ "\n",
+ "\n",
+ "Now lets look at the variables within the filegroup **Soil_Moisture_Retrieval_Data_AM** to access the actual data:\n",
+ "0\tEASE_column_index\n",
+ "1\tEASE_row_index\n",
+ "2\talbedo\n",
+ "3\talbedo_dca\n",
+ "4\talbedo_scah\n",
+ "5\talbedo_scav\n",
+ "6\tboresight_incidence\n",
+ "7\tbulk_density\n",
+ "8\tclay_fraction\n",
+ "9\tfreeze_thaw_fraction\n",
+ "10\tgrid_surface_status\n",
+ "11\tlandcover_class\n",
+ "12\tlandcover_class_fraction\n",
+ "13\tlatitude\n",
+ "14\tlatitude_centroid\n",
+ "15\tlongitude\n",
+ "16\tlongitude_centroid\n",
+ "17\tradar_water_body_fraction\n",
+ "18\tretrieval_qual_flag\n",
+ "19\tretrieval_qual_flag_dca\n",
+ "20\tretrieval_qual_flag_scah\n",
+ "21\tretrieval_qual_flag_scav\n",
+ "22\troughness_coefficient\n",
+ "23\troughness_coefficient_dca\n",
+ "24\troughness_coefficient_scah\n",
+ "25\troughness_coefficient_scav\n",
+ "26\tsoil_moisture\n",
+ "27\tsoil_moisture_dca\n",
+ "28\tsoil_moisture_error\n",
+ "29\tsoil_moisture_scah\n",
+ "30\tsoil_moisture_scav\n",
+ "31\tstatic_water_body_fraction\n",
+ "32\tsurface_flag\n",
+ "33\tsurface_temperature\n",
+ "34\tsurface_water_fraction_mb_h\n",
+ "35\tsurface_water_fraction_mb_v\n",
+ "36\ttb_3_corrected\n",
+ "37\ttb_4_corrected\n",
+ "38\ttb_h_corrected\n",
+ "39\ttb_h_uncorrected\n",
+ "40\ttb_qual_flag_3\n",
+ "41\ttb_qual_flag_4\n",
+ "42\ttb_qual_flag_h\n",
+ "43\ttb_qual_flag_v\n",
+ "44\ttb_time_seconds\n",
+ "45\ttb_time_utc\n",
+ "46\ttb_v_corrected\n",
+ "47\ttb_v_uncorrected\n",
+ "48\tvegetation_opacity\n",
+ "49\tvegetation_opacity_dca\n",
+ "50\tvegetation_opacity_scah\n",
+ "51\tvegetation_opacity_scav\n",
+ "52\tvegetation_water_content\n"
+ ]
+ }
+ ],
+ "source": [
+ "f = h5py.File(filename, 'r')\n",
+ "print('By using the command h5py.File() a filehandle is returned:')\n",
+ "print(f); print('\\n')\n",
+ "\n",
+ "print(\"Now lets look at the groups within the file to access:\")\n",
+ "i=0;\n",
+ "for key in f.keys():\n",
+ " print(str(i)+ '\\t'+key)\n",
+ " i+=1\n",
+ "group_id=list(f.keys())[1];# < Lets focus on the AM overpass for this example\n",
+ "print('\\n')\n",
+ "i=0\n",
+ "print(\"Now lets look at the variables within the filegroup **Soil_Moisture_Retrieval_Data_AM** to access the actual data:\")\n",
+ "for var in list(f[group_id].keys()):\n",
+ " print(str(i)+'\\t'+var)\n",
+ " i+=1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7307f44-93cd-49b0-aa11-ae85aca29722",
+ "metadata": {},
+ "source": [
+ "Now that we know the variables within the Soil_Moisture_Retrieval_Data_AM folder lets grab the data we want to plot. First we need to take a look at the extent of the data in terms of the number of rows and columns, we will need this later when opening the EASE-Grid 2.0 lat and lon files."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "80d938ed-4fe6-4bff-b71a-cce39e7a9bd4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The extent of the data in rows and columns is: (406, 964)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('The extent of the data in rows and columns is: '+str(f[group_id][list(f[group_id].keys())[0]].shape))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c0fc789-ac80-474a-9928-8d9d4564ceac",
+ "metadata": {},
+ "source": [
+ "Based on the list above we can find the index number of the variable we want to plot. For example, soil_moisture is at index 26 in the Soil_Moisture_Retrievel_Data_AM folder. We will use this index value in the cell below to read in the data associated with the soil_moisture variable. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "467ece65-932a-46e1-9f4c-1b47b628266b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data are returned as something easy to work with in python.\n"
+ ]
+ }
+ ],
+ "source": [
+ "var_id = list(f[group_id].keys())[26] # soil_moisture\n",
+ "sm_data = f[group_id][var_id][:,:]\n",
+ "sm_ds = f[group_id][var_id]\n",
+ "print('Data are returned as '+str(type(sm_data)) + ' something easy to work with in python.')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "84d934ed-c3d2-45e6-8d96-b6c9c227db32",
+ "metadata": {},
+ "source": [
+ "This cell plots the out the retrieval quality flag values. For a further explanation of these values, see the third notebook titled '3.0 SMAP Quality Flags'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8304ecdd-58cc-44ee-b2c9-6c9ca0b5c8b9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0 1 5 7 8 9 13 15]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "ret_flag_L3_P = f[group_id]['retrieval_qual_flag'][:,:]\n",
+ "print(np.unique(ret_flag_L3_P))\n",
+ "print(type(ret_flag_L3_P))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "910b2ef6-3e14-475e-b689-77bda4c1814e",
+ "metadata": {},
+ "source": [
+ "### Create a map with SMAP data\n",
+ "\n",
+ "Lets quickly plot the soil_moisture variable data to get a sense of what we are working with."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "3b6b4172-7ba8-451d-9051-912aea174adf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(sm_data)\n",
+ "cbar = plt.colorbar(orientation = 'horizontal')\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "740a8856-0c47-4053-985c-8b8dd201c5d2",
+ "metadata": {},
+ "source": [
+ "We need to ignore the null values to get a better idea of the range of values in the data set. \n",
+ "\n",
+ "You can find details on what values are used to represent null in the metadata of the supplemental documents, which are available from the data set landing [page](https://nsidc.org/data/SPL3SMP).\n",
+ "\n",
+ "For the soil_moisture variable and most SMAP data sets the null value is -9999. To confirm this is the case we can look at the attributes for the 'soil_moisture' variable. The following cell will find the '_FillValue'_ attribute for the soil_moisture variable and print out its value (in this case it will be -9999)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "44106db1-01aa-4d26-99b6-c1530af3c554",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-9999.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f[group_id][var_id].attrs['_FillValue'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "596bbaf0-c33b-4cd4-94ca-98bb834e0195",
+ "metadata": {},
+ "source": [
+ "Now we will replace the FillValue of -9999 with NaN and plot the soil moisture variable again, and set the color bar range to 0 - 0.55. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "69eca887-c762-45e3-879f-3026a8f51d05",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sm_data[sm_data==f[group_id][var_id].attrs['_FillValue']]=np.nan;\n",
+ "plt.imshow(sm_data,vmin=0.,vmax=0.55, cmap = 'terrain_r');\n",
+ "cbar = plt.colorbar(orientation='horizontal')\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49d9fbc8-69f5-419a-bc78-1c33f57a4d01",
+ "metadata": {},
+ "source": [
+ "Lets add some coastlines to this plot, and geolocate the data using the coordinates in the EASE-Grid 2.0 latitude and longitude files. First, we will read in the EASE Grid 2.0 latitude and longitude data sets. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "a3cf1f80-a58f-4818-87da-b3ba16a4c861",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read binary files and reshape to correct size\n",
+ "lats = np.fromfile('EASE2_M36km.lats.964x406x1.double', \n",
+ " dtype=np.float64).reshape((406,964))#< reshape to dimensions above\n",
+ "lons = np.fromfile('EASE2_M36km.lons.964x406x1.double', \n",
+ " dtype=np.float64).reshape((406,964))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8a7f186c-2fb7-40b3-9a4b-06a510a956e7",
+ "metadata": {},
+ "source": [
+ "Now we will use `cartopy` to plot the soil_moisture variable on a basemap in the Robinson projection. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "7b3f885e-8761-42ec-8688-e4e8da312427",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(10,6))\n",
+ "ax = plt.axes(projection=ccrs.Robinson())\n",
+ "ax.coastlines()\n",
+ "p = plt.pcolormesh(lons, lats, sm_data, transform=ccrs.PlateCarree(), clim=(0.,0.55), cmap='terrain_r')\n",
+ "cbar = fig.colorbar(p, location='bottom', pad=0.05)\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0233479-75bf-44d0-b969-1bc9c4b1d4b8",
+ "metadata": {},
+ "source": [
+ "Awesome! We have made a global plot, now lets see if we can streamline this to process more data and create a time series. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d27d4eb-6582-44c7-a0e8-f1847e769640",
+ "metadata": {},
+ "source": [
+ "### Plot a time-series at a location in Earth\n",
+ "\n",
+ "Lets navigate back to the L3_SM_P directory which contains all the files we downloaded and print out a list of all these files "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "95e1ffc2-70a4-4beb-b09e-06ac37d6bb37",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for fName in flist:\n",
+ " print(fName)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6996b015-54c1-4aac-89c4-bedb8498022f",
+ "metadata": {},
+ "source": [
+ "Now we will make a function to load these files, and read in the soil_moisture and retrieval_qual_flag variables. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "9fd5d171-79d6-4c32-82cd-49d8e097f5a3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def read_SML3P(filepath):\n",
+ " ''' This function extracts lat, lon and soil moisture from SMAP L3 P HDF5 file.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " filepath : str\n",
+ " File path of a SMAP L3 HDF5 file\n",
+ " Returns\n",
+ " -------\n",
+ " soil_moisture_am: numpy.array\n",
+ " ''' \n",
+ " with h5py.File(filepath, 'r') as f:\n",
+ " # Extract data info\n",
+ " group_id_am = 'Soil_Moisture_Retrieval_Data_AM'\n",
+ " var_id_am = 'soil_moisture'\n",
+ " flag_id_am = 'retrieval_qual_flag'\n",
+ " soil_moisture_am = f[group_id_am][var_id_am][:,:]\n",
+ " flag_am = f[group_id_am][flag_id_am][:,:]\n",
+ " soil_moisture_am[soil_moisture_am==-9999.0]=np.nan;\n",
+ " soil_moisture_am[(flag_am>>0)&1==1]=np.nan\n",
+ " filename = os.path.basename(filepath)\n",
+ " yyyymmdd= filename.split('_')[4]\n",
+ " yyyy = int(yyyymmdd[0:4]); mm = int(yyyymmdd[4:6]); dd = int(yyyymmdd[6:8])\n",
+ " date=dt.datetime(yyyy,mm,dd)\n",
+ " return soil_moisture_am,date"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40e84063-d082-457c-a525-42dee9a303a6",
+ "metadata": {},
+ "source": [
+ "Now we will test that this function works by loading the first file in the list and plotting the soil moisture variable. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "3ae0d56d-9c21-4a3d-ac41-b08db0ec9e79",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, '2017-03-27 00:00:00')"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sm_test,date = read_SML3P(flist[0])\n",
+ "plt.imshow(sm_test)\n",
+ "cbar = plt.colorbar(orientation='horizontal')\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')\n",
+ "plt.title(date)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bb0ed7f-76ec-47aa-bcae-85256771ea9e",
+ "metadata": {},
+ "source": [
+ "That was just one file, now we will load all 31 files to create a 3D array (soil moisture values over 31 days). The cell below will output the size of this array."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "6643683e-d762-4e66-8ef5-169a2e34c625",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sm_data_3d has dimensions (406, 964, 31)\n"
+ ]
+ }
+ ],
+ "source": [
+ "sm_data_3d = np.empty([sm_data.shape[0],sm_data.shape[1],len(flist)])\n",
+ "times = []\n",
+ "print('sm_data_3d has dimensions '+str(sm_data_3d.shape))\n",
+ "i=0\n",
+ "for fName in flist:\n",
+ " sm_data_3d[:,:,i],time_i = read_SML3P(fName)\n",
+ " times.append(time_i)\n",
+ " i+=1\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09dfcb9b-7d9d-4412-b7e3-be66a862b055",
+ "metadata": {},
+ "source": [
+ "Next we will calculate the mean soil moisture over 31 days and plot it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "08d51de5-b934-4cb2-af7a-d3eaae101c27",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/5l/f2dh2cn97r9822g1lk7jnt7r0000gp/T/ipykernel_67424/3387626929.py:1: RuntimeWarning: Mean of empty slice\n",
+ " sm_mean = np.nanmean(sm_data_3d,2)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sm_mean = np.nanmean(sm_data_3d,2)\n",
+ "sm_mean.shape\n",
+ "plt.imshow(sm_mean,vmin=0.,vmax=0.55,cmap='terrain_r')\n",
+ "cbar = plt.colorbar(orientation='horizontal')\n",
+ "cbar.set_label('$cm^3 cm^{-3}$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "003af2be-a78d-4fe7-84a8-3be2252c635b",
+ "metadata": {},
+ "source": [
+ "Lastly, we can select a region within this map and plot the average soil moisture over time for this region."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "3103952a-9b6f-4b35-a1e7-bdcb8222b316",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/5l/f2dh2cn97r9822g1lk7jnt7r0000gp/T/ipykernel_67424/3643618336.py:10: RuntimeWarning: Mean of empty slice\n",
+ " sm_time[i] = np.nanmean(sm_2d[subset]);\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Soil Moisture')"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "N_lat = 37.5; \n",
+ "S_lat = 33\n",
+ "W_lon = -113.5\n",
+ "E_lon = -110.0\n",
+ "\n",
+ "subset = (latsS_lat)&(lons>W_lon)&(lons\n",
+ "\n",
+ "\n",
+ "# **3.0 SMAP Quality Flags**\n",
+ "\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48e87d85-674a-4f38-8af9-14db223b0d96",
+ "metadata": {},
+ "source": [
+ "## 1. **Overview**\n",
+ "\n",
+ "This provides an overview of the retrieval quality flags and surface quality flags that are used with SMAP data. \n",
+ "\n",
+ "* Retrieval Quality Flag (combines all surface flags)\n",
+ "* Surface Quality Flag (provides information on why certain areas might be flagged) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88418780-4ced-44d3-a54c-01059c25f5f7",
+ "metadata": {},
+ "source": [
+ "### **Credits**\n",
+ "This tutorial is based on the notebooks originally provided to NSIDC by Adam Purdy. Jennifer Roebuck of NSIDC updated the tutorials to include the latest version of SMAP data and use earthaccess for authentication, seatching for and downloading the data in order to incorporate it into the NSIDC-Data-Tutorials repo. \n",
+ "\n",
+ "For questions regarding the notebook, or to report problems, please create a new issue in the [NSIDC-Data-Tutorials repo](https://github.com/nsidc/NSIDC-Data-Tutorials/issues).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "faef4353-4c94-45fe-9832-3831f3fa37e0",
+ "metadata": {},
+ "source": [
+ "### **Learning Goals**\n",
+ "\n",
+ "1. Understand the retrieval and surface quality flags and how to use them"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d21ca604-20de-460d-be82-d504720160d6",
+ "metadata": {},
+ "source": [
+ "### **Prerequisites**\n",
+ "\n",
+ "1. The nsidc-tutorials environment is set up and activated. This [README](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/README.md) has setup instructions.\n",
+ "2. SMAP data that were downloaded in the first notebook tutorial - 1.0 Download SMAP data. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc4ebd76-7580-4c7c-838b-7b0aae2a97a3",
+ "metadata": {},
+ "source": [
+ "### **Time Requirement**\n",
+ "\n",
+ "Allow approximtely 5 to 10 minutes to complete this tutorial. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5770fce2-57be-4510-bdb6-b071c220d79b",
+ "metadata": {},
+ "source": [
+ "## **2. Tutorial Steps**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1087c320-3a87-420b-b241-0fbe56620f9b",
+ "metadata": {},
+ "source": [
+ "### Import libraries\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a3353e2a-5005-43d8-839a-e9c7e16fcf08",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import datetime as dt\n",
+ "import glob\n",
+ "import h5py\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "921ec8d3-5486-48a6-9684-15f66aef587a",
+ "metadata": {},
+ "source": [
+ "Read in the SMAP data that we downloaded in the previous notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "759498fc-2ded-4288-9d59-5bfbaf1c22ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "this_dir = os.getcwd()\n",
+ "L3_SM_P_dir = os.path.join(this_dir, 'data/L3_SM_P/')\n",
+ "\n",
+ "flist = glob.glob(os.path.join(L3_SM_P_dir, '*.h5'))\n",
+ "filename = flist[0]; \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "358efeb8-49c7-496e-94d6-558178ae4cfb",
+ "metadata": {},
+ "source": [
+ "Read in the soil moisture and surface_flag variables from the Soil_Moisture_Retrieval_Data_AM group in each of the files. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc77eeae-bfa0-4a3c-88fc-2544b8fbc4e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f = h5py.File(filename, 'r')\n",
+ "group_id = 'Soil_Moisture_Retrieval_Data_AM'\n",
+ "var_id = list(f[group_id].keys())[25] # soil_moisture\n",
+ "sm_data = f[group_id][var_id][:,:]\n",
+ "surf_flag_L3_P = f[group_id]['surface_flag'][:,:]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7cf93589-41b5-44d7-988f-394fbf18c514",
+ "metadata": {},
+ "source": [
+ "Now lets look at the two types of flags\n",
+ "\n",
+ "### Retrieval Flags \n",
+ "\n",
+ "Four different values are possible, as outlined in the cell below. We will plot the retrieval quality flag and in the resulting plot areas that have a value of 0 (black regions) include data of recommended quality. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a85e1545-84ea-485a-8653-e37a27654e40",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Grab the Retrieval Quality Variable\n",
+ "ret_flag_L3_P = f[group_id]['retrieval_qual_flag'][:,:]\n",
+ "# Create a definition for the retrieval flags\n",
+ "ret_flags = {\n",
+ " 0:'Recommended Quality',\n",
+ " 1:'Retrieval Attempted',\n",
+ " 2:'Retrieval Successful',\n",
+ " 3:'Undefined'\n",
+ "}\n",
+ "# SMAP RECOMMENDED QUALITY BIT IS 0\n",
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "cax = ax.imshow((ret_flag_L3_P>>0)&1, cmap=plt.cm.get_cmap('bone', 2))\n",
+ "ax.set_title(ret_flags[0])\n",
+ "\n",
+ "cbar = fig.colorbar(cax, ticks=[0, 1], orientation='horizontal')\n",
+ "cbar.ax.set_xticklabels(['Good Data', 'Not Recommended']) # horizontal colorbar\n",
+ "\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6537954f-7f34-4ce9-8e1d-a4af62dc32ec",
+ "metadata": {},
+ "source": [
+ "### Surface Flags \n",
+ "\n",
+ "The different values that the flag can have are listed in the cell below "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c2e7941b-c392-4c91-be1c-5175b5b507a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "surf_flags = {\n",
+ " 0:'Static water body',\n",
+ " 1:'Radar water body detection',\n",
+ " 2:'Coastal Proximity',\n",
+ " 3:'Urban Area',\n",
+ " 4:'Precipitation',\n",
+ " 5:'Snow or Ice',\n",
+ " 6:'Permanent Snow or Ice',\n",
+ " 7:'Frozen Ground (radiometer)',\n",
+ " 8:'Frozen Ground (model)',\n",
+ " 9:'Mountainous Terrain',\n",
+ " 10:'Dense Vegetation',\n",
+ " 11:'Nadir Region',\n",
+ " 12:'Undefined'\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d92e6b62-08c2-4267-ae47-f299569dc206",
+ "metadata": {},
+ "source": [
+ "Now we will plot the surface flags, where black areas indicate no flag and white areas indicate flagged data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "689bfe5b-60e6-4c7c-8a61-63947721537a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i in np.arange(0,12):\n",
+ " fig, ax = plt.subplots()\n",
+ " cax = ax.imshow((surf_flag_L3_P>>i)&1, cmap=plt.cm.get_cmap('bone', 2))\n",
+ " ax.set_title(surf_flags[i])\n",
+ " cbar = fig.colorbar(cax, ticks=[0, 1], orientation='horizontal')\n",
+ " cbar.ax.set_xticklabels(['No Flag', 'Flag Present']) # horizontal colorbar\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e544f7dc-15b4-473a-b09a-043b13a94b97",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/SMAP/03_smap_quality_flags_rendered.ipynb b/notebooks/SMAP/03_smap_quality_flags_rendered.ipynb
new file mode 100644
index 0000000..0f7fa67
--- /dev/null
+++ b/notebooks/SMAP/03_smap_quality_flags_rendered.ipynb
@@ -0,0 +1,408 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e49d9ba5-51ce-40df-9942-7fc29c8aee5e",
+ "metadata": {},
+ "source": [
+ "