![](\"images/World_Mosaic_ALOS2.jpg\")
\n",
"Figure 1. The ALOS global mosaic combining ALOS and ALOS-2 sensors.\n",
"\n",
"### Let's explore further by loading data. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import modules \n",
"You can execute a cell by clicking on it and pushing control+enter"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"import datacube # Load the datacube library\n",
"import datetime\n",
"import numpy as np\n",
"import json\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def figure_ratio(ds, fixed_width = 20):\n",
" width = fixed_width\n",
" height = len(ds.y) * (fixed_width / len(ds.x))\n",
" return (width, height)\n",
"\n",
"\n",
"def callback(event):\n",
" global pX, pY\n",
" pX, pY = int(event.xdata + 0.5), int(event.ydata + 0.5)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Available ALOS products\n",
"The `list_products` method in the Datacube class displays the names and details of all available products. In the below cell we will query what ALOS products are currently indexed in our instance."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| \n", " | description | \n", "product_type | \n", "crs | \n", "resolution | \n", "spatial_dimensions | \n", "
|---|---|---|---|---|---|
| name | \n", "\n", " | \n", " | \n", " | \n", " | \n", " |
| alos2_palsar_AMA_ingest | \n", "ALOS-2 PALSAR mosaic tiles generated for use i... | \n", "gamma0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "
| \n", " | product | \n", "measurement | \n", "aliases | \n", "dtype | \n", "flags_definition | \n", "name | \n", "nodata | \n", "units | \n", "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", "alos2_palsar_AMA_ingest | \n", "hh | \n", "NaN | \n", "int16 | \n", "NaN | \n", "hh | \n", "0 | \n", "DN | \n", "
| 1 | \n", "alos2_palsar_AMA_ingest | \n", "hv | \n", "NaN | \n", "int16 | \n", "NaN | \n", "hv | \n", "0 | \n", "DN | \n", "
| 2 | \n", "alos2_palsar_AMA_ingest | \n", "date | \n", "NaN | \n", "int16 | \n", "NaN | \n", "date | \n", "0 | \n", "1 | \n", "
| 3 | \n", "alos2_palsar_AMA_ingest | \n", "incidence_angle | \n", "NaN | \n", "uint8 | \n", "NaN | \n", "incidence_angle | \n", "0 | \n", "1 | \n", "
| 4 | \n", "alos2_palsar_AMA_ingest | \n", "mask | \n", "NaN | \n", "uint8 | \n", "{'cloud_confidence': {'bits': [0, 1, 2, 3, 4, ... | \n", "mask | \n", "0 | \n", "1 | \n", "
![](\"images/Direct_Back_Scatter.jpg\")
\n",
"Figure 2. Example of direct back scatter.\n",
"\n",
"### Horizontal and Vertical Polarisation.\n",
"\n",
"The strength of direct back scatter is measured in polarisation, current spaceborne radar systems operate with linear polarisation, where the radar signals are transmitted and received at horizontal (H) and/or (V) polarisation.\n",
"\n",
"The first two bands of the mosaic is backscatter, and in the case of the ALOS-2 Global Mosaic product there are two types of backscatter measurements,\n",
"\n",
"HH: Transmission of horizontal wave; Reception of horizontal component.\n",
"\n",
"HV: Horizontal transmission; Reception of Vertical component,\n",
"\n",
"![](\"images/lin_vert.big.gif\")
\n",
"Figure 3. Vertical Polarisation\n",
"![](\"images/lin_horiz.big.gif\")
\n",
"Figure 4. Horizontal Polarisation\n",
"\n",
"\n",
"### DN Units\n",
"\n",
"Data are stored as digital number (DN) in unsigned 16 bit. The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation:\n",
"0 = 10 log10β©π·π2βͺ + πΆπΉ\n",
"\n",
"where, CF is a calibration factor, and <> is the ensemble averaging. \n",
"The CF values are β-83.0 dBβ for the PALSAR-2/PALSAR mosaic and β-84.66 dBβ for the JERS-1 SAR mosaic.\n",
"\n",
"This means that the final conversion is,\n",
"\n",
"![](\"images/DN.png\")
\n",
"\n",
"Let's explore the direct back scatter data over a Columbian rain forest.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"