diff --git a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial1.ipynb b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial1.ipynb index 43d4c091..1efc3507 100644 --- a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial1.ipynb +++ b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial1.ipynb @@ -281,7 +281,7 @@ "- **Medium-Earth orbit** (approximately 2,000 to 35,500 km above Earth): this type of orbit is often used by the [Global Positioning System (GPS)](https://en.wikipedia.org/wiki/Global_Positioning_System) satellites.\n", "- **High-Earth orbit** (above 35,500 km above Earth): Satellites in this orbit are typically geostationary. They maintain a fixed position above a specific location on Earth's surface. NOAA, for instance, operates two geostationary satellites that provide observations of the western hemisphere every five minutes and targeted observations of severe weather events every 30 seconds.\n", "\n", - " ## ![Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022.](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg) |\n", + "![Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022.](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg)\n", "\n", " Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022. (Credit: [NOAA](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg) )\n" ] @@ -314,13 +314,13 @@ "\n", "For instance, the Airborne Visible/Infrared Imaging Spectrometer ([AVIRIS](https://aviris.jpl.nasa.gov/)) shown below records data in 224 spectral channels. These sensors enable us to differentiate between various rock and mineral types, vegetation types, water quality, and other features.\n", "\n", - "## ![Sample image of AVIRIS over a lake and evaporation pond.](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa) |\n", + "![Sample image of AVIRIS over a lake and evaporation pond.](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa)\n", "\n", "(Credit: [NASA JPL](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa))\n", "\n", "**Radiometric resolution** quantifies the amount of information contained in each pixel and is often expressed as the number of bits representing the recorded energy. Each bit records an exponent of power 2. For instance, an 8-bit resolution equals $2^8$, implying that the sensor can utilize 256 potential digital values (0-255) to store information. The image below from NASA Earth Observatory illustrates how radiometric resolution affects the level of detail visible in remotely sensed data.\n", "\n", - "## ![Illustration of the impact of radiometric resoultion on remotely sensed data.](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ) |\n", + "![Illustration of the impact of radiometric resoultion on remotely sensed data.](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ)\n", "\n", "Credit: [NASA](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ)\n", "\n", @@ -405,7 +405,7 @@ "\n", "The answers to these questions bear significant implications for local water availability, thereby influencing crucial areas such as agriculture, wildlife conservation, and energy consumption.\n", "\n", - "## ![modis snow](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_snow.gif?raw=true)|\n", + "![modis snow](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_snow.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MOD10C1_M_SNOW)\n" ] @@ -426,7 +426,7 @@ "\n", "These applications have strong implications on communities through food security, culture activities, and other economic activities.\n", "\n", - "## ![modis ndvi](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_ndvi.gif?raw=true)|\n", + "![modis ndvi](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_ndvi.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MOD_NDVI_M)\n" ] @@ -449,7 +449,7 @@ "\n", "Clouds are one of the major sources of uncertainty in future climate projections. Satellite data is valuable to help us deepen our understanding of cloud processes to better incorporate the effects of clouds in climate models.\n", "\n", - "## ![modis cloud](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_cloud.gif?raw=true)|\n", + "![modis cloud](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_cloud.gif?raw=true)\n", "\n", "Cloud fraction, or the portion of a pixel covered by clouds. Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MODAL2_M_CLD_FR)\n" ] @@ -469,7 +469,7 @@ "- Quantify the variations in rainfall rates across different regions over time. This information can support water resource planning and management to meet the needs of local communities.\n", "- Investigate the frequency and potential causes of extreme rainfall events and examine their impacts on societal and natural systems. Examples include studying hurricanes and extreme rainfall events.\n", "\n", - "## ![rainfall](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_gpm_imerg_rainfall.gif?raw=true)|\n", + "![rainfall](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_gpm_imerg_rainfall.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/GPM_3IMERGM)\n" ] @@ -490,7 +490,7 @@ "- Monitoring SST is essential for studying large-scale ocean circulation and climate variability, such as the El Niño-Southern Oscillation (ENSO). Changes in SST patterns can provide valuable information about the occurrence and strength of ENSO events, which have significant impacts on global weather patterns.\n", "- SST plays a pivotal role in the study and prediction of tropical cyclones. Warm SST provides the necessary energy for the formation and intensification of tropical cyclones, making SST data critical for understanding and forecasting these powerful storm systems.\n", "\n", - "## ![modis sst](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_sst.gif?raw=true) |\n", + "## ![modis sst](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_sst.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MYD28M)\n" ] @@ -510,7 +510,7 @@ "- How have phytoplankton populations changed in response to shifting sea surface temperatures in different ocean basins?\n", "- By combining phytoplankton data with socioeconomic information, can we gain a comprehensive understanding of how the changing climate affects the fishing industry and the communities reliant on it.\n", "\n", - "## ![modis chl](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_chl.gif?raw=true) |\n", + "![modis chl](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_chl.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MY1DMM_CHLORA)\n" ]