NEO News

View NEO Imagery on a Sphere

There are several companies and organizations that provide spherical projector technology, but the first one I had ever heard of was Science on a Sphere (SOS) created by the National Oceanic and Atmospheric Administration (NOAA). Originally envisioned in 1995 by Dr. Alexander “Sandy” MacDonald and later patented in 2005, these Science on a Spheres (and other variations) are in hundreds of locations across the United States and abroad to help increase understanding of Earth’s complex processes.

Image showing Dr. James Garvin (on right), giving a presentation on "Science On a Sphere" to Yuri's Night guests.
Dr. James Garvin (on right), gives a presentation on “Science On a Sphere” to Yuri’s Night guests. Credit: NASA

In support of these visualization systems, NEO provides an archive of time-lapse global imagery to display in spherical projections located in the NEO SOS Archive. NEO uses the Content Creation Guidelines by NOAA to provide a time series in the preferred projection, image format, and resolution. There is also a labels.txt file available with each dataset to label the images with the correct acquisition, a color bar file to provide context to each time series, and a PIP text file (.SOS) that points to the labels and color bar file along with extra features like fade in time and frame rate. Please see the SOS Remote App manual to learn how to display NEO’s time-series datasets on your display system using the SOS Remote App.

Example directory with supporting files to display time series of Active Fires dataset on the Science on a Sphere.

This service started based on requests and only has part of the NEO archive available. If you see a dataset that is not in the archive on our site and you would like it for your spherical visualization, please send us an email using the Contact Us link below.

Here is a preview of some of the datasets we currently have available in the SOS Archive:

Active Fires from Terra/ MODIS

Image showing Active Fires for September 2020 on a spherical projection.
Image showing Active Fires for September 2020 on a spherical projection.

Aerosol Optical Thickness (Depth) from Terra/ MODIS

Image showing Aerosol Optical Thickness for October 2020 on a spherical projection.
Image showing Aerosol Optical Thickness for October 2020 on a spherical projection.

Chlorophyll Concentration from Aqua/ MODIS

Image showing Chlorophyll Concentration for October 2020 on a spherical projection.
Image showing Chlorophyll Concentration for October 2020 on a spherical projection.

Land Surface Temperature Anomaly (Day) from MODIS

Image showing the Land Surface Temperature Anomaly during the day for October 2020 on a spherical projection.
Image showing the Land Surface Temperature Anomaly during the day for October 2020 on a spherical projection.

Net Radiation from CERES

Image showing Net Radiation for July 2020 on a spherical projection.
Image showing Net Radiation for July 2020 on a spherical projection.

Outgoing Longwave Radiation from CERES

Image showing Outgoing Longwave Radiation for July 2020 on a spherical projection.
Image showing Outgoing Longwave Radiation for July 2020 on a spherical projection.

Rainfall from TRMM

Image showing Rainfall from TRMM for August 2016 on a spherical projection.
Image showing Rainfall from TRMM for August 2016 on a spherical projection.

Reflected Shortwave Radiation from CERES

Image showing Reflected Shortwave Radiation for July 2020 on a spherical projection.

Sea Ice Concentration from SSM/I / DMSP

Image showing Sea Ice Concentration and Snow Extent for November 18, 2020 on a spherical projection.
Image showing Sea Ice Concentration and Snow Extent for November 18, 2020 on a spherical projection.

Sea Surface Temperature from Aqua/ MODIS

Image showing Sea Surface Temperature for August 2020 on a spherical projection.
Image showing Sea Surface Temperature for August 2020 on a spherical projection.

Sea Surface Temperature Anomaly from AQUA/AMSR-E

Image showing Sea Surface Temperature Anomaly for September 2011 on a spherical projection.
Image showing Sea Surface Temperature Anomaly for September 2011 on a spherical projection.

Create and Apply the Right Color Palette in Adobe Photoshop for your Map Visualization (Part 3 of 3)

We have added the NEO color table to a grayscale image, learned how to accommodate the color blind easily with our maps, and now we are ready to build custom color palettes.

Adobe has an online color wheel that is helpful to use when surfing through different colors. If you are unsure what colors to start with, use the color wheel to give you a few ideas and follow these three steps as a guide for applying colors to your map with the wheel:

Step 1. Play around with the different color functions of the wheel to find a palette you would like to work with. You can work with a different hue and saturation of one color or look at three different complimentary colors. The radio buttons on the side of the wheel will help guide your decision-making. The RGB value for each color is at the bottom. Feel free to also mess with the lightness, hue and saturation sliders to get exactly what you want after the color wheel gives you an output. I decided to use the shades function and make a minty green palette. I plan to use these colors for land and then choose a contrasting color for the water.

My color wheel choice for this example.

Step 2. Open the color table back up for the grayscale image and use the same method as before: Select a couple of rows and change the colors to what you selected on the wheel, gradually moving from light to dark down the color table. Or, see what happens when you move from dark to light down the color table. Does it change the message of the map?

Step 3. Save your color palette for future use.

Alright, I know, that was short and easy. But not so fast, we have a couple more things to learn.

Follow these steps to make your own color palette in Photoshop:

Step 1. Open a new project for a clean slate to make palettes on. Do not worry about the canvas size as long as you have enough space to work with.

Step 2. Using the brush tool at a size that is easy to see, pick and paint a color on the canvas that you want on your map. I chose green because that is what I think of when I think of vegetation.

My Canvas so far.

Step 3. Now open the Color Picker back up and select a color that is lighter (less saturated) and move the hue up the color scale a little bit. Repeat this process but in the other direction for your third color. There is a tutorial by Greg Gunn that has a very similar process but is way more detailed. Please check out the video if you need a little more context on choosing the right colors.

I have selected and changed the hue and saturation to a part of the colors I am working with but this is not necessary. Do what is right for you.

Step 4. I have chosen a few colors to work with and am ready to add them to the color table. Clip the canvas to the colors you would like on your map. Go to Image, Mode, and select Indexed Color. Now open up the Color Table under Image, Mode. The colors you have chosen may be scattered around the table.

Step 5. Select one of your lightest colors on the table and add it to the 5th and 6th rows using the RGB values located on Color Picker. I may choose to add the same colors to three rows instead of two but this is a good starting place.

Step 6. Create a lighter color from the one you just filled in the 5th and 6th rows by toggling hue and saturation in Color Picker and add it to the 3rd and 4th rows. Repeat this process for rows 1 and 2.

Step 7. Now pick a second color that is darker than rows 5 and 6 and add it to rows 7 and 8. Repeat this process all the way down.

Step 8. Choose and apply a contrasting color for the last cell to represent water.

Here is what I came up with after Step 8.

Step 9. Save the color table somewhere that is easy to find and open up a new project with the grayscale NDVI map.

Step 10. Change the mode to Indexed Color and open up the Color Table.

Step 11. Load the color table you just created and see what you think. Feel free to change the colors up or maybe even repeat the steps with an entirely new set of colors. This tutorial is not available to get it right on the first try. We simply want to give you the tools you need to make the right map for your needs.

What do you think? Not bad for a first try?

Create and Apply the Right Color Palette in Adobe Photoshop for your Map Visualization (Part 2 of 3)

Now that we have finished part one and understand how the color table provided with each dataset on NEO is applied to each grayscale map, let’s focus on creating custom color palettes that are easy for everyone to see.

Color-blindness is a common condition that prohibits some individuals (mostly men) from distinguishing between colors. Especially, red and green.

“Roughly 1 in 20 people have some sort of color vision deficiency.”

U.S. Department of Agriculture

Luckily, there are plenty of resources that can help with creating color-blind friendly maps. Color Brewer is one great place to start for pairing colors together and we will use the site throughout this part of the series to guide our color decision-making.

If you are unable to see the number 74 in green, you may want to take a color blindness test. Image Credit: Wikipedia

Follow these steps to surf through Color Brewer and customize a color palette that suits your needs and the color-blind:

Step 1. Navigate to the Color Brewer site and make sure the colorblind safe box is selected.

Here is the Color Brewer site with a yellow circle around the colorblind safe button that should be selected.

Step 2. Select 9 classes so you will have plenty of colors to work with for an 8-bit dataset. An 8-bit dataset has 256 values (0-255) which means the color table we are working with is a 16 x 16 grid. This will make more sense when we are looking at the color table in Photoshop. You could select 8 classes so every two rows have a different color, but I like to graduate the color to one row at the end. I encourage you to play around with a few combinations and decide what is best for your map.

Step 3. Instead of the default HEX codes, Select RGB from the drop down.

Step 4. Pick a color scheme. I am going to choose the yellow to green combo under sequential multi-hue. It is similar to what we are displaying now but lighter and I really want the water to be more of a dark blue rather than black.

Step 5. Go back to Photoshop and open the Color Table window again (Image, Mode, Color Table…). To make it easier, my Color Brewer window and Photoshop application are sitting side-by-side on my screen.

My desktop set-up for this tutorial.

Step 6. Select two rows at a time on the color table and change the color using the RBG values that are on Color Brewer for the scheme you selected. Repeat this step as you move down the color bar until you get to the last two rows. Then you can graduate to one row and use the darker colors at the bottom of the scheme for the last two rows. The very last color (0) on the table is the map’s water (technically, it is areas of no data that are also where the oceans are). I chose to make the water a dark blue color rather than black.

Here is what the color table looks like after I have customized the palette and applied it to the map.

Step 7. Save the color table you have created to load to another map if you like what you see.

Spend a little more time trying out different colors and using the options Color Brewer provides. Keep in mind, the map represents a dataset and in this case we are trying to show areas of less and more vegetation. Choose wisely on the colors you want to represent places with dense and sparse vegetation. Next time we will look at creating a custom color palette from scratch and applying it to your map.

Create and Apply the Right Color Palette in Adobe Photoshop for your Map Visualization (Part 1 of 3)

Applying the right color palette to an image is crucial to conveying the right message to your audience. There are obvious no-nos in map-making like, do not color land and water blue because it may look like your entire map is water. Or, do not color a disaster map green because it may convey a message of positivity. This tutorial series will show you how to apply and save different color palettes, but it is important to look into why different colors are chosen, basic color theory, and best practices for choosing the right palette. There is a 6-part series on Earth Observatory published several years ago called the “Subtleties of Color” that is still a great base to start from before making and applying your own palettes. If you already feel comfortable with your knowledge and use of colors, let’s make color palettes!

Each NEO image is natively grayscale and the color table is applied in post-processing to display the colored image on each dataset’s page. Underneath the image is a downloadable Adobe Color Table (ACT) that can be saved and used to create and save color palettes for other grayscale images.

Let’s look at the Vegetation Index (NDVI) MODIS imagery as an example for applying a ready-made color table for the dataset following these steps:

Step 1. Download and save the color palette displayed for the NDVI imagery (filename: modis_ndvi.act) and a grayscale PNG at the temporal and spatial resolution of your choice. I saved the files to my desktop so they would be easy to find.

The yellow circles indicate where the color table and grayscale radio button are located on the vegetation index page.

Step 2. Open Photoshop and load the grayscale NDVI image into a new project.

Step 3. Make sure the swatches window is open. There should be a check mark next to “Swatches” and the window should appear on the top right.

Step 4. In the swatches window, expand the hamburger button on the right and click Import Swatches

Step 5. Navigate to where you saved the ACT file and open the file. You should now see the color palette in the Swatches window under its filename and you will be able to access it later and even reuse some of the colors for your own palette creations.

Screenshot of Photoshop with the grayscale image and ACT file loaded.

Step 6. Now go to Image, Mode, and select Indexed Color. Keep the default settings and click Ok. Your image name should have changed to “index”.

Step 7. Go back to Image, Mode and select Color Table…

Step 8. Select Custom from the drop-down menu at the top of the window and click Load…

Step 9. Navigate to the modis_ndvi.act file, select the file, and click Ok.

Step 10. The color applied to the map should now look the same as what is on our site for the MODIS Vegetation Index dataset.

Here is what my Photoshop screen looks like after step 5.

Now we are ready to move on to part 2 where we will learn how to apply a custom color palette that is color-blind friendly. See you then!

How to Visualize NEO Imagery in Excel

Did you know you can use Excel to visualize raster datasets? If not, follow this short tutorial and find out how.

Let’s use the cloud fraction imagery NEO provides for this example.

Step 1. Go to the cloud fraction imagery page and choose the CSV for Excel download option from the drop-down at 1.0-degree resolution for a month and year of your choice. I am going to download the latest monthly image for August 2020.

This is the cloud fraction imagery page with green arrows pointing to the selections you need to make when you download the CSV file.

Step 2. Open the CSV in Excel and select all data except for the latitude and longitude row and column (which are the first row and first column).

All of the cells except for the latitude and longitude row and column should be selected in this step. Here is an example of what the Excel sheet will look like.

Step 3. Find and replace all 9999 values with an empty cell. I pressed the space bar a couple of times in the Replace with: cell. Once you click the Replace All button, an alert message will come up, and you will notice the cells that previously had 9999 are now empty.

This is an example of where to find and replace the 9999 values.
This alert will come up once you hit the Replace All button.

Step 4. From the Excel home tab: Select conditional formatting, color scales, and choose one of the 2-color scheme options available or select More Rules… and choose a different minimum and maximum value color. I am going to choose blue for the minimum color and white for the maximum color to create a look similar to what is available on the cloud fraction page.

Here is where you need to be for step 4.
For the new formatting rule, I selected blue for the minimum color and white for the maximum color. The maximum value corresponds to the highest cloud fraction while the minimum value represents low to no cloud cover.

Step 5. Zoom out using the slider on the bottom right side of the excel window and you will notice the global imagery taking shape.

Voila! There is your image coming to life in Excel. Now it is time to zoom in and investigate the cloud fraction values at different latitudes and longitudes. You may also want to try repeating the process at a higher resolution.

I remember learning the difference between raster and vector data in my entry-level GIS courses. Vector data is all of the point, line, and polygon data while raster data is made of cells or pixels. I wish my professor would have shown me how to visualize raster data in Excel at the time to really grasp cell values that make up the imagery we see as a whole. It certainly would have been easier to process!

Please share what you process in the comments below. We would love to hear any feedback or suggestions you may have.

Questions asked frequently at NEO

After many years of serving the public with global visualizations of Earth’s system processes, we gathered the most frequent questions visitors of our site ask and answered them for you. The FAQs page can be found on the home page and lives here: https://neo.sci.gsfc.nasa.gov/faq/. Please look these questions over and see if they answer questions you may have had already or discoveries that will help understand our site better.

If you read through the page and still have your own unanswered questions we did not cover, please send us an email using the contact form below and we will make sure you are heard.

Visualizing Changes in Nitrogen Dioxide Levels During the COVID-19 Pandemic

On March 11, 2020, COVID-19 was classified as a global pandemic by the World Health Organization. That same month, all New York City non-essential businesses were ordered to close by the governor’s office and several residents fled the city to get away from the rapidly spreading virus. There is typically a significant amount of nitrogen dioxide (NO2) in the air from the burning of fossil fuels during mass transportation, especially in larger cities like New York City. But, because all of the non-essential businesses were closed, along with many transportation lines, there was a significant decrease in NO2 in March 2020 compared to previous years.

The data probe function in the NEO analysis tool shows a significant decrease in NO2 levels in March 2020 compared to March 2018 & 2019.

By adding the Nitrogen Dioxide dataset to the analysis tool for March 2018, 2019 & 2020, we can compare NO2 levels over one geographic coordinate using the data probe function or over a distance using the plot transect function. For more information on how this is done, check out our post on NEO Analysis in 10 Easy Steps. According to these New York City snapshots, NO2 levels decreased by roughly half in comparison to the previous 2018 and 2019 average NO2 levels when city operations were normal.

A quick draw of a transect line using the plot transect function shows a decrease in NO2 levels in March 2020 compared to March 2018 & 2019 over New York State.

The Governor of Sao Paulo, Brazil, Joao Dorio, also ordered a shutdown of the state for two weeks at the end of March 2020 to help slow the spread of the virus. The NO2 levels in April 2020 in comparison to the previous two years also decreased by nearly half.

Here is a snapshot of South America with the data probe floating over Sao Paulo, Brazil to compare NO2 levels in April 2018, 2019 & 2020.

Global human behavior changed rapidly as COVID-19 spread across the globe and the change can be detected from satellites in space. NASA scientists are monitoring several atmospheric indicators globally, including NO2, to read a global pulse on how our atmosphere is responding. Although NEO datasets are heavily processed for visualization and should not be used for scientific analysis, we can still qualitatively see changes on a global scale.

Global snapshot of NO2 levels in March 2020.

NEO FTP Service No Longer Available

Effective June 13, 2019, the NEO File Transfer Protocol (FTP) service is no longer available. It has been replaced with access via HTTPS.

NEO FTP Service is Retiring

NASA is in the process of deprecating the use of FTP protocol for file access across the agency. As a result, downloading files from NEO via FTP may no longer be available after April 15, 2019. NEO will support bulk downloading options via HTTPS, but FTP client software applications will no longer be able to access NEO holdings. Similarly, if users are using FTP command line utilities or scripts to download from the archive, those will need to be converted to using HTTPS-access methods.

The directories containing the bulk files can be found at https://neo.sci.gsfc.nasa.gov/archive/ Additionally, you may also use our Web Mapping Service (WMS).

For users who have scripts or command line experience, we recommend using either wget or curl to facilitate downloading from the bulk archive. There is quite a bit of documentation and examples that can be found simply by searching, or even just looking at the wget man page, but here are a couple wget examples:

If you want to maintain an up-to-date mirror of a specific directory, retrieving only the PNG files:

wget --no-directories --no-host-directories --no-parent --recursive --mirror --accept "*.PNG" -l1 https://neo.sci.gsfc.nasa.gov/archive/rgb/MOD_LSTD_M/

Same as above, but get only the images from 2007 (for example):

wget --no-directories --no-host-directories --no-parent --recursive --mirror --accept "*2007*.PNG" -l1 https://neo.sci.gsfc.nasa.gov/archive/rgb/MOD_LSTD_M/

If you are not comfortable using command line utilities to download NEO imagery, there is a growing number of graphical interfaces to facilitate downloading over HTTPS. Some are stand-alone applications, some are browser plugins. You can find these applications by searching on “download multiple files from website.”

Analysis: Pacific life – how is it related to ocean temperature?

Note that these examples are intended for curious people looking for hands-on Earth data exploration. Primary scientific research will require additional analyses through other methods. For the basics on how to use the NEO tool, see ‘Analysis tool in 10 easy steps’.

Here we explore phytoplankton blooms and their relationship to sea surface temperatures, with background information featured in ClimateBits: Phytoplankton.

Recent studies link warmer waters off the U.S. west coast to more frequent toxic algae blooms, negatively impacting the marine food web and the economy. In 2014-16, the waters off the west coast were unusually warm and were famously dubbed the ‘warm blob’ by the press. The warmer ocean impacted weather on the west coast and was linked to lower fish catches and stressed sea life.

A toxic algae bloom in 2015 extended from California to Alaska resulting in the closure of the Dungeness crab fishery and an economic decline of $100 million, according to the Fisheries of the U.S. Report, 2015. Sea lion strandings increased, including a starving baby sea lion that seated itself at a San Diego restaurant in early 2016, weighing half of what it should for its age according to the Sea World rescue team.

Following the strong El Niño of 2015-16, ocean temperatures off the west coast returned to ‘normal’. Here we use NEO to explore these reports. How do the satellite sea surface temperature records compare before, during, and after the warm anomaly?

Figure 1. North Pacific Sea Surface Temperatures during February 2013 (red), February 2015 (green), February 2017 (blue). Transect values from NW to SE along the U.S. west coast.

A NEO comparison of ocean surface temperatures for the month of February before the warm anomaly in 2013 (red), during the warm anomaly in 2015 (green), and after the warm anomaly in 2017 (blue). Along the entire west coast – from Alaska to the Baja Peninsula – temperatures during the warm blob (February 2015) were roughly 3 degrees C (or 5 degrees F) warmer compared to before (February 2013) and after (February 2017).

Temperatures off of Alaska (Distance ~ 0km along the transect) were around 7C in February 2013 and 2017, but around 10C in 2015. Off of southern California (Distance ~ 2000km), temperatures were around 13C in February 2013 but 16C during the warm blob in 2015. West of the Baja Peninsula (Distance ~ 3500km), temperatures were around 21C in 2013 and 2017, but 25C in 2015.

How do the temperature changes relate to ocean biology measured by satellites?

Figure 2. North Pacific chlorophyll concentrations during February 2013 (red), February 2015 (green), and February 2017 (blue) plotted in a histogram for the area west of California outlined in white.

Chlorophyll concentrations indicate the amount of phytoplankton blooming. More phytoplankton means more food for fish and the rest of the marine food web. In the chlorophyll histogram in Figure 2, chlorophyll during the warm blob in February 2015 (green) had lower values (around 0.1 mg/m3) more frequently than the other two years. The waters were almost 10 times more productive (approaching 0.9 mg/m3) in February 2013 (red) compared to the other two years. Recall that February 2013 had the coolest water.

Usually, cooler surface water means that the water has recently been at depth — below the sunlit surface. Deep water containing unused nutrients can support new phytoplankton blooms. Thus, cooler water is generally associated with higher chlorophyll concentrations. How do the two data sets compare along the west coast before, during, and after the warm blob?

Here we compare sea surface temperature and chlorophyll along a transect from NW to SE off the coast of California for February 2013, 2015, 2017.

Figure 3. Sea surface temperature (red) and chlorophyll (green) plotted along the white transect line in the large panel, from northwest to southeast for February 2013 (left), February 2015 (middle), February 2017 (right) – before, during, and after the warm blob, respectively.

In all of the plots in Figures 3, sea surface temperature and chlorophyll demonstrate their inverse relationship. Cooler, more productive water to the north is contrasted with warmer, less productive water toward the south. The peaks in the chlorophyll (green line) correspond to phytoplankton filaments typically associated with nutrient entrainment along the boundaries of circulation features, such as in the California Current system. Note that over the 2000km transect from northwest to southeast, temperatures changed about 10C and chlorophyll concentrations changed more than an order of magnitude (10x). Also notice that February 2013 (Figure 3, left) had chlorophyll peaks reaching concentrations around 5 mg/m3. During the warm anomaly in 2015, chlorophyll concentrations were never above 0.9 mg/m3. After the demise of the warm blob, sea surface temperatures cooled in 2017 (Figure 3, right) compared to 2015 (Figure 3, middle), chlorophyll concentrations remained low (< 0.9 mg/m3) and were certainly much lower than in 2013.

Diving into the 2017 data a bit more through scatter plots, we can highlight the geographical distributions of different data combinations.

Where are the highest chlorophyll concentrations?

Figure 4. Scatter plot of sea surface temperature (bottom axis) versus chlorophyll (left axis) during February 2017 for the region within the white line. The highest chlorophyll values (magenta box on the scatter plot) are highlighted in magenta on the map. Note that the values at the very top of the plot (74mg/m3) are outliers or artifacts.

Where are the warmest waters within the area outlined in white?

Figure 5. Same plot as Figure 4, with the magenta area highlighting a different distribution of temperature (16-21C) and chlorophyll values (0.05-0.2 mg/m3).

Where are the coolest waters within the area outlined in white?

Figure 6. Same plot as Figure 4 and 5, with the magenta area highlighting a different distribution of temperature (7-10C) and chlorophyll values (0.2-0.8 mg/m3).

Not surprisingly, the coolest waters are in the north; the warmest waters are in the south and the most productive waters with the highest chlorophyll values are next to the coast where nutrients were plentiful. Recall that January and February 2017 was a time of plentiful rain and snow on the west coast (a.k.a. atmospheric rivers that led to much run-off from land).

Note: This blog was written in response to a request for an analysis comparing sea surface temperature and chlorophyll. If there is an analysis you would like to see in this blog, please let us know! 

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