0. Giovanni as a Tool for Teaching Remote Sensing Applications
2012 Gregory G. Leptoukh Giovanni Workshop
Rich Kleidman
Science Systems and Applications, Inc
ARSET - AQ
Applied Remote SEnsing Training – Air Quality
A project of NASA Applied Sciences

1. NASA and Earth Science Applied Sciences Program
Applications to Decision Making: Eight Thematic Areas
Agricultural
Efficiency
Air Quality
Climate
Disaster Management
Ecological Forecasting
Public Health
Water
Resources
First I need to tell you a little bit about ARSET. We are a program funded by NASA Applied Sciences to provide professional training and education corresponding to the eight application areas.
The program has been in existence for four years and was originally tasked with providing training in air quality applications. This past year we have also added water resources. Eventually we hope to expand ARSET to cover all eight application areas shown in this slide.
In this presentation I will be dealing exclusively with the air quality part of the program
Weather (Aviation)

2. Who are we training ? Air Quality Managers and Regulators
EPA, state and local regulatory agencies, US Forest Service
Scientists/Technical: Meteorologists, air quality forecasters and modelers, health scientists, AQ researchers
Other/public: project managers, reps. from health agencies, World Bank
Our primary task is to provide training to professionals working as applied end users in air quality. Space permitting we also allow participation from academic institutions both staff and students.

3. Expertise ANY Audience can span a large range in expertise:
- No background in remote sensing and little science background
- No background in remote sensing and some science background
- Introductory expertise with satellite data
- Moderate expertise with satellite data
As you can see we cater to those with little to moderate experience using remote sensing products. We offer both
webinar and in-person courses. The ease of use of makes it an ideal platform for our training activities and target audience.

4. NASA Satellite Products for Air Quality Applications
Particulate Pollution (dust, haze, smoke)
- Qualitative: Visual imagery
- Quantitative*: Column Products and vertical
extinction profiles
Fire Products: Fire locations or ‘hot spots’
Trace Gases
- Quantitative*: Column Products
- Vertical profiles: mostly mid-troposphere
- Some layer products
These are the kinds of products that we focus on in our trainings.

5. Giovanni Instances Used in ARSET-AQ Training
Terra and Aqua MODIS Daily
Aura OMI Level 2G
Aqua/AIRS Global Daily
Air Quality
A-Train Along CloudSat Track
MISR Daily
To teach about the types of products shown in the last slide we frequently make use of the Giovanni instances shown in this slide. They are listed according to the frequency of use.

6. Our principal uses for Giovanni - conceptual organization
First exposure to hands on use of satellite data.
Illustrating proper use of data
Quick access to data for exploratory analysis
Comparison of coincident data sets

7. Introductory Activity
Used to:
Provide a first exposure to satellite data products.
Begin the process of educating our users about how to
evaluate the quality of satellite remote sensing products
and proper and improper uses of data and tools.
Illustrate the strengths and limitations of Giovanni.
The document which provides instructions on how to produce the plots
used in the activity as well as the presentation used to guide the
follow up discussion can be found on our website.
The activity is called “Giovanni Beginning Exploration”
The slides that follow are taken from a presentation and activity that we use on the first day of almost all of our in person courses. I will only be illustrating one of our activities that is centered around Giovanni. We have several others which we use on a regular basis.

8. Evaluating Remote Sensing DataOr
How to Avoid
Making Great Discoveries by Misinterpreting Data
Richard Kleidman
ARSET-AQ
Applied Remote Sensing Education and Training
A project of NASA Applied Sciences

9. It may take a few seconds to really get the idea behind this comic strip. Interpreting data can be a tricky business.

10. AOD at 550 nm Area Average Plot Giovanni MODIS Daily Instance
June , 2008
We first provide instructions on how to generate these plots for a specific set of dates and locations. These are carefully pre-selected to show a large difference between Terra and Aqua. We want this to be
a good jumping off point for a thoughtful evaluation of satellite data and what the Giovanni Tool does and does not do.
Aqua
Daily Overpass ~ 1:30 PM local time
Terra
Daily Overpass ~ 10:30 AM local time

11. Real or Not Real ? You Decide! A Potential Discovery! Terra Aqua
We ask users to first describe the differences produced by the two MODIS sensors and then to spend some time speculating on the potential causes for these differences.
Aqua
Daily Overpass ~ 1:30 PM local time
Terra
Daily Overpass ~ 10:30 AM local time

12. AOD at 550 nm Area Average Plot Giovanni MODIS Daily Instance
June , 2008
Hypothesis 1:
The differences in aerosol concentration represent a Diurnal Cycle with different amounts of aerosol being produced
or transported afternoon and morning.
Hypothesis 2:
The differences in aerosol concentration
are either not real
or not significant and
could be caused by
Sensor error
Algorithm error
Sampling error ?
Since our users are not very experienced at this point we then suggest some possibilities and go through a discussion and evaluation of each one.

13. Possible ways to interpret these differences
Real world differences
Leads to a conclusion(s) about aerosols
Can lead to further research about aerosols
Differences due to other factors
Can lead to false conclusion about aerosols
Need to be explored and understood to avoid
similar problems in the future
- Can lead to advances in remote sensing capabilities!
These are two very broad categories. Note from the last point that even discoveries that don’t reflect real world phenomena can be useful and should not be ignored.

14. Important Factors to Understand
Analysis Tool
Giovanni – provides data in 1 degree resolution
Data Products–
Aqua and Terra Level 2 Products are from a single overpass 10 KM resolution
Aqua and Terra Level 3 Products are global composites in 1 Degree resolution
Here we introduce the idea that the Giovanni tool itself might be a factor in the results.

15. Important Factors to Understand
Mid – latitude 1° x 1° is about 85 Km x 110 Km
~ Km MODIS retrievals possible
~ Km MODIS retrievals possible
Swath Nadir
Edge
Now we can use the results of the Giovanni activity as a springboard for learning some things about the product itself.

16. Important Factors to Understand
One MODIS 10 Km Retrieval
Begins life as
400
.5 km (at nadir)
pixels
And ends as
a product
composed of
12 – 120 pixels

17. Information Necessary to Understand the Results
Data Products–
Aqua and Terra Level 2 Products are in 10 KM resolution
Aqua and Terra Level 3 Products are in 1 Degree resolution
Giovanni provides Level 3 Data in 1 Degree resolution.
Sensor Characteristics –
Aqua and Terra are identical designs
There are some small differences in sensor performance
We begin to review and summarize the factors that could contribute to the differences we saw in the results from the two sensors. We intend that our users will generalize these ideas to all
data sets they will encounter.
Algorithm Details–
Aqua and Terra use the same algorithm.

18. AOD at 550 nm Area Average Plot
June , 2008
Examining other features in this data set and using our
knowledge of the sensor and products can help us to
understand the cause of the differences in mean aerosol.
A blank (white) square has no retrievals for the
entire time period.
The ease of use of Giovanni allows us to have the users go back and plot the results for the same area but a shorter time period. Now we begin to see gaps in the data for Terra and not Aqua.
We use this to lead a discussion about the effects of data sampling. In fact, the reason for the differences in the results between the two sensors is sampling. There are some high
aerosol events that Aqua was able to observe but that Terra was not.
Aqua
Terra

19. Evaluating Data Understand the sensor characteristics
Understand the product details
Understand the data visualization tools
and outputs.
These are the final points we emphasize. Good advice for experienced scientists as well as novices.

20. Giovanni helps us turn indiscriminate
consumers of satellite data products
into connoisseurs