Nanosatellites and Space Data

Small satellites make space affordable. Learn the building blocks. You will describe a nanosatellite's main parts and size classes, so you can judge what a small craft can and cannot observe.

• Classes by mass: nano is roughly 1 to 10 kg; a common cube is 10 cm per side.
• Parts: power from panels, computer, radio, sensors and a way to point.
• Orbit height decides how often it passes and how sharp the picture is.
• Why many small satellites together can give frequent views.
• Compare sizes: a one-unit cube measures about 10 centimetres per side, and several units can be joined, so a three-unit craft is roughly 30 centimetres long.
• Note that small craft usually carry one or two sensors for a narrow job, and trade size for numbers, so a group of ten can revisit a place far more often than one large craft.
• Compare a nanosatellite with a large one on mass, cost and lifetime; small ones often last one to five years.
• Learn why antennas and batteries are the usual limits.

Activity. Draw a block diagram with arrows showing power, data and commands.

Standard to meet. Diagram shows all main parts and what flows between them.

Common mistake. Thinking "nano" means weak; many missions only need small, cheap sensors.

Check yourself. What is one thing a small satellite can do well and one thing it cannot?

An orbit decides what is seen and when. You will predict passes over your location and relate orbit height to revisit time, so you know when fresh data can realistically exist.

• Low Earth orbit is a few hundred kilometres; one lap takes about 90 minutes.
• Revisit time: how long until the satellite sees the same place again.
• A radio pass over a ground station lasts only minutes, so data waits on board.
• Time of day affects light and shadow.
• A craft in low orbit circles Earth in about 90 minutes at roughly 500 kilometres height, so it passes a given spot only a few times a day or less.
• Use a free pass-prediction page to list the next five passes over your location, and note time, maximum height in the sky and duration in minutes.
• Use a free sky-tracking tool to see real passes of small satellites over your town.
• Note that cloud over the ground station does not stop radio, but weather can affect some links.

Activity. Find a free pass predictor and note three passes over your town.

Standard to meet. Record three passes with time and direction.

Common mistake. Forgetting that revisit time limits how fast you can see change.

Check yourself. What revisit time does your question need?

Different light bands reveal different things. You will choose a light band and pixel size that fit a question, avoiding images too coarse or too costly for the subject.

• Visible bands show what the eye sees; near-infrared shows plant health.
• Resolution is the size of one pixel (one small square of the image). A pixel can be 10 m or 3 m wide.
• Radar sees through cloud and at night.
• Match sensor to question.
• Match band to question: visible light for roads and buildings, near-infrared for plant vigour, thermal for heat, and radar when cloud blocks the view.
• Check pixel size before downloading: a 10-metre pixel cannot show a single car, but it can show a whole field's greenness, so match scale to subject.
• Check the width of the area each image covers (the swath); wider means fewer passes.
• Compare spectral bands on a sample image and write what each shows.

Activity. List four questions and the best sensor for each.

Standard to meet. Match each question to a sensor with a reason.

Common mistake. Picking a sensor because it is famous, not because it fits.

Check yourself. Which sensor did you pick and why?

Open data is free but has licences and labels. You will find open scenes with low cloud, read their metadata and respect their licences, so every dataset you use is documented and citable.

• Search public archives; choose area and dates.
• Record the source, licence and date for every file.
• Open the image in a free tool; check bands and coordinates.
• Keep files small by cropping to your area.
• Search an open archive for your area and date range, filter cloud cover to under 20 percent, and save the scene identifier in your notes for later citation.
• Read the metadata file for date, sensor, pixel size and processing level before opening the image, because wrong assumptions here carry into every later step.
• Keep a data log: file name, date, cloud percent, size and licence.
• Use small test crops before downloading a whole scene.

Activity. Download two dates for your area and record the licence.

Standard to meet. Source and licence recorded; files open correctly.

Common mistake. Ignoring licence terms on open data.

Check yourself. What is the licence of your data?

Compare two dates with a simple index. You will compare two same-season dates with a simple index and a stated threshold, separating real change from ordinary seasonal growth.

• A plant index is (near-infrared minus red) divided by (near-infrared plus red).
• Values near 0 are bare ground; higher values are greener.
• Check cloud and season before you compare.
• Compare same season in different years.
• Compare images from the same season, such as two Junes a year apart, so natural growth cycles do not look like real change.
• Compute a simple index for both dates, subtract one from the other, and mark pixels that change by more than a stated threshold such as 0.2.
• Plot your index values on a simple chart; a picture of change is more persuasive than a table.
• Repeat the calculation for a control area that should not have changed.

Activity. Calculate the index for 10 sample points on two dates.

Standard to meet. 10 points calculated; one clear change found.

Common mistake. Comparing a wet-season image to a dry-season one.

Check yourself. Are your two dates in the same season?

Every dataset has limits. Name them. You will list the limits of your data, such as cloud, resolution and missing ground checks, and test one result against a local source.

• Cloud and haze block views; mark them.
• Resolution limits what you can claim.
• Revisit gaps hide short events.
• Ground truth: check with a walk or local report.
• Write a limits box listing cloud cover, pixel size, revisit gap and any ground truth you lack, each with one line on how it could mislead.
• Where you can, check one result against a photo, a map or a local source; even a single ground check raises confidence more than another hour of processing.
• Write one sentence per limit saying how it could mislead a decision.
• Ask a local expert whether the pattern matches what they see on the ground.

Activity. Write a limits table with five rows.

Standard to meet. Give each row a limit, an effect and a check.

Common mistake. Hiding the limits.

Check yourself. What is your biggest limit?

Decision makers need the answer, a picture and a next step. You will condense an analysis into a one-page story with a question, a labelled map, three findings and a recommendation a decision maker can use.

• Headline sentence with a number.
• Two maps: before and after, same scale.
• Three bullet findings with units.
• Limits and one recommended action.
• Structure the page as question, one map or chart, three findings in plain words and one recommendation, and keep the whole page under 250 words.
• Label every chart with date, source and units, and add a scale bar, so nothing on the page needs a spoken explanation.
• Write the headline last, after you have seen all results.
• Use colour scales that are readable for colour-blind viewers.

Activity. Create your one-page story.

Standard to meet. Answer first; maps same scale; limits stated.

Common mistake. A map without a scale or date.

Check yourself. Do both maps share scale and legend?

Test the story with a real reader and plan growth. You will test your story on a real reader, log what you would change, and set practical goals for widening your skills.

• Show it to a decision maker; ask what they would do next.
• Give two peers written feedback.
• Share your data-source list with the next learner.
• Plan 90 days: another area, another index, a skill to add.
• Keep a log of each project with question, data used, time taken and what you would change, and review it after every third project.
• Set three 90-day goals, such as one new sensor type, one local partner who uses your results and one published page, and check them monthly.
• Offer the reader a follow-up in 30 days with a new image date.
• Add your method as a short recipe so someone else can repeat it.

Activity. Run the review and write the plan.

Standard to meet. Record a reader quote and date the plan.

Common mistake. Ending without a decision.

Check yourself. What should the reader do next?

Your chosen month has 60% cloud.

Question: What do you do?

Model answer: Choose clearer dates, use radar if available, or say the question cannot be answered with that month.

A farmer asks about a 400 square metre plot with 10 m pixels.

Question: What is the problem?

Model answer: The plot is about 4 pixels, too few to judge; advise a different data source or ground survey.

On the slide: L1 Basic · Basics of space

Notes: Explain satellite and orbit in plain words, and why some satellites move across the sky while others hover over one place. Notes: use a sky-watching app to find one pass. Standard: three sentences a friend can repeat.

On the slide: L2 Awareness · Awareness of space data

Notes: Describe what open satellite data exists (images, weather, ocean, land cover), who publishes it and what licence it carries. Notes: browse a public archive together and write down the licence. Standard: three datasets named with what each shows and its licence.

On the slide: L3 Foundation · First data task

Notes: Open and read an image: find a place, read its bands or colours, and note its date, scale and cloud cover. Notes: practise with a small crop of your own town. Standard: a note listing date, resolution and one limit of the image.

On the slide: L4 Practitioner · Analyst basics

Notes: Compare the same place on two dates and describe the change, stating cloud, season and resolution limits. Notes: spreadsheet only, with a table of the numbers you read. Standard: change described in a sentence with a number and one reason it may be wrong.

On the slide: L5 Specialist · Specialist

Notes: Answer one real land, water or weather question with free open satellite data and present the result with its limits. Notes: choose a question you can check on the ground, such as a lake edge or a green area. Standard: question, data source, method, result and three stated limits on one page.

On the slide: L6 Advanced · Advanced analyst

Notes: Build a time series and automate the repeated steps with free tools, with a data log and a check against ground truth. Notes: script the download and the table, and test the script on a second place. Standard: the series covers at least 12 dates and the script runs unaided on another place.

On the slide: L7 Super expert · Super expert

Notes: Lead method and teach teachers. Notes: review data sources yearly.