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Follow Your World used to be a convenient way to track satellite imagery over time—often with the expectation that your watch area would keep updating without much effort. If the service is now gone, broken, or no longer returns imagery, it’s not just an inconvenience; it changes your monitoring workflow.
This guide treats “Follow Your World is dead” as a starting signal: rebuild your tracking pipeline using reliable datasets and platforms that still work. You’ll get practical options for manual monitoring, automation, and desktop change detection, plus troubleshooting when your results don’t match what you expected.
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What it means that Follow Your World is dead
When a satellite tracking service is “dead,” it typically means one (or more) of these is true: the web/app interface no longer loads, historical data won’t render, watch/alert features stop updating, or sign-in/support endpoints fail. Even if imagery still exists in the public record, the tracking wrapper may be discontinued.
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Quick triage: confirm the failure mode
Before you abandon everything, confirm what broke. These checks save time when you later compare results across platforms.
1) Does the product fail to load at all?
If the homepage/app won’t open, try a different browser profile (or Incognito) and disable extensions temporarily. If the site fails consistently, it’s likely a service shutdown or auth endpoint issue—not your device.
2) Does it load, but imagery never updates?
If you can view the UI but get no new tiles or no new time steps, the backend ingest or scheduling may have stopped. That still leaves you with the underlying datasets—you just need a new delivery mechanism.
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3) Does only your account fail?
If other users can browse but your account fails, it could be an expired subscription, a legacy auth provider retirement, or a permissions issue. Still, don’t wait—plan a replacement pipeline.
What you actually need when tracking satellite imagery
“Tracking” usually combines four requirements. Different replacement tools cover different parts, so you’ll want to choose based on what you truly need.
- Area of interest (AOI): a polygon, bounding box, or point you monitor.
- Time cadence: daily, weekly, monthly, or “whenever a new cloud-free scene exists.”
- Dataset choice: resolution, revisit time, spectral bands, and coverage.
- Delivery: viewing in a map, exporting images, change alerts, or all of the above.
If you skip any one of these, you’ll see gaps that feel like “the tracking is broken,” even when the platform is fine.
Best replacement options (choose your path)
Pick the approach that matches your tolerance for setup vs automation.
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Use these when you want reliable map viewing and manual review. You’ll often check a time slider and download selected scenes.
- NASA Worldview: excellent for quick looks at global datasets.
- Sentinel Hub EO Browser: strong for Sentinel-based exploration.
- Copernicus data portals: useful for direct scene browsing.
APIs for automated tracking and alerts
Use these when you need repeated collection, export automation, and notifications when imagery changes.
- Google Earth Engine (GEE): powerful for time-series and change detection at scale.
- Sentinel Hub APIs: straightforward for requesting processed imagery regularly.
- USGS + automation: workable if you’re comfortable wiring scripts to requests and exports.
Desktop GIS for repeatable change detection workflows
Use these when you want transparent, inspectable processing steps, including cloud masking and classification workflows.
- QGIS with plugins for Sentinel/Landsat, then run repeatable analysis.
- Python + GDAL when you want full control of raster processing.
Step-by-step: set up tracking with a modern alternative
Define your target area and observation goals
Start with an AOI you can reproduce: a polygon for land monitoring, or a set of points for smaller features. Decide what counts as “a change.” For example: vegetation regrowth, surface moisture, snow cover, flooding, or construction progress.
Pick datasets with the right revisit time
Your cadence is constrained by the dataset—not by the app. As a rule of thumb:
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- Sentinel-2: commonly ~5 days at the equator with both satellites (often longer depending on location and clouds).
- Landsat: ~16 days (with Landsat 8/9 contributing to more frequent composites).
- MODIS: daily-ish, but coarser resolution.
If you expected “daily high-resolution” imagery, you’ll be disappointed with some public datasets. That mismatch is the #1 reason tracking feels “broken.”
Choose a monitoring approach: on-demand, periodic, or event-driven
- On-demand review: you check when you need it.
- Periodic pulls: your system requests imagery every 7 or 14 days.
- Event-driven: your system requests only when criteria are met (e.g., low cloud probability, seasonal window, threshold change).
Automate collection and notifications
For automation, you’re typically choosing between two patterns:
- Store-then-notify: collect new scenes into a folder/bucket, run checks, then notify.
- Compute-then-notify: process imagery and compute a change metric first, then store only the results.
Compute-then-notify usually reduces storage pressure and speeds up review.
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Below are practical starting points. You can mix them—for example, use a portal to validate filters, then automate via API.
Google Earth Engine (GEE)
GEE is a go-to choice when you want repeatable time-series analysis and automated export schedules.
- Go to https://earthengine.google.com/ and sign in with a Google account.
- Open the Code Editor.
- Create an AOI: use
ee.Geometry.Polygonor import a geometry asset. - Pick a collection: common starts include Landsat 8/9 Collection 2 or Sentinel-2.
- Filter by bounds and dates using
.filterBounds(aoi)and.filterDate('YYYY-MM-DD', 'YYYY-MM-DD'). - Add a cloud filter appropriate to the dataset (e.g., using QA bands or cloud probability where available).
- Generate a time-series output (true color, NDVI, or a custom metric) and either:
- Export composites to Drive/Cloud Storage, or compute a change metric and display it in the map.
If you want a concrete “tracking” feel, compute a metric (like NDVI delta) over time and trigger a notification only when the delta crosses a threshold.
Sentinel Hub
Sentinel Hub is designed for requesting processed Sentinel imagery efficiently, which makes it a strong fit for periodic “pulls” and near-real-time monitoring.
- Create a Sentinel Hub account at the Sentinel Hub console.
- Open an AOI in the EO Browser to validate coverage and choose bands/resolution.
- Decide your request type: for many tracking workflows, you’ll request processed imagery (e.g., cloud-optimized composites) rather than raw scenes.
- Use the APIs to schedule requests (manually first): set
timeInterval, bounding geometry, and output size. - Run a small test request for a week-long window to confirm filtering and output quality.
- When results look right, schedule repeated requests (daily/weekly) and write outputs to a bucket/folder.
- Optionally add a change metric stage using a simple script (cloud probability + spectral index thresholds).
Sentinel Hub works especially well when your monitoring is Sentinel-centric and you can accept Sentinel-2 revisit constraints.
NASA Worldview
Worldview is great when you want to quickly see what’s happening and verify changes visually without building infrastructure.
- Go to https://worldview.earthdata.nasa.gov/.
- Search for your location and set your AOI view.
- Select a dataset layer (examples include cloud products, vegetation-related products, and true-color imagery where available).
- Use the time controls to scrub through dates and identify change windows.
- For repeat monitoring, export key snapshots and store them in your own system (since Worldview is primarily a visualization tool).
It’s not usually the most direct “alerting” system, but it’s excellent for validating that the data you want exists and changes as expected.
USGS EarthExplorer + external automation
If you need Landsat-based tracking and you’re comfortable with automation, EarthExplorer gives you access to many official archives. You then schedule requests and exports on your side.
- Open https://earthexplorer.usgs.gov/.
- Set your AOI (map search, coordinates, or upload boundary).
- Choose a dataset (e.g., Landsat collections depending on your time range and needs).
- Set date range and filter for scene quality when available.
- For automation, export the scene list (or use API methods if available to your workflow) and download only the needed scenes.
- Run a post-processing script to generate change metrics and notify you.
EarthExplorer is a reliable source, but it’s your responsibility to turn downloads into “tracking.”
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Common mistakes and troubleshooting
Temporal mismatch (why your “tracked” images don’t update)
If you expected daily updates, you may have chosen the wrong dataset or wrong cadence. Revisit time can be fine, but cloud cover or seasonal windows can prevent usable scenes.
- Try expanding the date filter window (e.g., 30 days instead of 7).
- Switch from single-scene review to composites (when the platform supports it).
Cloud cover and missing scenes
Tracking often fails silently when scenes are too cloudy. You may see empty exports or repeated dates with unusable imagery.
- Use cloud probability products when available.
- Require a minimum clear percentage threshold and skip the rest.
- For indices like NDVI, ensure your platform applies masking consistently.
Coordinate / projection errors
A bad AOI can look like “no imagery.” Common causes include swapped latitude/longitude or polygons that don’t match how the platform interprets geometry.
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- Confirm you’re using WGS84 (EPSG:4326) when importing coordinates, unless the tool explicitly asks for another CRS.
Storage limits and large-area exports
Exports can fail or stall when the AOI is large, the resolution is high, or you request too many bands/time steps at once.
- Start with a small AOI test.
- Reduce output size (pixels) and export only what you need (e.g., RGB + one index).
- Export fewer timestamps, then increase once the pipeline is stable.
FAQ
Is there any way to recover my old Follow Your World watch history?
Only if the service still provides an export. If the backend is down or the accounts are inaccessible, you’ll need to rebuild from scratch using dataset portals/APIs and your original AOI/time targets.
What satellite imagery is best for change tracking?
It depends on your target. Vegetation and land cover changes often use Sentinel-2 (bands in the visible and near-infrared). Flooding can use multi-temporal radar (Sentinel-1) where optical clouds are a problem. For snow and thermal monitoring, consider MODIS or specialized products.
Can I automate alerts without writing code?
Sometimes. Web portals can help you manually review time-series, but true alerts usually require an API, a workflow automation tool, or scripts that compute a change metric and send notifications.
Why do I get different results between platforms?
Differences usually come from cloud masking, compositing rules, band scaling, and processing defaults. Match dataset versions and preprocessing settings as closely as you can.
Do I need the latest images, or is a composite acceptable?
Composites are often better for “tracking” because they reduce cloud gaps. If you need exact day-by-day evidence (e.g., for incident documentation), you’ll need scene-level selection rather than composites.
Bottom Line
Follow Your World being dead doesn’t end satellite imagery tracking—it ends a convenient wrapper. The durable solution is to rebuild your workflow around proven data sources (Sentinel/Landsat/NASA products) and a platform that still supports time-series access.
If you want quick monitoring, use NASA Worldview or Sentinel Hub’s EO Browser. If you want real tracking with thresholds and notifications, move to Google Earth Engine or Sentinel Hub APIs and automate the “collect → compute change → notify” loop.
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