How to Monitor Any Area with Satellite Time Series: A Complete Guide

What Is Satellite Area Monitoring?

Satellite area monitoring is the practice of repeatedly measuring the same geographic region using satellite data and plotting the results over time. Rather than analyzing a single snapshot, you build a time series — a sequence of measurements spaced days or weeks apart — that reveals how a location is changing.

This technique is fundamental to modern environmental science, agriculture, disaster response, and infrastructure surveillance. The same method that scientists use to track Amazon deforestation is now accessible to anyone with a browser.

Why Time Series Beats Single Images

A single satellite image tells you what a place looks like on one day. A time series tells you the trajectory — is this forest slowly declining? Did the flood waters recede? When exactly was the harvest?

Approach	What You Learn
Single image	Current state
Two images (before/after)	Change between two moments
Time series (12+ images)	Trend, seasonality, anomalies, rate of change

Time series also handles cloud cover better. If one overpass is obscured, the trend from surrounding dates still tells the story.

Choosing the Right Index

Different applications require different satellite indices. The choice depends on what you want to monitor:

Vegetation and Land Cover

• NDVI — Most widely used. Green vegetation absorbs red and reflects near-infrared. Values drop when plants are stressed, harvested, burned, or cleared.
• EVI — Enhanced Vegetation Index. More sensitive in densely vegetated areas where NDVI saturates.
• SAVI — Soil-Adjusted Vegetation Index. Better in arid or sparse vegetation zones where bare soil contaminates NDVI.
• NBR — Normalized Burn Ratio. Best for detecting fire damage and tracking post-fire recovery.

Water

• NDWI — Detects open water bodies. Watch lakes, reservoirs, and floodplains.
• MNDWI — Modified NDWI using SWIR instead of NIR. More accurate in urban areas with buildings near water.
• NDMI — Moisture content in vegetation canopy. Useful for drought early warning.

Built Environment and Urban

• NDBI — Normalized Difference Built-Up Index. Increases as impervious surfaces replace vegetation.

Snow and Ice

• NDSI — Normalized Difference Snow Index. Tracks snowpack extent and seasonal melt timing.

SAR (Synthetic Aperture Radar)

• VV / VH Intensity — Raw radar backscatter. Changes with surface roughness, moisture, and vegetation structure. Works through clouds.
• RVI — Radar Vegetation Index. Sensitive to vegetation density and biomass.
• RFDI — Radar Forest Degradation Index. Responds to forest disturbance even under closed canopy.
• CR — Cross Ratio (VH/VV). Highlights double-bounce scattering from flooded vegetation.

Nighttime Lights

• DNB — Day/Night Band from VIIRS. Tracks economic activity, electrification, conflict impacts, and post-disaster recovery.

Ship Detection

For maritime monitoring, automated ship detection on Sentinel-1 SAR imagery provides vessel counts per scene — useful for port activity monitoring and fishing effort estimation.

Setting Up a Monitoring Zone

Step 1: Define Your Area of Interest

You have two options:

1. Draw a polygon directly on the map. Click to add vertices, double-click to close.
2. Upload a GeoJSON file with your pre-defined polygon or multipolygon.

Size constraints: Monitoring zones up to 5,000 km² are supported. For reference, 5,000 km² is roughly the area of a small country like Luxembourg, or a region 70 × 70 km. For most applications — a single farm, forest patch, or urban district — you will be well within this limit.

Step 2: Select Indices to Track

You can select multiple indices for the same polygon in one registration. This is useful when you want to monitor the same area using both optical and SAR data simultaneously — for example, NDVI from Sentinel-2 and VV backscatter from Sentinel-1.

When multiple indices are selected, each creates a separate monitoring entry, but they share the same geographic boundary and can be viewed in a combined graph.

Step 3: Set the Analysis Start Date

The start date determines how far back the system searches for satellite scenes. A longer period gives more context but takes more time to analyze and consumes more tokens.

Practical guidelines:

• Seasonal monitoring (crops, phenology): Start 12–18 months ago to capture at least one full seasonal cycle
• Event response (recent flood, fire): Start 3–6 months before the event
• Long-term trend (deforestation, urban growth): Start 2–5 years ago

Step 4: Confirm Token Estimate

Before the analysis runs, the system estimates how many satellite scenes fall within your area and time window, and calculates the expected token cost. Review this estimate before confirming.

Sentinel-1 and Sentinel-2 have a 6-day and 5-day revisit cycle respectively at mid-latitudes, so a 12-month window typically yields 50–70 scenes per sensor.

Reading the Time Series Graph

Once the analysis completes, results appear as an interactive line graph with time on the x-axis and index value on the y-axis.

Key Features of the Graph

Trend line — The overall direction tells you whether conditions are improving or deteriorating over the monitored period.

Seasonal cycles — Vegetation indices typically show strong annual cycles. Summer peaks and winter troughs are expected; it is the deviations from this baseline that matter.

Anomalies — Data points flagged as statistically unusual are highlighted in the graph. These indicate events worth investigating: a sudden NDVI drop might signal deforestation or crop harvest; an unexpected spike in VH backscatter might indicate flooding.

Coverage information — Each data point includes how much of your polygon was covered by the satellite scene. For ship detection, scenes with partial coverage include a correction option to extrapolate the count to full coverage.

Interpreting Common Patterns

Pattern	Possible Interpretation
Gradual NDVI decline	Vegetation stress, drought, gradual clearing
Sharp NDVI drop + slow recovery	Fire, logging, or flood event
Annual NDVI cycle with consistent peaks	Agricultural field with seasonal crops
Step change in VV backscatter	Construction, flooding, or major land clearing
NDWI spike	Flooding or reservoir filling
NDWI decline	Drought, reservoir drawdown
DNB increase	New development, economic growth
DNB decrease	Power outage, conflict, evacuation

Investigating Anomalies

When the graph shows an interesting event, click on that data point to open the corresponding satellite scene directly in the map. You can then:

• View the full scene in true color or any band combination
• Compare with neighboring dates to confirm the anomaly
• Overlay reference layers (administrative boundaries, water bodies) for context

This seamless connection between time series statistics and direct imagery inspection is what makes satellite monitoring actionable rather than just informational.

Adjusting the Analysis Period

After initial setup, you can extend or shorten the analysis start date:

• Extending backward (earlier start date): The system analyzes additional historical scenes and adds them to the graph
• Shortening forward (later start date): Removes old data points and trims the graph

This is useful when you want to zoom in on a specific period after initial exploration, or when you realize you need more historical context.

Best Practices

Match the satellite to the monitoring goal. For vegetation, Sentinel-2 optical is generally superior in quality but affected by clouds. Sentinel-1 SAR is cloud-independent but requires understanding radar behavior. Combining both — one for optical quality, one for all-weather coverage — gives the most complete picture.

Use consistent analysis periods when comparing sites. If comparing two forests, analyze both from the same start date so seasonal phase differences don't confuse the comparison.

Expect gaps in optical data in tropical and polar regions. Cloud cover can create multi-week gaps in Sentinel-2 time series. SAR data fills in these gaps but requires different interpretation.

Anomalies need ground truthing. An anomaly in the graph is a hypothesis, not a conclusion. Always verify with the imagery and, where possible, independent sources.

Summary

Satellite area monitoring transforms satellite imagery from static snapshots into a continuous surveillance system. By tracking indices over time, you can detect gradual degradation before it becomes visible in a single image, pinpoint the exact date of an abrupt event, and distinguish real change from seasonal variability. The key steps are: define your area, choose the right index for your monitoring goal, set an appropriate time window, and use the graph to identify and investigate anomalies.