Landsat vs. Sentinel: Choosing the Right Free Satellite Imagery
If you work in GIS, remote sensing, or geospatial analysis, two names come up constantly: Landsat and Sentinel. Both are free. Both are globally available. Both have powered some of the most important environmental and land-use studies of the last decade.
But they are not interchangeable.
Choosing the wrong one for your project wastes time, produces weaker outputs, and can undermine your analysis entirely. This article breaks down exactly what separates these two satellite families and gives you a clear framework for picking the right one.
A Quick Background on Each Program
Landsat is the longest-running Earth observation satellite program in history. Operated jointly by NASA and the USGS, Landsat has been collecting imagery since 1972. The current operational satellite, Landsat 9, was launched in 2021. Its predecessor, Landsat 8, remains active and continues to collect data. Together, they form a continuous archive that no other program can match.
Sentinel is the satellite constellation built under the European Union’s Copernicus Programme, managed by the European Space Agency (ESA). The Sentinel family covers multiple mission types: optical, radar, atmospheric, and ocean monitoring. For most GIS professionals, the two most relevant satellites are Sentinel-2 (multispectral optical) and Sentinel-1 (synthetic aperture radar, or SAR).
When people compare “Landsat vs. Sentinel,” they are usually comparing Landsat 8/9 against Sentinel-2. That is the comparison this article focuses on.
Key Technical Differences
Spatial Resolution
This is where the gap is most immediately visible.
Sentinel-2 delivers multispectral imagery at 10 meters for its visible and near-infrared bands. Landsat 8/9 delivers multispectral imagery at 30 meters. That is a 9x difference in pixel area. For mapping at the field scale, monitoring urban growth at the neighborhood level, or detecting small agricultural features, Sentinel-2 wins decisively.
Landsat does offer a panchromatic band at 15 meters, which can be used for pan-sharpening. But the native multispectral resolution remains 30 meters.
Revisit Frequency
Sentinel-2 is a two-satellite constellation: Sentinel-2A and Sentinel-2B orbit together and produce a combined 5-day revisit cycle at the equator, and even shorter intervals at higher latitudes.
Landsat 8 and Landsat 9 operate together but follow a 16-day cycle per satellite. With both satellites combined, the effective revisit frequency improves but still falls short of Sentinel-2’s temporal density.
For time-series analysis, cloud mitigation strategies, or near-real-time monitoring, Sentinel-2 has a clear advantage.
Spectral Bands
| Band Type | Landsat 8/9 | Sentinel-2 |
|---|---|---|
| Coastal/Aerosol | Yes (Band 1) | Yes (Band 1) |
| Blue | Yes | Yes |
| Green | Yes | Yes |
| Red | Yes | Yes |
| Red Edge | No | Yes (3 bands) |
| Near-Infrared | Yes | Yes |
| SWIR 1 | Yes | Yes |
| SWIR 2 | Yes | Yes |
| Panchromatic | Yes (15m) | No |
| Cirrus | Yes | Yes |
| TIRS (Thermal) | Yes (2 bands) | No |
The key differentiators here are the red edge bands in Sentinel-2 and the thermal infrared (TIRS) bands in Landsat.
Sentinel-2’s red edge bands (centered around 705, 740, and 783 nm) are highly sensitive to vegetation chlorophyll content. They are extremely valuable for precision agriculture, crop stress detection, and forest health monitoring.
Landsat’s thermal bands measure land surface temperature. No other freely available optical satellite provides this. If your analysis involves urban heat island mapping, drought monitoring, evapotranspiration estimation, or wildfire burn severity, Landsat is the only choice in the free tier.
Archive Depth
Landsat’s archive extends back to 1972. Sentinel-2 only began collecting data in 2015 (Sentinel-2A) and 2017 (Sentinel-2B).
If your project involves any form of long-term change detection spanning more than a decade, Landsat is the backbone you need. There is no substitute.
Choosing Based on Use Case
Use Sentinel-2 When:
- You need high spatial detail at 10 meters
- Your analysis requires frequent revisits or cloud-free composites over short periods
- You are working in precision agriculture, crop mapping, or vegetation stress analysis
- You are leveraging red edge indices such as Red Edge NDVI or Chlorophyll Red Edge (CRE)
- You are producing visually sharp output maps for presentations or client deliverables
- You are monitoring dynamic features such as flood extent, algal blooms, or construction progress
Use Landsat When:
- You need thermal infrared data for temperature mapping or evapotranspiration analysis
- Your change detection analysis extends back more than 10 years
- You are working on long-term deforestation, glacial retreat, or coastal erosion studies
- Your pipeline depends on established indices calibrated to Landsat bands (such as NDVI time series going back decades)
- You are building a land cover classification that must align with USGS or NLCD historical data products
Use Both Together When:
- You are building dense time-series stacks and need both temporal frequency (Sentinel-2) and historical depth (Landsat)
- You are developing machine learning models for land cover and want to maximize training data volume
- You are monitoring seasonal vegetation cycles and need fine spatial resolution alongside historical baselines
The Harmonized Landsat and Sentinel-2 (HLS) dataset, produced by NASA, merges both into a single consistent analysis-ready product at 30-meter resolution with high temporal frequency. It is an excellent starting point for projects that need both satellites without manual harmonization.
Data Access and Processing Platforms
Both datasets are freely accessible. Here is where to get them:
Landsat:
- USGS Earth Explorer (earthexplorer.usgs.gov)
- USGS LandsatLook
- Google Earth Engine
- AWS Open Data Registry (Landsat Collection 2 on S3)
- Microsoft Planetary Computer
Sentinel-2:
- Copernicus Data Space Ecosystem (dataspace.copernicus.eu)
- Sentinel Hub (commercial tiers, but free quota available)
- Google Earth Engine
- AWS Open Data Registry
- Microsoft Planetary Computer
For cloud-based analysis at scale, Google Earth Engine remains the most accessible platform for working with both datasets simultaneously, especially for large-area time-series analysis without downloading bulk data locally.
Processing Considerations
Atmospheric Correction
Both programs offer analysis-ready data (ARD) products with surface reflectance corrections applied.
- Landsat Collection 2 Level-2 products include surface reflectance and surface temperature
- Sentinel-2 Level-2A products include bottom-of-atmosphere (BOA) reflectance generated with the Sen2Cor algorithm
If you are downloading raw data, always prefer the corrected Level-2 products for any quantitative analysis.
Cloud Masking
Cloud masking is a consistent challenge with optical imagery. Landsat uses the CFMask-based QA_PIXEL band. Sentinel-2 provides the Scene Classification Layer (SCL) in Level-2A products. Both require careful handling, and neither is perfect in complex terrain or tropical regions.
For dense time-series workflows, the s2cloudless Python library provides robust probabilistic cloud masking for Sentinel-2.
Common Mistakes to Avoid
Choosing Sentinel-2 for everything because “higher resolution is always better.” Resolution is one dimension. Thermal data, archive depth, and spectral configuration all matter more for certain applications.
Using Landsat for detailed urban mapping. A 30-meter pixel in a dense urban environment covers a lot of mixed land types. Sentinel-2 is far more appropriate for urban morphology analysis.
Ignoring the HLS dataset. Many analysts download and process Landsat and Sentinel-2 separately when HLS already handles the harmonization. It saves weeks of preprocessing effort.
Applying NDVI thresholds developed for one sensor directly to the other. Band center wavelengths and bandwidths differ between the two sensors. Cross-sensor index comparisons require careful calibration or the use of harmonized datasets.
Summary Table
| Feature | Landsat 8/9 | Sentinel-2 |
|---|---|---|
| Spatial resolution (multispectral) | 30m | 10m |
| Revisit frequency | ~8 days (combined) | 5 days |
| Thermal bands | Yes | No |
| Red edge bands | No | Yes |
| Archive depth | Since 1972 | Since 2015 |
| Best for | Long-term change, thermal analysis | High-resolution mapping, vegetation |
| Operator | NASA / USGS | ESA / Copernicus |
| Cost | Free | Free |
Final Thoughts
Landsat and Sentinel-2 are complementary tools, not competitors. Each was designed with a different set of priorities. Understanding those priorities is the difference between a GIS analyst who picks a sensor out of habit and one who picks it with purpose.
If your project demands thermal data or a 50-year archive, Landsat is non-negotiable. If you need sub-10-day revisits and 10-meter detail across vegetation-rich landscapes, Sentinel-2 is the clear choice. In many advanced workflows, both belong in the same pipeline.
The best geospatial analysts know not just how to download imagery but why they chose that imagery in the first place.
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