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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThe reliable dataset is the one that matches your monitoring scale, revisit needs, historical period and analysis skill—not a universally “best” satellite. For field or forest-patch detail, start with 30 m Harmonized Landsat Sentinel-2 (HLS), Sentinel-2 surface reflectance or Landsat Collection 2. For regional or global trends where regular composites matter more than parcel detail, use MODIS vegetation-index products. Whichever source you choose, screen clouds and shadows, retain the quality layers, verify band definitions and scaling, and compare like seasons.
NDVI is useful for measuring relative greenness, but it is not a diagnosis of plant health. Vegetation type, season, soil, atmosphere, moisture and cloud contamination all affect the ratio. A defensible monitoring record therefore combines an appropriate sensor with quality assurance, consistent processing and interpretation in context.
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What makes public satellite data reliable?
Reliability has several dimensions that are easy to confuse:
- Spatial suitability: pixels must be small enough for the feature or management unit you are studying.
- Temporal suitability: observations or composites must arrive often enough to detect the change you care about.
- Consistency: a long record needs stable processing, comparable seasons and documented collection versions.
- Observational quality: clouds, shadows, haze, snow, saturation and terrain effects must be identified and masked.
- Product readiness: a published index can be convenient, but an experimental product may not be updated or maintained.
- Traceability: metadata should tell you the sensor, acquisition date, processing baseline, scale factor, quality flags and missing-data conventions.
A satellite’s advertised revisit is not the same as a usable, cloud-free observation at your location. Likewise, two products labelled “NDVI” are not automatically interchangeable if they use different resolutions, compositing windows, masks or scaling.
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Choose a product family by monitoring question
| Source or product | Best fit | Published characteristics | Important checks |
|---|---|---|---|
| NASA HLS vegetation-index suite | Harmonized Landsat/Sentinel-2 time series, 30 m mapping and several vegetation or disturbance indices | Nine indices: NDVI, EVI, SAVI, MSAVI, NDMI, NDWI, NBR, NBR2 and TVI. NASA describes a global average revisit of less than 1.6 days. | The 1.6-day figure is a combined global average, not a guaranteed clear observation. The HLS project reports about 1.7 days of product latency when source and auxiliary data arrive on time. Historical processing was scheduled for completion in 2026; verify current coverage before assuming every earlier date is present. |
| Sentinel-2 MSI | Local detail and direct access to Copernicus scenes | Copernicus distributes products free through the Copernicus Data Space Ecosystem. NASA’s described Sentinel-2 NDVI entry is 10 m and uses Band 8 (near-infrared) and Band 4 (red). | A NASA page cautions that its described NDVI data may be experimental or research-grade and states: “These data are not guaranteed to be consistently available or routinely updated.” Confirm the scene’s processing baseline, cloud mask and acquisition date. |
| Landsat Collection 2 | Long historical records, surface reflectance and sensor-specific analysis | USGS provides surface-reflectance-derived NDVI products. For Landsat 8 and 9, the calculation uses Band 5 (near-infrared) and Band 4 (red); the NDVI output uses a 0.0001 scale factor. | USGS identifies Tier 1 scenes as the highest available quality and suitable for time-series analysis. Tier 2 scenes do not meet the Tier 1 geometry specification, potentially because of orbital information, cloud cover, control or related limitations. Collection 2 includes pixel-quality and radiometric-saturation/terrain QA files. |
| MODIS NDVI/EVI | Broad regional or global patterns where regular composites outweigh fine spatial detail | Products are available at 250 m, 500 m, 1 km and 0.05-degree grids. Vegetation indices are delivered as 16-day or monthly products; Terra and Aqua records are phased eight days apart. | The algorithm uses quality metrics to reject low-quality values and selects a representative view from the remaining observations. Inspect the QA layer and composite period before interpreting an abrupt change. |
Use the same boundary, comparable dates or seasons, index definition and quality masking when comparing products. A 10 m scene, a 30 m HLS observation and a 250 m MODIS composite measure different spatial units and time windows; they should not be treated as equivalent measurements.
Understand the main public options
Harmonized Landsat Sentinel-2 vegetation indices
HLS combines Landsat and Sentinel-2 inputs into a harmonized record. Its vegetation-index suite is useful when you need more than greenness: NDMI and NDWI can add moisture context, while NBR and NBR2 help examine fire or burn impacts. NASA describes the snapshots at 30 m and a global average revisit under 1.6 days. That cadence is a statistical average across the combined missions; local clouds, missing acquisitions and processing delays can create longer gaps.
The HLS project reports approximately 1.7 days of latency when all source and auxiliary data arrive in time. If you need a complete historical series, check the current processing status for your date range. Historical processing was scheduled for completion in 2026, so a request for an older date should be tested rather than assumed to be complete.
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Sentinel-2 MSI
Sentinel-2 is a practical choice when a 10 m view can separate fields, riparian strips or forest gaps that would be mixed in a coarser pixel. Copernicus makes Sentinel-2 products freely available through the Copernicus Data Space Ecosystem. For the NASA-described NDVI entry, Band 8 supplies near-infrared reflectance and Band 4 supplies red reflectance.
Do not infer operational guarantees from the existence of a download page. NASA labels the described NDVI product as potentially experimental or research-grade and warns that it is not guaranteed to be consistently available or routinely updated. If your monitoring must run every week or month, inspect the actual scene archive and establish a fallback based on surface-reflectance scenes rather than depending on a derived product whose update schedule is uncertain.
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Landsat Collection 2
Landsat is valuable when the historical record matters as much as current detail. USGS surface-reflectance products support Landsat-derived NDVI, and the Collection 2 package includes quality files for cloud, fill, saturation and terrain-related screening. For Landsat 8 and 9, use Band 5 for near-infrared and Band 4 for red when following the USGS NDVI product definition.
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For time-series work, select Tier 1 scenes where possible. Tier 2 scenes can be useful, but they miss the Tier 1 geometric specification and may reflect less accurate orbital information, insufficient control, cloud conditions or other factors. Record the tier and collection in your analysis log so a later user can distinguish a data gap from a rejected scene.
MODIS vegetation indices
MODIS sacrifices parcel-level detail for consistent, broad-area composites. NASA lists 250 m, 500 m, 1 km and 0.05-degree products, with 16-day and monthly delivery options. Terra and Aqua composites are phased eight days apart, which can provide a more regular broad-area record than relying on one platform alone.
MODIS processing evaluates quality metrics, filters low-quality values and selects a representative view from the remaining observations. The composite is therefore not a simple average of every pixel acquired during the period. Use its QA information when a cloud event, haze episode or sudden disturbance could bias the selected view.
A step-by-step discovery workflow
- Define the decision and map scale. Write down whether you are monitoring a crop field, forest disturbance, watershed drought, regional productivity or a global ecosystem trend. Let that decision determine the minimum useful pixel size.
- Set the cadence and latency requirement. Decide whether you need a near-real-time alert, a monthly report or a multi-year seasonal baseline. Separate the satellite’s nominal revisit from the time you can wait for processing and a clear observation.
- Select a product family. Consider HLS for harmonized 30 m observations and multiple indices; Sentinel-2 or Landsat when direct surface-reflectance scenes and sensor-specific histories matter; and MODIS when coarse spatial detail is acceptable in exchange for regular composites.
- Use the official access portal. Sentinel-2 products are available through Copernicus Data Space. HLS is archived at NASA LP DAAC and supports download or cloud-native analysis. USGS documents Landsat Collection 2 access and product structure. Start with the issuing agency’s product page, not an unlabeled third-party mirror.
- Confirm mission, collection and dates. Check that the location, requested period, processing baseline and derived-index product actually exist. HLS documentation lists mission start dates and historical-processing status; Sentinel-2 and Landsat pages identify their own product generations.
- Download metadata and QA with the imagery. Never archive only the visual index. Keep acquisition time, tile or scene identifier, processing version, scale factor, fill value definitions and every quality band used to mask pixels.
- Apply the quality mask before statistics. Exclude clouds and shadows where the product QA supports that decision. Also check saturation, terrain occlusion, snow or water flags, fill values and scene geometry. For Landsat time series, favour Tier 1 and document any Tier 2 exception.
- Verify bands and scaling. Sentinel-2 NDVI as described by NASA uses Band 8 and Band 4. Landsat 8–9 NDVI as described by USGS uses Band 5 and Band 4, with a 0.0001 output scale factor. Do not copy a scale factor or band number from one sensor into another workflow.
- Test a small area first. Inspect several dates over a representative field or forest patch. Check that masked pixels are removed, water and bare soil behave plausibly, and the value range matches the product documentation before processing an entire region.
- Freeze the processing recipe. Record the product version, masks, resampling method, date window, index formula and aggregation rule. Apply the same recipe to every date in the comparison.
NDVI: calculation, meaning and limits
NDVI is calculated as:
NDVI = (NIR − red) / (NIR + red)
Healthy, actively growing vegetation generally reflects more near-infrared than red light, so the ratio often rises with canopy greenness. The value is still an observation of reflected light, not a direct measurement of biomass, disease or yield. Vegetation type, phenology, soil background, atmospheric effects, surface moisture, clouds and shadows can all change it.
Use EVI when dense vegetation causes NDVI sensitivity to flatten; NASA describes EVI as improving sensitivity in dense vegetation. Use moisture-oriented indices such as NDMI or NDWI when water stress is central, and burn-related indices such as NBR when fire disturbance is the question. HLS includes these alternatives, while MODIS offers NDVI and EVI product families.
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Band and scale reference
| Workflow | Near-infrared band | Red band | Scaling note |
|---|---|---|---|
| NASA-described Sentinel-2 NDVI | Band 8 | Band 4 | Use the product metadata; no universal cross-sensor encoding should be assumed. |
| USGS Landsat 8–9 derived NDVI | Band 5 | Band 4 | NDVI output scale factor is 0.0001. |
| HLS vegetation-index products | Use the index product’s metadata | Use the index product’s metadata | Confirm the tile, version, scale and fill conventions in the HLS documentation. |
When calculating NDVI yourself, first convert stored digital numbers according to the surface-reflectance metadata. When using a ready-made NDVI layer, apply its documented scale factor before comparing values or exporting statistics. A value of 5,000 in a product with a 0.0001 scale is 0.5; treating the stored integer as the physical index would produce a meaningless chart.
Quality assurance that prevents false vegetation change
Clouds, shadows and haze
Cloud-contaminated pixels can produce sharp, artificial declines or increases. Use the product’s cloud and shadow flags, and inspect a true-colour or quality view around any abrupt change. A satellite pass that occurred on schedule is not evidence that every pixel was usable.
Fill, saturation and terrain
Fill values are not low vegetation. Remove them before calculating means or trends. Saturated detector values and terrain-occluded pixels can also distort a ratio; Collection 2 provides QA information for these conditions. In mountainous areas, compare observations with the terrain-related flags and avoid mixing masked and unmasked slopes.
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Composite and scene timing
A MODIS 16-day value represents a quality-selected view within a period, while a Sentinel-2 or Landsat scene is tied to a particular acquisition time. Align the time meaning before declaring one product “higher” or “lower.” For seasonal studies, compare the same phenological window each year rather than arbitrary calendar dates.
Spatial mixing
A coarse pixel can combine crops, soil, water and roads. If a field is smaller than a MODIS pixel, its apparent NDVI may mostly describe neighbouring land. HLS or Sentinel-2 may resolve the field, but a 10 m or 30 m pixel can still mix edges, tree shadows and bare soil. Use a consistent boundary and report the fraction of valid pixels.
How to compare sources without misleading yourself
Use these comparison axes in a project specification:
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- Resolution: the pixel size and whether the target feature occupies enough of a pixel.
- Cadence and latency: nominal revisit, composite length, processing delay and expected clear-observation gaps.
- Historical coverage: mission start, available collection and whether reprocessing is still underway.
- Index choice: NDVI for broad greenness, EVI for dense canopies, moisture indices for water status and NBR-family indices for burn effects.
- QA and masking: available cloud, shadow, saturation, terrain, snow, water and fill flags.
- Workflow: ready-made index versus reflectance bands, local processing versus cloud-native analysis, and the amount of metadata you can preserve.
- Terms and readiness: public access does not guarantee an experimental product’s update schedule or operational support.
For a fair comparison, aggregate each source to a common boundary, use comparable dates or seasons, apply equivalent quality rules and document resampling. Do not blend a 16-day MODIS composite with a single clear Sentinel-2 scene and call the difference a vegetation response.
Common failure modes and fixes
“The revisit says one day, but I have a two-week gap.”
The stated HLS revisit is a global average of combined missions, not a promise of a clear local pixel. Clouds, missing inputs and processing latency explain many gaps. Extend the date window, inspect both missions’ QA and distinguish “no acquisition” from “acquired but unusable.”
“My NDVI values are thousands instead of decimals.”
Check the product’s scale factor. USGS Landsat-derived NDVI uses 0.0001, so stored integers must be multiplied by that factor before analysis. Never assume Sentinel-2, HLS and Landsat use the same encoding.
“A field suddenly turns brown for one date.”
Inspect cloud, shadow, haze, saturation, terrain and fill flags, then view neighbouring dates. If the date is a single contaminated observation, mask it rather than interpreting it as crop failure. If the signal persists across quality-screened dates and matches field context, investigate a real disturbance.
“Two products disagree.”
Check pixel size, acquisition time, compositing window, band definition, atmospheric correction, scaling and masks. A MODIS composite and a Sentinel-2 scene may both be correct for their own measurement definitions.
“The portal has an index, but it stops updating.”
Confirm whether the product is experimental or research-grade. NASA’s Sentinel-2 NDVI description explicitly warns that availability and routine updates are not guaranteed. For a service that must continue, retain the underlying surface-reflectance workflow or select a maintained product family, and record the date you verified availability.
Practical choices for typical projects
Field-level crop monitoring
Use Sentinel-2 when 10 m detail is needed and clear scenes are available, or HLS when a harmonized 30 m record and several indices simplify the workflow. Mask clouds and shadows, compare the same crop-growth stages and consider EVI or moisture indices when NDVI saturates or water stress is the actual question.
Forest disturbance and recovery
Use Landsat Collection 2 or HLS for a multi-year 30 m record. Prefer Landsat Tier 1 scenes for time-series geometry, retain the QA bands and add NBR or NBR2 for burn impacts. Treat HLS historical completeness as a status to verify because processing was scheduled for completion in 2026.
Regional drought context
MODIS 250 m, 500 m or 1 km composites can provide a regular regional signal when individual fields are not the unit of analysis. Use the 16-day or monthly product consistently, inspect quality metrics and compare the same seasonal periods across years.
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Choose the MODIS grid and composite period that match the scale of the trend. A 0.05-degree product is appropriate only when that coarse unit is acceptable. Report the composite definition and QA treatment; do not describe it as parcel-level monitoring.
Access, cost and operational expectations
The major sources described here are publicly available. Copernicus states that Sentinel-2 products are free through its Data Space Ecosystem; NASA describes HLS and MODIS products as public data, and USGS documents public Landsat access. “Free” refers to data access, not necessarily to storage, bandwidth, cloud computing or the staff time needed to process large archives.
HLS can be downloaded or analysed with cloud-native tools. A cloud workflow can reduce local storage and move filtering close to the archive, but it still requires a reproducible script, versioned inputs and a plan for compute or egress costs. If you use a hosted platform, verify its current pricing, quotas and data-retention terms separately; those conditions are not established by the satellite agencies’ free-data policies.
A reproducibility checklist
- Area of interest, coordinate reference system and boundary version
- Mission, sensor, collection and processing baseline
- Acquisition dates or composite periods and timezone convention
- Reflectance bands or derived-index name
- Band mapping and scale factor
- Cloud, shadow, snow, water, saturation, terrain and fill masks
- Scene tier or quality category
- Resampling and aggregation method
- Minimum valid-pixel threshold for each statistic
- Software version, script revision and export date
- Whether a product is experimental, research-grade or routinely maintained
Keeping this record makes a vegetation chart auditable. It also lets you replace a missing or discontinued product without silently changing the meaning of the time series.
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