The Tool Desk
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All three measure land-surface temperature (LST)—the radiometric temperature of the surface skin—not the air temperature reported by a weather station or forecast. Your study design should therefore specify the spatial support, observation time, cloud tolerance, archive period, and whether you need absolute values or comparable change before you download imagery.
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What open satellite LST data can you use?
Open LST imagery is available from several public programs, but the products answer different kinds of questions. The most useful starting choices are Landsat Collection 2 Level-2 Surface Temperature, the MODIS MOD11 family, and ECOSTRESS ECO_L2G_LSTE. They differ in archive length, pixel or grid support, observation frequency, acquisition time, cloud screening, and version status.
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| Product family | What it provides | Best fit | Important checks | Official documentation |
|---|---|---|---|---|
| Landsat Collection 2 Level-2 Surface Temperature | Processed, scene-based surface-temperature products for Landsat 4–9, with mission-specific availability periods. | Regional or site studies that need a long archive and detailed scene coverage. | Cloud and possible cloud-shadow retrieval errors, mission transitions, scene gaps, product tier, quality flags, and the correct scale factors. | USGS Landsat Collection 2 Surface Temperature |
| MODIS MOD11 | A daily LST/emissivity product family. NASA describes 1 km generalized split-window retrievals and 6 km day/night grids for different retrieval approaches. | Broad-area or multi-year analysis where regular sampling matters more than fine spatial detail. | Which MOD11 product and retrieval you selected, day versus night, compositing, clouds, and QA fields. | NASA MODIS MOD11 |
| ECOSTRESS ECO_L2G_LSTE | An instantaneous LST and emissivity product. The NASA Open Data record for Version 3 states a 70 m global grid. | Studies that need finer gridded support than MODIS and can work with variable acquisition times and scene availability. | Collection/version, swath and observation availability, local acquisition time, geolocation, and quality information. | NASA ECOSTRESS data and NASA Open Data ECO_L2G_LSTE record |
Nominal grid spacing is not proof that sensors have equal effective thermal resolving power. Landsat thermal measurements are resampled in delivered products, and each instrument and processing chain has its own spatial response. For an effective-resolution claim, consult the specification for the exact sensor and product rather than comparing pixel sizes alone.
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What LST means—and what it does not mean
LST is an estimate of the temperature of the land surface “skin” sensed by thermal infrared instruments. It responds to the material being viewed, moisture, shade, solar loading, surface roughness, and the satellite’s observation time. A roof, bare soil patch, irrigated field, and shaded tree canopy can have very different LSTs at the same moment.
Air temperature is a different physical quantity. Weather observations and forecasts generally describe air at a standard height, while a satellite LST pixel describes the radiating surface. NASA’s ECOSTRESS explanation notes that the surface can be much hotter than the reported air temperature; do not substitute an LST raster directly for a 2 m weather record. A comparison with station data requires a separate, validated method that accounts for timing, footprint, land cover, and atmospheric conditions.
Choose a product from the study question
Use Landsat for a long, scene-based archive
USGS provides global Collection 2 Level-2 surface-temperature scenes for Landsat 4–9, with distinct start and end dates by mission. The overview lists the mission-specific periods; read those dates for your sensor and study area instead of assuming one continuous record. The Landsat 7 surface-temperature scene archive is shown through April 2022, so it should not be described as an actively acquiring sensor after that date.
Landsat is a strong general choice when you need to map differences within a city, watershed, field system, or protected area and compare the same type of scene over many years. Its scene cadence and cloud conditions can still leave irregular observations, so it is not automatically a monthly time series.
Use MODIS when temporal regularity and area matter most
NASA’s MOD11 family is designed for frequent LST and emissivity retrievals over broad areas. The MOD11 documentation distinguishes 1 km generalized split-window retrievals from 6 km day/night grids. Those are different supports and should not be collapsed into a single statement that every MOD11 output is a 1 km product.
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MODIS can be preferable for regional climatologies, seasonal curves, or multi-year analyses where a coarser footprint is acceptable and regular sampling is more valuable than parcel-level detail. Confirm whether your chosen file is a daily, composited, day, night, or day/night product and use its QA fields.
Use ECOSTRESS when its sampling pattern fits
The ECOSTRESS ECO_L2G_LSTE Version 3 record describes instantaneous LST and emissivity on a 70 m global grid. That fine grid can reveal surface contrasts that a kilometre-scale product smooths, but it does not guarantee daily coverage of every location or a common observation time. Review actual scenes for your area and period.
ECOSTRESS Collection 1 was discontinued in calendar year 2025. In 2026, check the current Collection 2 records, product guides, and access instructions rather than copying an older Collection 1 tutorial. NASA provides access information through its ECOSTRESS data page, Earthdata services, and AppEEARS; follow the collection-specific documentation at download time.
Define the analysis before downloading data
A clear design prevents a technically correct download from producing an invalid comparison. Write down the following decisions first:
- Footprint: the exact coordinates, boundary, projection, and whether you need individual pixels, a neighbourhood statistic, or an area average.
- Period: years, months, seasons, and the mission or collection versions that cover those dates.
- Observation time: whether daytime, nighttime, or a consistent local-time window is scientifically meaningful.
- Spatial support: the smallest feature you can interpret after accounting for the instrument and processing, not just the nominal grid spacing.
- Cloud tolerance: the maximum missingness you can accept and the rule for declaring a month or year insufficiently sampled.
- Target quantity: absolute LST, anomalies, seasonal averages, trends, or differences between land-cover classes.
- Validation: station, field, or other independent observations if you intend to compare satellite LST with ground measurements or make accuracy claims.
If the goal is monthly change over several years, decide whether “monthly” means a calendar-month composite, a fixed number of valid observations, or a single repeat-date scene. These choices produce different estimates and must be recorded.
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Download Landsat Collection 2 Level-2 Surface Temperature
Use EarthExplorer to find scenes
- Open the USGS Landsat Collection 2 Surface Temperature documentation and note the product guide, current collection details, scale factors, and known caveats.
- Use EarthExplorer, the official USGS search and download route, and define your area with coordinates, a map polygon, or an uploaded boundary.
- Set the date range and select the Landsat Collection 2 Level-2 surface-temperature dataset. Choose the missions that actually cover your period; the USGS overview lists different availability dates for Landsat 4–9.
- Inspect browse images, acquisition dates, scene identifiers, cloud information, processing tier, and available quality layers before adding a scene to the download list.
- Download the complete Level-2 package, including the surface-temperature band and the quality-assessment files. Keep the original archive unchanged.
The delivered Level-2 product is already processed. USGS describes inputs such as Level-1 thermal data, optical inputs, ASTER GED/emissivity information, ASTER NDVI data, and atmospheric profiles or auxiliary data, but ordinary users do not need to obtain those auxiliary datasets merely to use the finished Level-2 surface-temperature output.
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Apply the product’s scaling and masks
Landsat surface temperature is stored as scaled values and is expressed in Kelvin after conversion. Do not treat the stored integer as a temperature and do not insert a remembered coefficient from an older tutorial. Use the scale factor and offset in the product guide for the exact Collection 2 product and sensor, then convert Kelvin to Celsius or Fahrenheit only after that conversion if your analysis requires it.
Use the supplied QA information to remove fill, cloud, cloud shadow, and other flagged pixels. USGS identifies a known surface-temperature retrieval issue associated with clouds and possibly cloud shadows. A clear-looking RGB browse image is not sufficient evidence that a thermal pixel is valid; inspect the thermal QA bits and acquisition context.
Access MODIS MOD11 data
- Start at NASA’s MOD11 product page and identify the exact MOD11 collection and file type that matches your study.
- Choose the spatial and temporal support deliberately: the page describes 1 km generalized split-window retrievals and 6 km day/night grids for different products or retrieval approaches.
- Select day, night, or both, and check whether the file is an instantaneous observation or a composite. Record the product’s temporal definition.
- Download the science layers and QA fields through the access route linked by NASA. Preserve the collection identifier and download date with your files.
- Decode the product-specific scale, fill values, and QA flags from its user guide before calculating statistics. Mask invalid or low-quality observations before compositing.
For a broad monthly series, a consistent MODIS product and a documented compositing rule can provide more regular sampling than scene-based Landsat. That advantage comes with a coarser support: a value represents a much larger area than a detailed Landsat or ECOSTRESS grid cell.
Access ECOSTRESS L2 global 70 m data
- Read the current ECOSTRESS data page and the algorithm and user-guide index before selecting files.
- Use NASA Earthdata search or AppEEARS as directed for the current collection. Confirm that you are downloading the supported Collection 2 product, not an obsolete Collection 1 workflow.
- Select the ECO_L2G_LSTE instantaneous LST and emissivity product and verify the version. The NASA Open Data record for Version 3 states a 70 m global grid.
- Check the actual observation date and local observation time for every scene. ECOSTRESS overpasses are not a guaranteed daily, same-time series for every location.
- Download and retain geolocation and quality layers. Mask poor-quality pixels and document any reprojection or resampling applied to the gridded data.
The 70 m grid is useful for detailed spatial questions, but it is not a promise of uniform coverage or equivalent native thermal detail everywhere. Scene availability, view geometry, quality flags, and changing acquisition times can dominate the usable sample.
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What to do when scenes are cloudy
Thermal-infrared retrievals cannot provide a valid clear-sky surface temperature through cloud. Cloud-contaminated or cloud-shadowed values should be masked, not interpreted as unusually cold ground. A missing observation is a result to document, not a value to fill silently.
A practical cloud-gap procedure
- Use the product’s QA and cloud or cloud-shadow masks, rather than relying only on a visual RGB check.
- Search adjacent acquisition dates, while recording the change in observation date and local time.
- Use another product only if its spatial support, observation time, and physical definition remain appropriate for the question; do not merge sensors automatically.
- Set a minimum valid-pixel or valid-observation rule for each monthly or seasonal statistic.
- Report the number and distribution of missing observations. A trend based mostly on clear days can be biased if cloudiness changes with season or year.
For a study that needs a fixed monthly schedule, MODIS may offer more opportunities than Landsat, while ECOSTRESS may provide excellent detail but fewer usable scenes at a consistent time. The correct choice depends on the missingness your design can tolerate.
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- Inventory inputs. Record product name, collection or version, sensor, scene or granule identifiers, acquisition dates, local observation times, and download dates.
- Clip only after quality screening. Apply fill, cloud, cloud-shadow, emissivity, geolocation, and other product-specific QA masks before calculating an area statistic. Clipping first is acceptable for efficiency, but it must not discard QA layers.
- Convert values correctly. Apply the scale and offset defined by the exact product guide. Keep the result in Kelvin internally when possible and label every output unit.
- Standardize spatial handling. Document projection, pixel alignment, resampling method, and whether values were aggregated. Avoid presenting a resampled grid as if it created new thermal information.
- Aggregate transparently. For monthly or seasonal products, state whether you used a mean, median, percentile, maximum, anomaly, or another statistic, and how many valid observations contributed.
- Preserve provenance. Keep the unmodified source files, processing scripts or settings, QA rules, and a table of excluded observations.
- Validate where needed. If you compare with weather stations or field measurements, match observation time and location and discuss the mismatch between a surface skin temperature and air temperature.
Designing a multi-year monthly LST analysis
Choose a consistent temporal unit
For each calendar month, define a valid-observation rule before looking at results—for example, a minimum count or a minimum fraction of valid pixels. A month with one clear scene should not be treated as equivalent to a month with many independent observations unless that is an explicit design choice.
Keep day and night observations separate unless your question specifically concerns their combination. Different overpass times sample different heating states. ECOSTRESS’s changing acquisition time is especially important; two scenes from the same month may not represent the same part of the diurnal cycle.
Separate sensor changes from environmental change
A long Landsat record can span multiple missions. Sensor transitions, product versions, spatial support, and observation timing can introduce differences that resemble a trend. Record which mission supplied each observation and avoid implying that a single sensor acquired every date in the archive. If you combine Landsat with MODIS or ECOSTRESS, treat the datasets as distinct measurements unless a documented harmonization and validation method supports the comparison.
Report uncertainty and missingness honestly
The available product descriptions do not establish one universal accuracy figure or cross-sensor uncertainty. Do not invent an error bar from pixel size or quote an accuracy number without the technical guide or a validation study for your exact product and conditions. Report valid-pixel counts, cloud gaps, QA thresholds, aggregation choices, and any independent validation instead.
Common mistakes and how to fix them
| Problem | Why it fails | Correction |
|---|---|---|
| Using a temperature raster as air temperature | LST measures the surface skin, which can be much hotter or cooler than near-surface air. | Label results as LST and use a separate validated method for comparison with station air temperature. |
| Reading stored Landsat integers as degrees | Collection 2 values require product-specific scaling and are tied to Kelvin units after conversion. | Apply the exact scale and offset from the current product guide, then convert units. |
| Keeping cloudy pixels because the RGB image looks clear | Thermal retrieval errors can occur with clouds and possibly cloud shadows that are not obvious in a browse image. | Decode QA flags and remove affected pixels. |
| Calling every MOD11 value 1 km | NASA distinguishes 1 km generalized split-window retrievals from 6 km day/night grids. | Name the exact MOD11 product and its support. |
| Assuming a 70 m ECOSTRESS grid means daily 70 m coverage | The product is instantaneous, with variable swath availability and observation times. | Inspect actual scenes, dates, times, and quality layers. |
| Following an old ECOSTRESS tutorial unchanged | Collection 1 was discontinued in 2025. | Use current Collection 2 records and guides. |
| Combining products by matching dates only | Different sensors have different footprints, algorithms, overpass times, and QA definitions. | Keep products separate or use a documented harmonization and validation method. |
| Downloading atmospheric inputs for ordinary Landsat use | The processed Level-2 product already incorporates the required processing inputs. | Download the delivered Level-2 product and its documentation; obtain auxiliary data only when your method specifically requires them. |
How to report the dataset so others can reproduce it
A methods section should identify the sensor and exact product, collection or version, date range, geographic boundary, access portal, and download date. State the scale and unit conversion, QA and cloud rules, treatment of cloud shadows, projection and resampling, temporal aggregation, valid-observation threshold, and any validation data.
Link readers to the primary documentation: USGS’s Collection 2 surface-temperature page, its surface-temperature overview, and Landsat data-access guidance. For ECOSTRESS, cite the data page, the technical documentation index, and the Version 3 dataset record. For MODIS, cite the MOD11 product page.
Cost, access, and practical requirements
The imagery and the official documentation are open and available at no cost. USGS says its no-cost open-data policy for Landsat has been in place since 2008. You need an account or authentication where the portal requires it, storage for the original files, and software or code capable of reading raster data and QA bands. A paid thermal camera, special sensor, or commercial imagery subscription is not required to obtain these satellite products.
Processing can still require substantial local storage and compute time when you download many scenes or create multi-year composites. Plan storage for both source archives and derived rasters, and retain enough metadata to identify every input.
A decision checklist
- Do you need a long, scene-based archive? Begin with Landsat Collection 2 Level-2.
- Do you need frequent broad-area observations and can accept kilometre-scale support? Examine the appropriate MOD11 product.
- Do you need a finer gridded product and can tolerate irregular, time-varying observations? Examine current ECOSTRESS L2 data.
- Have you specified day or night, local observation time, cloud tolerance, and valid-observation rules?
- Will you apply the exact scale, offset, and QA mask for the selected collection?
- Will your results remain clearly labeled as surface temperature rather than air temperature?
- Have you recorded mission, version, dates, processing, missingness, and validation choices?
The best dataset is the one whose spatial support, temporal sampling, observation time, archive, and quality information match the question—not simply the one with the smallest nominal pixel.
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