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Use Landsat 8 or 9 when you need land-surface temperature or another thermal observation today. Use Sentinel-2 when the question is optical—land cover, vegetation, or surface reflectance—and you need its finer multispectral detail. Do not use Sentinel-2 as a substitute for temperature: its MSI instrument has no thermal bands. TRISHNA is the future option for more frequent, high-resolution thermal imaging, but it is still in development and CNES currently lists a 2027 launch schedule, so its imagery is not an operational data source yet.
The right choice therefore depends first on the measurement, then on spatial scale, time-series needs, cloud conditions, product processing, and access date. The guide below separates what is available now from what TRISHNA is designed to provide.
The quick decision
| Your primary need | Best fit among these missions | Reason |
|---|---|---|
| Land-surface temperature now | Landsat 8/9 | Operational thermal bands and USGS Collection 2 Level-2 surface-temperature products. |
| Optical land-cover or vegetation mapping | Sentinel-2 | Multispectral MSI observations with finer optical detail; there is no thermal instrument. |
| Thermal observations at roughly 60 m every few days in the future | TRISHNA | Planned 60 m products and a three-day equatorial revisit; the mission is not yet operational. |
| Long-term, repeatable analysis | Landsat archive, optionally combined with Sentinel-2 | USGS maintains Collection 2 products for Landsat 8/9 and earlier missions, while Sentinel-2 can add optical observations. |
| Evapotranspiration modelling | Landsat thermal data now; Sentinel-2 as optical input | Thermal measurements supply temperature information, while optical reflectance helps characterize vegetation. A Sentinel-2/TRISHNA calibration workflow is planned by CNES but depends on TRISHNA becoming available. |
A nominal revisit is only an opportunity to acquire an image. Clouds, haze, viewing geometry, missing scenes, and the quality threshold you set determine how many usable observations you actually obtain.
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Start with the measurement, not the satellite name
“Land and water analysis” covers fundamentally different measurements. Surface reflectance describes how much sunlight a material reflects. Land-surface temperature (LST) is a thermal measurement related to the surface’s emitted infrared energy. Water extent, turbidity proxies, vegetation indices, evapotranspiration, and water stress use different combinations of bands and derived products. A mission that is excellent for one of these tasks can be unsuitable for another.
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If the output is temperature
Choose an instrument with thermal infrared bands and an appropriate temperature product. Among the three missions here, Landsat 8/9 is the currently usable option. Sentinel-2 cannot supply LST because its MSI payload has no thermal bands. TRISHNA is intended to supply thermal observations, but its launch and data release are still in the future.
If the output is reflectance, land cover, or vegetation condition
Sentinel-2 is a strong optical choice when its spatial detail and acquisition opportunities match your site. Landsat’s optical bands remain useful when continuity with the Landsat archive, a common processing chain, or paired thermal data matters.
If the output is water temperature or water energy exchange
Use thermal-capable Landsat products for current work, subject to pixel size and cloud screening. Sentinel-2 can characterize optical water properties and shoreline or vegetation context, but it cannot replace the temperature measurement. TRISHNA is designed for inland and coastal water analysis once the mission is operating.
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What each mission actually contributes
TRISHNA: planned thermal and optical observations
TRISHNA (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment) is a joint CNES–ISRO mission. Its planned payload combines a four-channel long-wave thermal-infrared sensor with a seven-band visible, near-infrared, and shortwave-infrared sensor. The mission is designed to retrieve surface temperature and emissivity and to support surface-energy budgeting, evapotranspiration, vegetation-water-stress monitoring, and inland and coastal water studies. The CNES mission page currently lists TRISHNA as in development with a scheduled launch in 2027.
CNES lists 60 m products and a three-day revisit at the equator. ISRO describes approximately 57 m land/coastal resolution at nadir and 1 km resolution over oceanic and polar regions on its TRISHNA mission page. These are planned specifications, not measurements from an operating archive. A launch date can change, and imagery should not be treated as downloadable until an official release is announced.
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Landsat 8/9: current optical and thermal observations
Landsat 8 carries the Operational Land Imager (OLI) for visible, near-infrared, and shortwave-infrared observations and the Thermal Infrared Sensor (TIRS). USGS lists 30 m multispectral optical data, a 15 m panchromatic band, and two TIRS thermal bands with 100 m native resolution on the Landsat 8 mission page. Landsat 8 has a 16-day repeat cycle; Landsat 9 is phased eight days apart, creating an opportunity for an approximately eight-day combined cadence when both satellites acquire usable scenes.
Do not confuse native detector resolution with the grid spacing of a delivered product. USGS documents resampling of TIRS data in the archive, so a thermal product may be distributed on a grid whose pixel spacing differs from the sensor’s 100 m native resolution. Use the product metadata when matching pixels to field plots, shorelines, or small reservoirs.
Sentinel-2: detailed optical observation without thermal bands
Sentinel-2’s Multispectral Instrument (MSI) provides visible, near-infrared, and shortwave-infrared optical observations comparable in purpose to Landsat’s optical bands, with finer optical spatial detail in many applications. The USGS comparison identifies Sentinel-2 as an optical mission and directs users to Copernicus for data access. MSI has no thermal instrument, so Sentinel-2 reflectance cannot be relabeled as satellite-derived land-surface temperature.
Specifications and availability side by side
| Characteristic | TRISHNA | Landsat 8/9 | Sentinel-2 |
|---|---|---|---|
| Operational status for this comparison | In development; CNES currently lists launch in 2027. | Operational archive and products available through USGS. | Operational optical data; USGS points to Copernicus access. |
| Thermal capability | Planned four-channel long-wave thermal sensor. | Two TIRS thermal bands on Landsat 8; thermal observations are available for Landsat 8/9 products. | None; MSI is optical. |
| Optical capability | Planned seven-band visible/NIR/SWIR sensor. | OLI multispectral bands at 30 m and a 15 m panchromatic band for Landsat 8. | MSI multispectral optical observations with finer detail for many land applications; exact comparable values depend on band and product. |
| Thermal or product resolution | Planned 60 m products; ISRO states about 57 m at nadir over land/coasts and 1 km over oceanic and polar regions. | TIRS thermal bands: 100 m native resolution on Landsat 8; delivered grids can be resampled. | Not applicable: no thermal bands. |
| Optical resolution | Planned mission products; specifications vary by product. | 30 m multispectral, 15 m panchromatic for Landsat 8. | Band-dependent MSI resolutions; use the product metadata for the band and processing level. |
| Repeat information | Planned three-day revisit at the equator. | 16 days for Landsat 8, with Landsat 9 offset by eight days. | Not stated here; determine the effective acquisition pattern for your location and product. |
| Derived products | Planned L1C, L2A, L2B, and L3 products, including surface temperature, emissivity, vegetation variables, evapotranspiration, stress, and daily evapotranspiration time series. | USGS Collection 2 Level-2 surface reflectance and surface-temperature products. | Optical surface-reflectance products and other Copernicus products; no LST from MSI alone. |
| Access | GEODES and ISRO Bhoonidhi are planned distribution routes; verify availability after launch. | USGS Landsat data and Collection 2 routes; USGS states the archive is no-cost. | Copernicus access, as directed by USGS. |
Why Landsat 8/9 is the practical thermal choice now
Landsat is the defensible starting point when a project must produce a present-day temperature map, compare temperature with historical observations, or use a documented archive rather than wait for a planned mission. Collection 2 Level-2 includes surface reflectance and surface-temperature products, and USGS states that the data remain available without charge through its Landsat access routes. See the USGS Collection 2 Level-2 science-products page for product definitions and access details.
What Landsat can resolve—and what it cannot
- Its 30 m multispectral optical bands support land-cover, vegetation, and reflectance analysis.
- Its TIRS thermal bands have 100 m native resolution, so very small ponds, narrow rivers, field edges, and mixed shoreline pixels may not be represented as pure surfaces.
- A delivered thermal grid can be resampled; inspect metadata rather than assuming every output pixel is an independent 100 m detector measurement.
- The 16-day cycle for each satellite and the eight-day offset between Landsat 8 and 9 improve temporal opportunity, but cloud screening can reduce usable frequency substantially.
A reliable Landsat workflow
- Define the measurement before downloading: surface temperature, surface reflectance, or both.
- Select Collection 2 Level-2 scenes covering the area and date range. Filter by cloud and scene quality, but also inspect cloud shadows, haze, and water-adjacent contamination.
- Use the surface-temperature band and its scale factors exactly as documented in the product metadata. Do not substitute an optical band or an unscaled digital number for temperature.
- Check the thermal pixel grid against the target size. If the study area is smaller than a thermal pixel or contains mixed land and water, report that limitation.
- Pair the temperature product with surface reflectance only after confirming that acquisition dates and projections align.
- Keep the scene identifier, processing level, acquisition date, cloud mask, scale factors, and resampling method with the analysis so later dates remain comparable.
When Sentinel-2 is the better optical choice
Choose Sentinel-2 for questions such as “Where is vegetation changing?”, “Which pixels are built-up, bare soil, or water?”, or “What is the surface-reflectance pattern along this shoreline?” Its MSI bands provide optical detail suited to land-cover and vegetation work. That detail does not add a hidden temperature channel: a vegetation index or reflectance composite is still an optical observation.
A Sentinel-2 workflow
- Write the optical question and select the MSI bands or reflectance product that answer it.
- Access scenes through the Copernicus route identified by USGS.
- Choose an atmospheric-correction or surface-reflectance product appropriate to the analysis, and keep the processing baseline with your metadata.
- Apply the supplied cloud and scene-classification masks. Water, thin cloud, haze, and cloud shadow can all distort reflectance even when a scene’s headline cloud percentage looks acceptable.
- Resample bands only when necessary, and document the target grid. Different MSI bands can have different native spatial sampling.
- If temperature is also required, join Sentinel-2 optical observations to a thermal source such as Landsat by date and location; do not derive LST from Sentinel-2 alone.
What TRISHNA changes once it is available
TRISHNA is aimed at a gap between detailed optical mapping and relatively infrequent, coarser thermal observations. Its planned 60 m products and three-day equatorial revisit could make temperature, emissivity, evapotranspiration, and vegetation-water-stress monitoring more frequent and spatially detailed than a Landsat-only workflow. Those advantages are mission objectives and planned specifications, not a demonstrated accuracy ranking.
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CNES GEODES describes the intended product chain on its TRISHNA project page:
- L1C: top-of-atmosphere reflectances and radiances with a cloud mask.
- L2A: surface reflectance, land/surface temperature, emissivity, water vapour, and aerosol information.
- L2B: vegetation variables, albedo, evapotranspiration, and vegetation-stress indicators.
- L3: daily evapotranspiration time series.
The L3 evapotranspiration output would be a derived model product, not a direct temperature measurement. GEODES and ISRO’s Bhoonidhi portal are identified as intended distribution routes; verify the actual release, product names, licensing, and latency after launch.
How to plan without treating TRISHNA as available
- Design data schemas that can hold acquisition time, sensor geometry, thermal bands, optical bands, quality flags, and product level.
- Use Landsat to establish a current baseline and Sentinel-2 to build the optical context.
- Record validation sites and independent temperature or flux measurements now if a future cross-mission study is planned.
- Do not promise a three-day cloud-free time series or daily direct temperature observations. The revisit is an acquisition opportunity, while the daily evapotranspiration product is derived.
Using the missions together
These missions are complementary rather than interchangeable. A sensible current combination is Landsat thermal data plus Sentinel-2 optical data, provided the dates, projections, atmospheric corrections, and cloud masks are handled consistently. CNES GEODES describes a planned workflow in which Sentinel-2 visible/NIR images and TRISHNA observations calibrate simpler FAO-56 evapotranspiration models and support daily, high-resolution evapotranspiration maps. Until TRISHNA is operating, Landsat can supply the available thermal observations for a similar experimental design, but that is not the same as claiming TRISHNA-equivalent performance.
Match the measurement and scale
- Use the thermal grid to set the minimum meaningful temperature-mapping unit.
- Aggregate finer optical data when comparing it with coarser thermal pixels; otherwise a single temperature pixel may be compared with a mixture of many optical classes.
- For narrow rivers or small ponds, report the proportion of mixed pixels and consider whether the target is large enough for a defensible thermal statistic.
- For vegetation stress, align optical indices and thermal observations as closely in time as cloud conditions permit; a long date gap can represent a real change in canopy or moisture.
Check cross-sensor comparability
Different detectors, atmospheric corrections, viewing angles, calibration histories, and resampling choices can create apparent differences that are not environmental change. ESA describes pre-launch thermal cross-calibration work involving planned missions, including TRISHNA, because inter-sensor bias and comparability matter when measurements are combined. The ESA overview is a useful reminder not to claim that one mission is intrinsically more accurate without a matched validation study.
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A decision process for a real project
- Name the variable. Write “surface reflectance,” “land-surface temperature,” “water extent,” “vegetation stress,” or “evapotranspiration,” rather than the broad phrase “land analysis.”
- Separate direct from derived measurements. Temperature bands measure emitted thermal radiation; evapotranspiration and stress layers are calculated products that use measurements and models.
- Set the date requirement. For work that must run now, remove TRISHNA from the operational options. For a future mission plan, mark its 2027 schedule as provisional.
- Set the spatial requirement. Compare the target’s size with Landsat’s 30 m optical and 100 m native thermal resolutions, Sentinel-2’s band-specific optical sampling, or TRISHNA’s planned 60 m products.
- Set the temporal requirement. Count usable, cloud-free observations rather than quoting revisit alone. Landsat 8 and 9 provide offset opportunities; TRISHNA’s three-day figure is planned at the equator.
- Choose processing level. Prefer an analysis-ready surface product when it meets the question, and retain quality masks and metadata.
- Design the validation. If comparing sensors, collect coincident observations or use an independent reference. Do not infer accuracy from resolution or revisit.
- Document access and reproducibility. Save portal, collection, product level, acquisition date, projection, scale factors, cloud mask, and resampling method.
Common mistakes and how to avoid them
Calling Sentinel-2 a thermal source
This is the most consequential error. MSI is optical, so Sentinel-2 cannot produce satellite LST by itself. Use it for reflectance and pair it with a thermal mission when temperature is required.
Presenting TRISHNA as already launched
CNES currently lists the mission as in development with a 2027 launch schedule. Treat all TRISHNA resolutions, revisit values, product levels, and portal routes as planned until official operational data are released.
Equating a thermal product grid with native detector resolution
Landsat 8 TIRS is specified at 100 m native resolution, while archive products can be resampled. Read the metadata and state which grid and resampling method your result uses.
Assuming revisit equals usable frequency
Clouds, shadows, haze, snow, viewing geometry, and quality masks can remove otherwise scheduled acquisitions. Build a usable-observation count for the site and period.
Claiming one mission is more accurate from specifications alone
Resolution and revisit do not establish accuracy. A defensible comparison needs matched dates, comparable atmospheric processing, independent reference observations, and an explicit error metric.
Mixing dates without recording environmental change
An optical image and a thermal image acquired days apart may describe different moisture, crop, or water conditions. Keep acquisition times visible in every joined dataset and explain any temporal mismatch.
Example choices by scenario
I need a current heat map of an agricultural district
Start with Landsat 8/9 Collection 2 Level-2 surface-temperature data. Use the optical bands for vegetation context, mask clouds and shadows, and evaluate whether the 100 m native thermal sampling is adequate for field size.
I need to classify shoreline vegetation and open water
Use Sentinel-2 optical reflectance products and its MSI bands. If the project also needs water temperature, add a thermal observation from Landsat rather than treating a Sentinel-2 index as temperature.
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Prototype the workflow with current Landsat thermal observations and Sentinel-2 optical inputs. Track the planned TRISHNA product chain and verify GEODES or Bhoonidhi releases after launch before changing the operational source.
I need a multi-decade land-change series
Prioritize Landsat continuity and consistent Collection 2 processing. Sentinel-2 can add newer, finer optical observations, but keep sensor changes and processing baselines explicit in the time series.
Where to get the data
- Landsat: use USGS Landsat access and Collection 2 Level-2 routes. Surface reflectance and surface-temperature products are available at no cost according to USGS.
- Sentinel-2: use the Copernicus access route identified by USGS.
- TRISHNA: monitor CNES GEODES and ISRO’s Bhoonidhi as planned distribution channels, then confirm actual product availability and access terms after launch.
Portal availability, product naming, processing versions, and schedules can change. Record the access date and the exact collection or product identifier whenever a result is published.
Bottom line for land and water analysis
For a project that must run now, use Landsat 8/9 for temperature and Sentinel-2 for detailed optical information, combining them when the analysis needs both. TRISHNA is an important planned addition—not a current replacement—with a CNES-listed 2027 launch schedule, planned 60 m products, and a three-day equatorial revisit. Make the measurement type, not the mission label, the first decision.
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